AE TESTING OF CONCRETE JOINT AT HAZAMAJOURNAL OF ACOUSTIC EMISSION
Editor: Kanji Ono
Associate Editors: A. G. Beattie, T. F. Drouillard, R. Hill and S. L. McBride
1.

Alms and Scope or the Journal

Journal of Acouslic EmissioD is aD iDtematiOllll1 journal designed to
be of broad interelland use to both researcher and practitioner of acoustic
emission. It will publish original CODln"butionS of all upects of Rsean:Iland sipificaD1 eDgineeriDg advances in the sciences lUId applications or
ac:owtic emissiOD. The journal will also publisb Rviows, Ibe abstracts ofpapers preseDted at meetiDgs. technical Dotes, communicatioDS lUId
summaries of reports. CumDt DOWS of interest to the acoustic emission
communities, aDDouncements of future con-feRnces lUId working groupmeetings aDd new products will also be included.
Journal of Acoustic Emission includes the following classes ofsubject mattersj
A. RelOllrcb Articles: Manuscripts should represent completed
origiDal wort embodyiDg the Rsuill of exteDsive investigation. These
will be judged for scientific and teehnic:al meriL
B. Engineering ApplicatiODS: Articles must present significant
advances iD Ibe engineering applicatioDS of acoustic emission. Material
will be subject to reviewa for adequate description of procedures,substantial databue and objective illtefpretation.
C. Technical Notes: This clus provides publications of worts ofcumnt interest, aDd new or improVed experimental techniques andprocedUlU.
D. Communications: Short items of cumnt interest, dis- cussionsof published articles aDd relevant applications.
E. AE UtentUR: This SectiOD will collect the titles and abllllaetsof papers publlsbed elsewbere and those preseDted at meetiDgs andconferences. Reports of conferences and symposia win also be presented,
together with meeting schedules.
P. Reviews, Tutorial Articles lUId Special Contributions: This
class or articles will cover Ibe subjects of general interest.
Nontechnical Section: This part will cover book reviews,
significant persODal lUId technical accomplishments, cumDt news andnew prodUetl.
2.

Endorsemenl

Acoustic Emission Womng Group (AEWO), European Working
Group on Acoustic Emission (EWGAE), Committee on Acoustic Emission
from Reinforced Composites (CARP), and Acoustic Emission Wort.ing
Group of India bave eDdorsed the publicatioD of Joumal of Acoustic
EmissioD. This Journal gratefully aclalowledges financial contributioDsfrom EWGAE aDd AEWG.
3.

Governlbl Body

The Editor aDd Associate Editors will implemeDt the editorial
policies described above. The Editorial Board wiD advise the edilors on
aDy major change. The Editor, Professor Kanji ODO, has the geneml
responsibility for all lbe matters. Associate Editors, Dr. S.L. McBrideand Dr. A.O. Beattie, assist the Rview processes as lead reviewers aDd
Associate Editor, Dr. R. Hill coordinates lbe regional review processes
for Europe. Mr. T.P. Drouillard is responsible for the AE Lileraturesection.
The members of the Editorial Board win advise and assist the editorson the publication policies and other upects. They will promptly Rview
the manuscript assigned aDd will coDtribute actively to the Joumal. The
Board presendy includes Ibe following members:

Jeba Baron
F. Bea1J
LBolinP.R. BRCkeDriqe
S.H. Carpenter
H.Croslact
D.M.Egle
P. Fleischmann
TJ.Fowler
LGolasti
M.A. HaIllQad
H.R. Hardy,Jr.
T. Holroyd
l.V. Ivanov
P.Jax

T. Kisbi(Swlll1eD)
(USA)
(USA)
(FRO)

(USA)
(France)(USA)(poland)
(USA)
(USA)
(UK)
(USSR)(FRO)(Japan)A.E. Lend, Jr.
K.MogiW.Morper
C.R.L Murthy
A. Nielseo

M. OblSU
A.A. Pollock
J. RogelI. Roman
C.Sauby
C. Thau10w
E. Waschkies
K. Yamaguchi
4.

(CaDada)

(USA)

(USA)

(Japan)(DDR)(India)(DelUllllrt)(JapaD)(USA)(France)(Israel)(UK)(N01W8Y)(FRG)(Japan)PubllceUoa

Journal of Acoustic Emission is publisbed quarterly in March, June,
September aDd December by Acoustic Emission Group, 308 Westwood
Blvd. - Box 364, Los Angeles, CA 90024-1647. It may also be reached at
6532 Boelter Hall, UDiversity of California, Los Angeles. California
90024 (USA) telephoDe 213-815-5233.
5.

SUbscrlpUoa

SUbscription should be sent to Acoustic Emission Group. Annual
rate for 1990 and 1991 Is US $90.00. For surface delivery, add S6 in lIle
U.s. and S8 for canada and elsewheR. For air mail delivery to US aDd
Canada, add S15, South America and Western Europe, add S23 and add $28

elsewhere. Add S5.OO for orders DOl prepaid and requiring invoices.
Overseas orders nmst be paid in US currencies with a cbeck drawn 00 a US
bank. Inquire for individual and bookseller discouDts.
6.

AdverUsemenl

Advertisement only for products and services Rlated 10 acoustic
emission will be accepted. Send a camera ready copy ODe mODth befoRscheduled publication date. IDquiR for rates. ADoouncementa for boob,
uaiDing courses and fUlUR meetings will be included without charge.

ISSN 0730-0050 Copyright e 1990 Acoustic Emission Group

Coden: JACEOO

Printed in U.S.A.

The International Joint Meeting, 1st Workshop on Acoustic
EmIssion In Civil Engineering and 2nd Workshop on AE
and Rock Fracture Mechanics,
Kumamoto, Japan, October 29 - 31, 1990

Current Research and Future Trend of AE Applications to Civil
Engineering and Geological Technology
Masayasu Ohtsu
Topical Editor
Introduction
The International Joint Meeting, lst Workshop on Acoustic Emission in Civil Engineering and 2nd
Workshop on AE and Rock Fracture Mechanics, was held at the city of Kumamoto, OCtober 29 - 31, 1990.
This Meeting was sponsored by Acoustic Emission Working Group of the Kyushu Association for Bridges
and Structural Engineering. In all, 29 papers related to the application of AE to civil engineering andgeological technology were presented. This was the rust international activity on AE applications to thesefields. Fifteen of the papers presented are included in this special issue ofJournal of Acoustic Emission.
The confer~nce was organized into sessions on source characterization, civil engineering materials,
structural integrity, mechanical behavior and Kaiser effect of rock, and concrete engineering. In the session
on civil engineering materials, the majority of the papers were concerned with geological materials and concrete. In the area of structural integrity, the stability of rock cavern, tunnel, and landslide were reported. The
application of Kaiser effect to the evaluation of geostress was also presented.
As indicated by these reports, AE applications in these areas are growing rapidly at present Actually,
the research activities in geo-technology rust boomed some twenty years ago, but no sustained efforts had
been conducted at the major companies and instibltes. Renewed interests in recent years have arisen from theneed of inspection technologies in construction fields related with three R's for infrastructures, namely, repair, restoration and rehabilitation.
Code and Standardization
In the fields related to geological materials and concrete, AE techniques are inherently applicable tomonitor the construction process and to maintain existing structures. With respect to the geological materials and strucmres, AE application to the process monitoring is well known. Because the construction processes are highly dependent on site conditions, however, the inspection procedure is not unique to the site.
The in-process monitoring of industrial products is not readily transferrable to geological materials. Still.
the need for the monitoring of construction processes is increasing. The safety monitoring of existing geologic structures is also in demand. A number of concrete structures are approaching their service-life limit
due to deterioration. This is why techniques for three R's are needed, AE applications for the evaluation of
concrete deterioration in particular.
Practical geological and concrete applications require the standardiz.ation, because the construction
works are normally under governmental supervision. Consequently. to advance AE technology further, theestablishment of the codes is essential. The Japanese Society for Nondestructive Inspection (JSNDI), theJapan Concrete Institute (lCI), and the Japan Society of Civil Engineers (JSCE) are all working on this
aspect currently.
Concluding Remarks
Geologic materials and concrete cover a variety of engineering fields, such as mineral science, mining,
pelJ'oleum engineering, resource engineering. and civil engineering. Structures related with these technologies are of large scale and AE applications are useful in various phases. However, a new technology devel
Professor Masayasu Ohtsu is affiliated with Department of Civil and Environmental Engineering, KumamolO
University, KumamolO 860, Japan.

oped may be either unknown or inapplicable in other fields. This is the biggest obstacle to AE applicationsin these fields. Researchers publish results in only their own field and people in other fields often unawareof other's success. Consequently, mutual communications are critically important and international conferences such as this one should be held often around the world
I hope that reports from this International Joint Meeting stimulate further developments and result inwider uses of acoustic emission technology.

CONTENTS
Variety of Acoustic Emission Waveforms Produced by Discrete Crack Growth in Rock
Steven D. Glaser and Priscilla P. Nelson

51-512Estimation of Maximum Stress in Old Railway Riveted I-Girder Bridges using Acoustic Emission Signals
Hisanori Otsuka, Hiroshi Hikosaka, Hiroyuki Miyatake and Syouzou Nakamura
513-517
Another Look at Booming Sand
Marcel F. Leach and Gottfried A. Rubin

S18-S21

Expected Acoustic Emission from Around a Shaft in Intact Rock
BJ.S. Wilkins and GL. Rigby

522-528

Acoustic Emission Monitoring during Microseismic Activity Caused by Mine Subsidence
Brian R A. Wood and Robert W. Harris529-534
Acoustic EmissionIMicroseismic Activity Monitoring of Salt Crystallization for Stone Conservation
M. Montoto, R.M. Esbert, L.M. SuArez del RIO, V.G. Ruiz de Argandofta and C.M. Grossi
S3S-S41
Acoustic Emission Monitoring during In-situ Heater Test of Granite
T. Ishida, K. Kitano, N. Kinoshita and N. Wakabayashi

S42-548

Using Acoustic Emission Testing in Seepage Investigations
Andrew R. Blystta

S49-SS4

Acoustic Emission ofPenettation Experiments to Judge Soil Condition
5. Naemura, M. Tanaka, S. Nishikawa, M. Nakamura, K. Jo and T. Kishishita

5SS-SS8
A Laboratory Investigation of AE from Coal
R. W. Harris, B. R. A. Wood and T. Flynn

5S9-562

Determination of the Initial Suesses on Rock Mass using Acoustic Emission Method
K. Michihiro, K. Hata, H. Yoshioka and T. Fujiwara

S63-S76

U.5. Bureau of Mines Research on the Kaiser Effect for Determining Stress in Rock
Michael J. Friedel and Richard E. Thill

S77-S89

Evaluation of Joint Properties of Anti-washout Underwater Concrete by Acoustic Emission Measurement
Kazuya Miyano, Tatsuo Kita, Yuji Murakami and Takako Inaba
S90-596
Automated Detennination of First P-Wave Arrival and Acoustic Emission Source Location
E. Landis, C. Ouyang and 5. P. Shah
597-5103
Application of Acoustic Emission Techniques in the Evaluation of Frost Damage in Mortar
Hisatoshi Shimada and Koji Sakai
5104-5109

II

Variety of Acoustic Emission Waveforms Produced byDiscrete Crack Growth in Rock
Steven D. Glaser and Priscilla P. Nelson
The authors are affiliated with Department of Civil Engineering. The University of Texas. Austin. Texas79712. S.D. Glaser is now with National Institute of Standards and Technology. Gaithersburg, MD 20899.
Abstract
An experimental program was undertaken to investigate high-fidelity acoustic emission (AE) wavefonns produced during discrete fracture propagation in rock. Large chevron-notched specimens were loaded incontrolled conditions, and characteristic acoustic emissions associated with Mode I and Mode II fracture wereisolated. Tested rock included medium-grained granite and micritic dolostone. The entire test system. including surface-mounted NIST type piezoelectric transducers, was calibrated for each experiment to establish thesystem response function.
The AE waveforms belonged to five characteristic classes. By far the most common three classes are
related to basic step impulses. Prominent subclasses of several classes are identified and discussed in detail.
The variety of AE event classes was the same in Mode I and Mode II loadings for both rock types. The
mechanism for crack propagation in rock is thought to be due to localized tensile failure for both tensile andshear loadings.

1. Introduction
For the last few years. a program has been underway at the University of Texas at Austin to carefullyrecord and decode calibrated AE waveforms from discrete fracture propagation in rock. The goal was to sortthese waveforms into characteristic shape groups and correlate these groups with a known and controlledmode of fracture propagation. The recorded signals clearly divided into five main classes (Glaser. 1990;
Glaser and Nelson. 1992a). However. within these classes there were tight subgroupings of signals that
obviously were due to very similar source mechanisms. These subtle differences within waveform classes
hold additional information about the fracture process. much of which is not readily understood. This paper
presents the range of waveform shapes recorded during our experimental program. The explanations of thepossible causal mechanisms given in this paper are deduced from the experimental results.

2. Summary of the Experiment
2.1 Experimental Plan
This experimental program was undertaken to decode the displacement record of stress waves produced
during discrete fracture propagation in rock. The testing conditions were carefully controlled so that characteristic waveform shapes could be correlated with the mode of crack propagation. The specimens used forthis series of tests were loaded such that the stress regime causing crack growth was known. at least on amacroscopic scale. The specimens were loaded first in four-point bending to insure that crack growth would
be in Mode I. then in four-point shear (Ingraffea. 1981; Jeng and Shah. 1989) to promote crack growth inMode II. The specimens had a chevron notch cut perpendicular to the long axis so that the plane of fracture
would be known, and to improve control over the rate of crack growth. The crack front traveled through 257
mm of the material. with a maximum crack front length of295 mm after the crack front propagated past thechevron notch. Two different rocks. San Saba dolostone and Elberton granite were used as specimens so thatit could be determined whether the associated fracture mechanisms would be a function of rock type.

81

The samples used for this experimental program were quite large (0.3 m x 0.3 m x 1 m) so that a suitable length ofsignal was recorded before reflections from specimen boundaries and sample resonance would
contaminate the signal. Wavefonns were accepted as AE events only if the initial arrival of energy was
unambiguously present in the record. Only the initial part of each event wavefonn was used for analysis;
from the initial arrival of the P-wave to the arrival of the S-wave. Thus, the signals analyzed were effectively from an infmite half-space.
The effect of each specimen on the waveforms traveling from the crack to the transducer was accounted
for by calibrating the test system at the beginning of each loading. The impulse response function for theentire test setup-the load frame. specimen, transducer, electronics, and digitizer-was derived and an inverse
fllter calculated so that the system bias could be removed from the recorded signals. In fact, the effect of thesystem was very slight. Almost all signal energy was below 350 kHz.
NIST-type high·fidelity piezoelectric transducers were built to insure that the output of the transducer
was proportional to vertical surface displacement only and had a virtually flat frequency response to wellover 1 MHz, and a small amount of phase shift to the signal. Similar care was taken with the unity gain
matching preamplifier and 20 dB amplifier.
In addition to the AE waveforms collected, the load, load point displacement, crack mouth opening or
sliding displacement and trigger time were continuously logged throughout the test for each event. This
allowed correlation between individual AE event waveforms and specimen condition at that time.

2.2 Method ofPattern Recognition
It was initially planned to use a commercial pattern recognition program, lCEPAI{ from Tektrend
International, to choose and sort the waveforms into characteristic classes (Glaser and Nelson, 1989).
However, after much work it became apparent that ICEPAK was not designed for, and was unsuited for, thistask (Glaser, 1990; Glaser and Nelson, 1992b). Instead, a straightforward heuristic method was chosen bydirectly observing waveforms recorded.
All the waves for the dolostone test were printed out on separate sheets of paper. The waveforms werethen sorted into as many different stacks as appeared to be needed to defme characterizable groups. Mter several days the process was repeated An archetypical shape for each group was fonned for each stack, or class.
Mter sustained exposure to the data, it became evident that the events could be divided into eight mainclasses, with many subgroupings. The result of this methodology was verified when the signals from thegranite tests were sorted. The waveforms fell into the same groupings found for the dolostone, with no forcing of decisions.

3. Characteristic Waveform Shapes
3.1 Waveform Notation
Careful systematic sorting of the AE waveforms detected during the dolostone and granite testing
resulted in five classes (and three subclasses) of events: Classes A (and AI), B (and Bl), C (and Cl), 0, andE. Archetypal wavefonns representing characteristics of each class are presented and described in the following sections. Archetypal waveforms from prominent subgroupings within the classes are also presented.
For clarity, a shorthand notation for the material test and cycle being discussed was developed. The first
letter in the notation gives the material being tested - d for dolostone and g for granite. The second letter. C,
stands for cycle. This is immediately followed by the number of the cycle (a total of three cycles for dolostone and four for granite testing). Mter the cycle number is given, the letter M is given which stands forMode. This is followed by 1 for Mode I and 2 for Mode II. If an individual wavefonn is being identified, anumber will follow which will give the relative position of that wavefonn in relation to all the signals analyzed for that loading cycle. This counter is reset for each loading cycle. For the dolostone test, there were
two Mode I and one Mode II loading cycles. The granite test was comprised ofone Mode I and three Mode IT
loading cycles. This nomenclature system is summarized by: iCjMk:m, where i = {d,g} dolostone or gran-82

ite, j .. (1,2,3) loading cycle, k co {1,2} loading Mode, m co the moth event in the loading cycle. As an example, the 143rd event of the second dolostone loading cycle, which was loaded in Mode I geometry would
be labeled dC2Ml:143. This system will be used to refer to loading cycles as well as to label waveforms infigures to provide an indication of where in the test the event occurred.
3.2 Class A Signals
The Class A event is the most common waveform shape. This class was by far the most prevalent typein the Mode I tests as well as appearing frequently in the Mode n tests. The dolostone Class A archetype isshown in Fig. 1, and is characterized by a sharp initial displacement rise on the order of2 to 8 J!S, followed
by a fairly constant displacement in the same direction as the initial motion. This type of event can be characterized as approaching a "boxcar wave" for the initial P-wave displacement The S-wave energy arrives
very suddenly in a sense of motion opposite to that of the initial displacement There often is an increase indisplacement, of varying relative amplitude, several J.lS before the actual displacement reversal caused byarrival of the shear componenL
Figure 2 shows a waveform that results from breaking a glass capillary tube at the central area of thedolostone failure surface. Since the location of this source event was known, the time of arrival of the firstreflection (side), refraction (SP-wave), and S-wave can be calculated. These arrivals are labeled in Fig. 2 as
SIDE, SP, and S respectively. It is interesting to note the close similarity between the Class A event andthe signal shown in Fig. 2. This similarity in signal shape leads to the conclusion that the Class A waveforms are caused by a step-force kinematic.

t

30

·300

20

·200

10

·100

l

0

0

·10

100

·20

200

·30

300
0

10

20

30

40

50

0

10

20

30

40

SO

8C

Fig. 1 The dolostone Class A archetype.

Fig. 2 Typical waveform resulting from step force
input on failure plane.

An exemplary granite Class A signal is shown in Fig. 3. Many of the granite Class A events, which
often had a low signal amplitude, exhibited a large oscillatory displacement after the initial rise. These
superimposed oscillations have a "typical" frequency of about 330 kHz (ranging from 220 to 480 kHz).
Such higher frequency information "riding" on the constant table-like displacement was also noticed for the
dolostone. However, for the granite, the amplilUde of this oscillation can be on the scale of the P-wave displacemenL Therefore, the heuristic technique developed for pattern recognition of dolostone signals was
slightly extended to son the granite waveforms.
Given the lithology of the granite, the modulation present among all classes of signals from the granite
test is not surprising. The initial portions (0 to 20 J.lS) of most of the granite signals have a maximum
amplitude of less than 5 mY, compared to more than 10 mY for the dolostone signals. Because of the low
signal amplitude, the influence of background noise on the granite waveforms is more significant In addition, the individual crack growth increments for the granite are very small and on the order of grain size (0.1
to 2.5 mm), so that the displacements are much smaller temporally and spatially than for the dolostone.
This leads to a less "drawn out," more oscillatory waveform. Another contribution to the modulation comes83

from crack surface damage which developed in the granite. The granite crack surface was covered with smallloose "flakes" which can serve as small vibrators superimposing the higher frequency oscillation on thegeneral archetypal ~shape.
The increments of macrocrack growth did not occur as single, isolated, and large discrete events in the
granite. Neighboring grains could fracture almost simultaneously, the signals of the many smail microevents interacting both constructively and destructively, leading to an apparently oscillatory waveform.
Signals from multiple crack growth increments, with a second distinct waveform arriving microseconds
after the arrival of the trigger event, were represented among the granite waveforms but were absent from thedolostone signal ensemble.
Among the dolostone Class A waveforms, there is also variability in the relative amplitude of the shondisplacement rise, or "precursor", before the sudden arrival of the S-wave. This "precursor" was also present
in the waveforms generated by capillary breaks, and is in some part due to the arrival of the criticallyrefracted S-wave, or SP-wave. The "precursor" was not very apparent in the granite Class A signals. The
relative importance of this "precursor" for the dolostone and the granite events can be seen in Fig. I and
Fig. 3.
Class A waveforms with a strong "precursor" are placed in a subgroup of Class A, Subclass At. The
dolostone archetype for Subclass Al is illustrated in Fig. 4. This subclass is vinually the same as Class A,
except the precursor before the S-wave arrival is so dominating as to be an obvious sorting criterion.

l

10

40

5

20

0 -P:::"'-~~---I--.........-.........,H-

D

e

·5

dC2M1:36

S

0

·20

.......-...,·10 +---r----r--...--~
o
20
40
10
30

-40

50

0

10

30

20

40

50

soc

sec

Fig. 3 The granite Class A archetype.

Fig. 4 The dolostone Subclass Al archetype.

3.3 Class B Signals
The dolostone Class B archetype is shown in Fig. 5, and is characterized by a gradual ramp-like initialarrival of P-wave energy followed by a sudden arrival of S-wave. In this class, too, there may be higher frequency oscillation riding on the main displacement, and a varying amplitude "precursor" displacement jumpshortly before the S-wave arrival. It has been shown experimentally (Glaser, 1990; Nelson and Glaser,1992) and implied theoretically (Ohtsu and Ono, 1986; Hayashi and Nishimura, 1986) that the ramp-like
Class B event is caused by the same step-like source mechanism as the Class A waveform. For the Class B
signal, this center of motion is oriented so that some component of the step force is directed away from thesurface being monitored by the transducer.
An exemplary granite Class B waveform is shown in Fig. 6. Class B events were fairly common
among the granite signals, although at a much smaller occurrence rate than for the dolostone. For both thegranite and the dolostone, the Class B signals were associated with Mode II propagation for which the failure surface was more uneven than for Mode I crack growth. The granite fracture surface was certainlysmooth compared to the numerous large and inclined fracture planes on the dolostone failure surface. The
inclined fracture increment surfaces occurred much less frequently for the granite than for the dolostone, so
the far fewer number of Class B signals is not unexpected. Among the Class B events there is a group of
signals which shows a small initial displacement dip for 5 to 10 J.lS preceding the main positive displace

84

15

dC3M2:28110

dC3M2:83

10
5
~
E

5

1 O-+O--=:..----------'t----

0

·5

·5

·10
·10

.154---......- - - , . - - . . . , - - - 1 -..........,
0

10

20

30

40

o

50

10

20

30

40

50

880

Fig. 5 The dolostone Class B archetype.

Fig. 8 A dolostone Class B waveform transitional
to Class A.

9C4M2:121

10

gC4M2:288

40

5
~

20

o+.o=::::-+.....:o........~_

I

04-~------+----r

·5

-20

·10

-40

o

10

20

J1SllC

30

40

so

o

10

30

20

40

50

8C

Fig. 6 The granite Class B archetype.

Fig. 9 The granite Subclass B1 archetype.

dC3M2:179

~

20

20

10

10

~

0

0

·10

·10

·20

-20
0

10

30

20

40

50

0

eo

10

20

30

40

50

8C

Fig. 7 Example of dolostone Class B subclasssignal variety.

Fig. 10 The dolostone Class C archetype.

rnent ramp. An example of this group is shown in Fig. 7. The cause of this substantial sharp displacement
couple is not known at this time. An example of the organic nature of the actual waveform shapes is given
in Fig. 8. where the wave shown has a sharp initial small-displacement rise. This type of event is classified
as Class B since the step. which is the Class A criteria. is of small magnibJde in comparison to the subsequent ramp increase in displacemenL
A distinct subclass of the Class B is the granite Subclass Bl archetype shown in Fig. 9. A typical
Subclass B1 waveform can be characterized as a Class B except there is no discernible distinct arrival pointof S-wave. SP-wave. or reflected wave energy. The Subclass B1 events occur almost exclusively among thelater Mode nevents for both rock types. and occur quite rarely in the Mode I loadings.

85

3.4 Class C Signals
The dolostone Class C archetype is given in Fig. 10. This waveform is characterized by a sudden sharp
arrival of P-wave energy followed by a relatively constant displacement reminiscent of a square wave pulseor the Class A archetype. The arrival of the shear energy is sudden and sharply defined as for the Class A
signal. but the S-wave displacement appears to be in the same direction as the P-wave energy arrival. The
downwards motion of the Class C waveform arrives long after the expected S-wave arrival. For the period
corresponding exclusively to the P-wave displacement, there is no difference between the Class C signal andthe Class A archetype.
The difference between the shape of the Class C and Subclass Al signals is that the "precursor" of the
Subclass Al event blends into. and actually contains. the S-wave arrival. If the signals were bUncated at thetime of first reflection. about 12 to 15 J.lS after the initial arrival. the Class A, AI. and C signals would beindistinguishable.
Like the Class B signal. the Class C signal is thought to be caused by the same step-like source kinematic as the Class A signal. For the Class C signal. the step-like source motion occurs at an orientationsuch that some major portion of the energy is directed towards the surface being monitored by the transducer. While the experimental connection is not as solid as for the Class B signal. the Pekeris solution(pekeris and Lirson. 1957) leads to this conclusion. Extending this logic. because the granite failure surface
was much smoother than the dolostone failure plane, there were few Class C events among the granite signals compared to the dolostone ensemble.
Among the dolostone signals a Subclass Cl archetype shown in Fig. 11 was identified. Subclass Cl
starts with a smooth ramp or slight steep rise. gradually reaching a maximum before the shear energy arrival. which is not as distinct and sharp as for the Class C events. Our understanding of the Cl signal
source is limited. There is an interesting and possibly important group within Subclass Cl. in which there
is a large change in the direction of the surface displacement (Fig. 12). This group of subclass Cl eventsoccurs predominantly quite late in the Mode U loading of both rocks. and the shapes of these events are
remarkably constant. This group could possibly be related to the Class B signal. with a large initialdisplacement.
100

dC2M1:187

60
40

50

20

~

~

O-i=:::;::;;....--------

E

0

·20
·50

·40
·60

·100 +-----.r---"""T""--r----,-----,
o
20
40
50
10
30

0

8C

10

30

20

40

50

90C

Fig. 11 The dolostone Subclass Cl archetype.

Fig. 12 Example of dolostone Subclass Cl groupcommon towards the end of the test.

3.5 Class D Signals
The Class D archetype is shown in Fig. 13. Thismay be characterized as a boxcar that is initially displaced. The initial negative displacement is followedby positive displacement recovery and a subsequentnearly constant displacement. This shape issomewhat reminiscent of the waveforms reported forthe pencil lead break calibration at different radial
distances from the transducer (Michaels et al.•1981). The Class D archetype often shows a sudden.
sharp. positive displacement immediately before theS-wave arrival and always exhibits a sudden shear
energy arrival in a direction opposite to the main Pwave energy. The Class

86

15
10

20

5

I

dC1Ml:8830

gC3M2:108

10

I

0

O-+-or---I--.,;,~~~--+t-­
·10

·5

·20

·10

·30
·15
20

10

0

30

o

50

40

10

20

40

30

50

8C

Fig. 14 The dolostone Class D archetype.

Fig. 13 The granite Class D archetype.

D dolostone archetype is shown in Fig. 14. Class D events contain complex displacement patterns indicating a much more complicated source kinematic than is adequate for other events. The dolostone and granite
Class D events generally have a relatively low amplitude, averaging 10mV. For the dolostone, considering
the initial 25 J.lS of the signal (before S-wave arrival), only 30 % of the Mode II Class D waveforms had an
amplitude above 10 mY.
3.6 Class E Signals
A characteristic dolostone and granite Class E waveforms are shown in Fig. lSa and lSb. However, aclear representative archetype was unavailable for the Class E events. These events are with rounded initialarrival and no discernible shear energy arrival within the usual fust SO J.lS. Discernible wave mode arrival isabsent in this class. In fact, most of the Class E events, especially for the granite, were later discarded when
their source was shown to have been located outside of the possible fracture zone. However, with only twotransducers, not all Class E events could be so discarded. The fact that the difference in initial arrival times
for the two transducer channels is within the allowable location window for a sizable percentage of theClass E events might mean that some of these signals are real AE events which should be understood in thecontext of the applicable loading. Another possibility is that the "valid" Class E signals might be caused bysome form ofcrack face interaction.
81 dC3MZ:326100

bl gC2M2:61

10
50

~

5

~

0

0
·5

-50

·10
·100
0

10

20

30

40

50

60

0

10

20

30

40

50

60

II9C

Fig. 15 The dolostone (a), and granite (b) Class E archetypes.
3.7 Waveforms with "Pre-arrival" EIJI!Tgy
For all but a handful of events, the P-wavearrival rose directly out of the background noise. A troublesome
ambiguity concerning wave arrival arose. This problem is identified in reference to Fig. 16, with theperceived arrival of the P-wave is labeled 'P'. For a few events, there is a long, gradual precursor to theconunonly seen P-wave arrival. This precursor occurs over the order of 10 to 20 JJ.S and, given the arrival
time for the shear wave and other reflections, this precursor cannot be part of the conventional P-wave

87

60

~

40
20
0
0

10

20

30
sec

40

50

60

Fig. 16 A signal showing a long, gradual pre-arrival to the actual wave arrival.
arrival. The near-field energy can never arrive before the P-wave (Aki and Richards, 1980, p. 45) and therefore cannot be the cause of this precursor. A possible explanation is that the actual causal event is not asingle, sharp, step function but might be characterized by some crack surface rubbing or localized displacement before a sudden release of energy due to crack growth.
3.8 Summary
This research indicates that there are five characteristic signal shapes (and three subclass shapes) that arepresent in the data. To clarify the important differences among the five classes, Table 1 highlights the major
features of each class. These group selections are not necessarily a unique classification, but extreme attention was given to avoid bias and inu-oduction of preconceived notions. The fact that the same archetypicalclasses are applicable for such different rocks as granite and dolostone reinforces the conclusion that thesegroupings reflect a commonality in causal mechanisms. The existence of the same classes of waveforms
from both tensile and shear loadings of the specimen indicates that the same mechanisms are at work inboth materials during both Mode I and Mode II loadings.

Table 1 The major classification features associated with the arrival of the p. and S-wave.
Class
A

B

C
D
E

P-WaveAbruptness
Direction
jump
up
jump
up
jump
up
jump
down
rollingup

S-WaveDirection wi&h respect to the P-wave
opposite
oppositesame
oppositesame

4. Waveform Shapes Unique to the Granite Test
4.1 Oscillatory Granite Signals
The granite AE events show a great deal of oscillatory behavior. In fact, a group of events from theMode II loadings can be seen as undifferentiated oscillating wavefonns, comprising about 10 % of all ModeII events. Two such events are shown in Fig. 17. Comparison with other events, and experimental work
using sources on a variety of facet orientations (Glaser, 1990; Nelson and Glaser, 1992; Glaser and Nelson,
1990), lead to the conclusion that these wavefonns could not be caused by a step-like displacement. In thetensile Mode I load cycle of the granite test, only five events (1.7 %) were oscillatory. In this loadinggeometty, &he two crack faces are being puUed apart, causing a direct cleavage of the rock wi&h few if any
chances for bifurcation or surface damage creating flakes. Thus, few flakes can serve as resonatOrs and thesense of movement makes it hard to excite the loose grains and flakes that might exist

58

The oscillation cannot be caused by the detection system since this behavior was not present during anyof the dolostone test runs using the same equipment Neither are these oscillations universally present in all
the granite events. What is more likely is that these oscillations are caused by either discrete mineral grainsor the fractured "flakes" which could act as small oscillators. In an attempt to isolate the source of the oscil
lations, capillary breaks were made at several locations on a saw-cut vertical surface of a granite specimen.
If the oscillations are due to crystal vibration or wave scattering, then this behavior should manifest itself
when capillaries are broken on the saw-cut face. No oscillatory behavior manifested. This indicates that discrete mineral grain vibrations or grain-induced wave scattering are not the cause of this behavior.
In the Mode n geometry, the two crack faces are displaced past each other. This sliding action can easily excite loose particles. The heterogeneous formation of localized fracture throughout the process zone, as
evidenced by the numerous bifurcations and multi-sized detritus, creates many good oscillators of appropriate size (length =nI2 • wavelength, where n := integer; Bueche, 1980).
In an attempt to isolate the flake resonating mechanism, the actual failure surface of the granite specimen was used. Both capillary breaks and loose material crushing were used as sources. This source model isappropriate because, immediately behind the AE event/crack tip, the crack surfaces are disengaged, acting
like free surfaces. Figure 18a shows a typical waveform resulting from breaking a capillary on the saw-cut
surface. In comparison, Fig. lSb shows an example waveform from the breaking of a capillary at a loose,
shattered area on the actual failure surface. Rather than the typical sharp rise and following steady table-like
displacement that was obtained from the saw-cut surface, the wave in Fig. ISb strongly resembles the oscillatory waveforms under discussion. Very similar waveforms were produced when loose grains which makeup part of the process zone on the failure surface are crushed or "popped" off the surface. Clearly, the oscillations are associated with the fracture surface and process zone "flakes" that were so prevalent on the granite
failure surface.
10a) gC3M2:520

bl gC4M2:370

20
5

10

~

~

0
·10

0

·5

·20
·10
0

10

20

30

40

SO

0

60

sec

10

20

30

40

SO

60

Be

Fig. 17 Examples of undifferentiated oscillatory granite signals.

4.2 Very Low Amplitude Granite Signals
A total of 8 % of the granite signals were of very low amplitude and could not be classified. Figure 19
shows such a Mode IT signal, with defmed characteristics features that could be due to either the P-wave or
S-wave arrival or specimen resonances. In all cases, the pretrigger length used by the digitizer insured that
the P-wave aaival would be digitized if the digitizer was triggered by the S-wave arrival, ruling out the possibility that the start of the signal was missed. It should be noted that Mode II granite signals had a relatively low amplitude and often had a P-wave arrival amplitude larger than the early S-wave displacements.
This is most likely due to an orientation effect, where the amplitude of the S-wave propagation pattern isapproaching zero, while the P-wave amplitude never disappears. Another possibility is subtractive interference from a "doppleganger" or multiple event "cascades".

89

a)

400

200

200

I

100
~
E

0

·200

0

·100

-400

·200
0

10

20

40

30

50

80

0

10

20

40

30

50

60

ec

Fig. 18 Example of the results of breaking a capillary on a saw-cut surface (a)and on a loose flake of granite on the failed surface (b).
10

gC2M2:241

5

·5

·10

+---r--.--.,r---r--'T"""-""
o

10

20

30

40

50

80

Fig. 19 An exemplary granite very low amplitude signal.
We attempted to develop a heuristic approach for classifying these very low amplitude events by examining the immediately preceding and subsequent events. This yielded results as ambiguous as the direct classification attempt The amplitudes are so low that there is not enough information present to make an
informed decision. This "class" is repeated but is only a significant portion (18 %) of the fIrSt granite Moden cycle and is rare in the rest of the test (0 to 4 %). We suggest that these Very Low Amplitude events areotherwise classifiable signals lost in noise rather than distinct enough to warrant a new classification. Anyremaining questions can be answered by newer, more expensive, instrumentation that would facilitate lowering the system background noise level. Simply boosting gain will just make the ambiguous signal, and thenoise, proportionally larger.
S. Conclusions
In summary, the same classes of AE waveforms were recorded for the granite test as were for the dolostone test The same events were present in Mode I and Mode n loadings, implying that the same mechanisms are al work. The waveforms recorded during the granite test were generally low amplitude signals
compared to the events from the dolostone test This is associated with the granite fracture growth increments being on the scale of the small individual grains rather than the fingernail-sized facets of the dolostone. The granite signals also displayed some high frequency oscillation believed to be caused by reSonanceof loose rock flakes and grains in the fracture zone.
While the analysis of data from this experimental program allowed many interesting and important
conclusions to be reached, the experiment seemed to raise as many questions as it answered. Many of theambiguities about event validity and shape could be answered in tests using a lhree dimensional array of at810

least six transducers. This would allow the accurate location of where the event took place, and the calculation of the event Green's function, of particular interest for Class D events so that the relevant source kinematic could be determined. Since this was the only class of signal from fracture that is not believed caused
by a step-like source related to tensile crack growth, the Class D signal is believed to hold the answer about
what processes besides tensile cracking exist in the process zone. Accurate source location would also
answer questions concerning the validity of Class E events, and would increase the understanding of how thespatially-distributed crack tip process zone develops in the Mode n geometry. This experimental program isseen as productive and rewarding, but is is also seen as the starting point for many more studies and discoveries. It is hoped that enough researchers take this as a signpost on the road to decoding the natural infonnation given us by nature.
Acknowledgement

This project is currently being funded by the State of Texas Advanced Research Program, FICE code
003658-512. Previous support was also provided by the National Science Foundation, Contract MSM8600567. This support is greatly appreciated.
References

K. Aki and P.G. Richards (1980), Quantitative Seismology, W.H. Freeman, San Francisco, p. 45.
FJ. Bueche (1980), Introduction to Physics for Scientists and Engineers, McGraw-Hill, New York, pp.
712.
S.D. Glaser (1990), "Acoustic Emission Waveforms Produced by Discrete Crack Propagation in Rock,"
Doctoral Dissertation. The University of Texas at Austin, Austin, Texas, p. 368.
S.D. Glaser and P.P. Nelson (1992a), "Acoustic Emissions Produced by Discrete Fracture in Rock: Part 2:
Kinematics of crack growth during controlled Mode I and Mode II loading of rock," International Journal ofRock Mechanics, in press.
S.D. Glaser and P.P. Nelson (1992b), "High-Fidelity Wavefonn Detection for Acoustic Emissions From
Rock," Materials Evaluation, 50(3), 354-366.
S.D. Glaser and P.P. Nelson (1990), "Correlation of Quantitative AE Waveforms with Discrete Fracture
Mechanisms in Rock", Progress in Acoustic Emission V, JSNDI, Tokyo, pp. 414-421.
S.D. Glaser and P.P. Nelson (1989), "Quantitative Characterization of Acoustic Emission Waveforms
Produced by Discrete Mode I Crack Propagation". Proc. International Conference on Recent Developmentsin the Fracture of Concrete and Rock, Wales, Great Britain. Elsevier, London, pp. 111-120.
K. Hayashi and H. Nishimura (1986), "Considerations on Field AE Data Based on Theoretical Studies of
Elastic Waves Due to Sudden Movement of a Subsurface Reservoir Crack," Progress in Acoustic Emission/II, JSNDI, Tokyo, pp. 742-749.
Y.S. Jenq and S.P. Shah (1989), "Trajectory and Stability of Cracks in Concrete Subjected to Mixed Mode
Loading," Proceedings of the 1989 SEM Spring Conference on Experimental Mechanics, Society for
Experimental Mechanics, Bethal. CT, pp. 43.

811

T. Kishi (1985), "Acoustic Emission Source Characterization and Its Application to Micro-Cracking,"
Zeitschrift Fur MetalIkude, 76(7), 512-51.
A.R.lngraffea (1981), "Mixed-Mode Fracture Initiation in Indiana Limestone and Westerly Granite," Proceedings, 22nd U.S. Symposium on Rock Mechanics, pp. 186-191.
J.E. Michaels, T.E. Michaels, and W. Sachse (1981), "Applications of Deconvolution to Acoustic Emission Signal Analysis," Materials Evaluation, 39(10), 1032-1036.
P.P. Nelson and S.D. Glaser (1992), "Acoustic Emissions Produced by Discrete Fracture in Rock: Part 1:
Effects of system: source location and orientation effects," International Journal of Rock Mechanics, inpress.
M. Ohtsu and K. Ono (1986), 'The Generalized theory and Source Representations of Acoustic Emission,"
Journal of Acoustic Emission, 5(4), 124-133.

CL. Pekeris and H. Lifson (1957), "Motion of the Surface of a Uniform Elastic Half-Space Produced by a
Buried Pulse, n Journal of the Acoustic Society of America, 29, 1233-1238.
M. Shibata (1984), "A Theoretical Evaluation of Acoustic Emission Signals, the Rise Time Effect ofDynamic Forces," Materials Evaluation, 42(1), 107-120.

S12

Estimation of Maximum Stress in Old Railway Riveted
I-Girder Bridges using Acoustic Emission Signals
Hisanori Otsuka, Hiroshi Hikosaka, Hiroyuki Miyatakeand Syouzou Nakamura
Hisanori Otsuka is affiliated with Public Works Research Institute. Ministry of Construction. Takehara,
Tsukuba 305. Japan. Hiroshi Hikosaka is with Kyushu University. Hiroyuki Miyatake is with JR Kyushu
Corp. and Syouzou Nakamura is with Kawasaki Steel Company.
Abstract
The applicability of acoustic emission tests for the estimation of the maximum stresses in old rivetedgirder bridges was investigated. A good agreement between experimental and theoretical values was found.
1. Introduction
Acoustic emission is a type of nondestructive testing methods which could monitor the stress variation. the yielding of materials and initiation and propagation of cracks. An early application to steel structures was reported by Pollock (1972) on the performance of a portable military bridge. In the test, AE sources were attributed to locations where plastic deformation had occurred. In the United States. successive research programs have been conducted to develop AE monitoring systems for bridges (ASNT. 1987).
It is well known that AE signals are seldom detected at stress levels less than the maximum stress previously experienced in the materials (Kaiser. 1953). Significant AE signals can be observed once stress exceeds the previous maximum level. The phenomenon is called Kaiser effect
The maximum stress is needed for predicting the residual fatigue life of structures. because fatigue damage may be evaluated from this information. From this viewpoint, we investigated the possibility of theapplication of AE to the maximum stress estimation in old riveted girder bridges. In addition. we studied
AE signals during tensile tests in specimens of various ages.
2. Experimental
The measurement of AE signals in our experiment was conducted using the 9502U-PLOT (NF Circuit
Design Block Co.) The equipment consists of two transducers. two pre-amplifiers. a main analyzer, an X-Yplotter and an oscilloscope. Using the analyzer. hit time. source location. maximum amplitude of AE signals. and external data (strain and load, etc.) can be recorded in real time, and then analyzed results are obtained by an X-Y plotter.
Old railway girder bridges used for this experiment are a 95-year-old bridge with a 6.7 m span length (inservice for 92 years) and a 60-year-old bridge with 5.99 m span length (in service for 54 years). The bridges
consist otsingle-span-deck girders. The 95-year-old girders were separated into a single plate girder at thesite. Rust and paint on these girders were removed by shot-blasting.
Both girders have doubly symmetric cross sections. Flanges of the girder consist of two angles and one
cover plate. Figure l(a) shows the cross section of the 95-year-old girder. The 60-year-old girders have thesame cross sectional configuration except for the dimension of 747 rom height and 223 rom width. Figurel(b) shows the cross section of the girder tested under fatigue loading. These girders (named as test girders)were prepared for testing by cutting off from the original girder and welding a compression flange to theweb. AE tests were conducted for the original girder and the test girder for the 95-year-old girders and the

513

81

1-.3
I zaa 1--1t[~J
Fig. 1 Cross section of a 9S-year-old girder. (a: left) Original shape. (b: right) Shape for fatigue test
7(>c10")

8

II

2

1000
!

100

2000

.

3000

.,,.,,

L-o--'

200

300

~

,
400

IHlOO

",,/em'two test girders for the 6().year.ald girders. AE
signals were measured at lower flanges, because
these flanges were in service mainly as tension
members until the girders were removed. Sample
specimens for tensile tests were cut off from web
steel near the neuttal axis.
Table 1 shows the chemical composition of the
9S- and 60-year-old steels. Values of phosphorus
and sulfur in both steels are slightly higher thanthose values of an ordinary mild steel, nS-SS400.
However, both steels could be comparable toSS4oo. The mechanical properties of both materials
are also shown in Table 1. The yield and theultimate strength of the 6().year.ald steels are lower
than those of comparable steels and the elongationof the 95-year-old steel is the lowest of all steels.

L-_

BOO

MP.

3. AE Signals During Tensile Tests

Fig. 2 Total AE counts vs. sttess (new material,
SS400).

Figures 2, 3 and 4 show the relationshipbetween total AE counts and nominal sttess forSS4oo, 60- and 9S-year.ald steels. A threshold level was set at 60 dB in these experiments. It is found thatthe occurrence of AE signals are different before and after yielding. The number of AE events is clearly
discriminated into two stages of pre- and post-yielding. Prior to yielding, AE event rate is smaller than thatof post-yielding. After yielding, curves of cumulative AE counts are varied, depending upon material. In thecase of new SS400 steel. the occurrence of AE signals decreases as suesses approach the ultimate strength.
In contrast, AE generation of the 95-year.ald steel increases close to the ultimate strength. The 6Q-year.ald
steel exhibits an intermediate trend.
Figure 5 shows an example of Kaiser effect, conducted using the 6O-year.ald steel specimen. The specimens were machined from the neuttal axis of the girder. Thus, the specimens were not significantly
suessed during service. It is clearly observed that AE signals at the cycle 2 begin to occur from about 70 %level of the maximum suess at the cycle I, and the occurrence of AE signals is less active than those of cycle 1. The slope of the curve at the beginning stage in cycle 2 is approximately equal to the slope near the
maximum sttess of cycle 1. Considering the results of all specimen. AE signals in cycle 2 begin to914

Table 1 The chemical composition and mechanical properties of the 95- and 60-year-old steels.
Test girder
95 year old
60 year old

C

S 1

Mn

P

S

A I

O. U
0.12

0.017
0.018

O. 57
0.61

0.057
0.055

0.066
0.043

0.001
0.005

0.204

0.01

O. 50

0.015

0.037

0.001

Test
girderYield stress( MPa )

Ult i!:latestrength( MPa )

Youns' smodulus( "'Pa )95 year old

289

433

20.6110 4

24.1

403

20. hl04

29.7

20.61104

29.3

60 year old

newII

Locationweb
stiffener
web

265
284

444

EI onga t ion(%)(lCl0')

4

No.2

12 (Xl0')
II
Il

3

•

12

:!1

I

No.1

a

Iu
"II

§

Sa
Iu

..

~I

!.

40110

.&0

Fig. 3 Total AE counts vs. stress (a 60-year-old
girder).

_AD

5"'ill/em'

Iiio'"'PitFig. 4 Total AE counts vs. stress (a 95-year-old
girder).

actively occur at about 70% of the maximum stress in cycle 1. Eventually. the rapid increase on the slopeof the cumulative curves is observed above the previous maximum stress. It is noted that AE activity
during the unloading is almost zero.
4. Estimation of Maximum Stress from AE Signals in Old Girders
AE signals were measured for the 95- and ~year-old girders during two loading-unloading cycles. Two
AE sensors were installed on the top surface of the tension-side flange near midspan for the 95-year-old
girder and on the bottom surface for the 60-year-old girder. Two sensors face each other across the rivet.
Strain gauges are attached on the flange outside of the rivet. Therefore, strains over the so-called net areacross section were measured. During the experiment, strains were also recorded onto the AE analyzer.

815

Fig. 5 The confumation of Kaiser effect.
1l00G

10lI0Illoading

ere/et1 - 0 &1100

crc:/et2 .. -(>----

unlODd;ng_.~.--

lll4lll

o
D

lAD

lOll

.r

Figure 6 shows the relationship between the
cumulative AE counts and strains of the 95-year-old
girder. AE signals increase beyond the strain of 200
x 10-6 of the fIrst cycle. This may be associatedwith Kaiser effect. A cause of AE signals during theunloading could be due to the noise of friction between the components of flange.
To estimate the maximum stress for both girders, Figs. 7 and 8 are drawn in the magnifIed scale
for cumulative AE counts. It is rather diffIcult to
identify the point of changing slope, because theslopes change gradually in the graphs. Consequently, the transition points were determined as theintersection of two extrapolated lines. The strain of
this intersection is considered to correspond to the
maximum strain. Multiplying Young's modulus tothe maximum strain, the maximum stress based on
Kaiser effect was determined. The maximum stresses for the 95- and 60-year-old girders were estimatedto be 43.2 and 32.4 MPa, respectively.
In estimating the maximum strain of the firstloading from the second loading curves, only 6.5%
and 3.3% errors were encountered for 95- and 60years-old girders, respectively. This means thatwhen time elapsed is shan, the accuracy of the
maximum stress estimation from the AE cumulative curve is fairly good because of the existence ofclear slope change.

5. Estimation of Maximum Stress Caused
by Locomotive Loading

According to the traffIc records on the maximum loading applied to the girders, DS I-locomotive (KS equivalent value:: 142 kN) and Clllocomotive (KS = lOS kN) were the heaviest locomotives for the 95- and 6O-years-old girders, respectively. The maximum stresses were calculated from
Fig. 6 Total AE counts during cyclic loads.
the design information on the wheel arrange-ments.
Loading direction on the test girders was not
defInite and the maximum stress was calculated for both directions. The section moduli of the girders were
calculated from the initial cross section because the degree of the corrosion was not known. Impact
coefficients were calculated from the formula in the specifications, assuming that the maximum speed of thelocomotive is 60 kmlh. Based on these assumptions, the theoretical maximum stresses were computed asshown in Table 2. These values are considered to be a little higher than actual values, because thecontinuity and the stiffness of the rail are not taken into calculation.
When comparing these values with the estimated values from the AE tests, a clear agreement is observed. Therefore, it is concluded that by using the AE tests, the estimation of the maximum stress of oldgirders is possible with the sufficient accuracy.

816

100
I

. . ~lllu. al!'a!n •in ht lnadtfts'

I

,

••0

I

110

adDU.

~traln :In hi loadlnt I
I

I

I

•

I
I

I

I

1

I

~

I

1

I

I

~ 10

,"'""'L1i

200

)

"I

I

'10

100

.,

120

.

l&l toO

I
I

10011

o

·
I

I

• . . .JqQ atraln: 1n tat lo.dlna

·

12

I

100

I

I

. . .llNg at,aln
In ht lOlld'na

I
I
I
I
I

I

I
I
I
I
I

I

I
I

,
I

o

I

100

200

Fig. 7 Estimation of maximum sttess (95-year-old
girder).

D

6. Conclusion
Since the maximum sttess is one of the mostimportant information to predict the residual fatigue
life of sttuctures, the applicability of AE tests forthe estimation of the maximum sttesses in old
riveted girder bridges was investigated. A good
agreement between experimental and theoreticalvalues was found. Thus, it may be concluded thatAE tests are successfully applied to the estimationof the maximum stress of steel girders.
References
ASNT (1987), Nondeslruclive Tesling Handbook,

Fig. 8 Estimation of maximum stress (60-year-old
girder).
Vol. S, AE Testing, ASNT, Columbus, OH, pp.
333-339.

1. Kaiser (1953), "Erkenntnisse und Folgerungenaus der Messung von Oerauschen bei Zugbeanspruchig von Metallische Werkstoffen, Archiv
fur das Eisenhutten Wesen, 24(1-2),43-45.
A.A Pollock and B. Smith (1972), "Sttess-Wave
Emission Monitoring of a Military Bridge", Nondestructive Testing, pp. 348-353.

Table 2 Maximum stresses.

Test girder

Absolute Maximum mo.
maximum at measuring
mo. (kN) points (kN)

(mm 3 )

Impactcoer ficient

SectionmodulusCalcu lated Max i. stressmax i. stress by AE tests( MPa )( MPa )

95 year old

270

239 or 247

6. 74110 6

1. 30

46. 2 or 47.6

43.2

60 year old

158

153 or 156

5.45xl0 6

1. 31

36.6 or 37.5

32.4

817

Another Look at Booming Sand

Marcel F. Leach and Gottfried A. Rubin
The authors are affiliated with Dept of Physics & Astronomy. Laurentian University. Sudbury. Ontario
P3E 2C6. Canada.
Abstract
This is a comparative study of a relatively rare phenomenon which occurs in some desert regions.
Analysis of the acoustic emissions of booming sand reveals an intense and very distinct beat pattern; to thebest of our knowledge. this is the first published report on this feature obtained in the laboratory for airborne signals produced by relatively small samples of booming sand. In-situ samples of the sand have been
sectioned into small size ranges in an attempt to determine whether any size fraction has a monopoly onbooming. The period of the camer wave within the beat patterns denotes low frequency emissions having
peaks which seem proportional to the inverse of the particle diameter. The length of the beat envelope.
which characterizes the beat period. appears to vary with the number of sand particles; this suggests that the
motion of the sand can be represented by a cell model with varying mean free path. These observations
should help in the investigation of the sounding mechanism of the booming phenomenon.

1. Introduction
These are typical descriptive accounts of the booming phenomenon which occurs in some desert
regions:a) In the field: R.A. Bagnold (1954): "On two occasions it happened on a still night, suddenly - a
vibrant booming so loud I had to shout to be heard by my companion. Soon other sources. set goingby the disturbance, joined their music to the fust. with so close a note that a slow beat was clearly recognized. This weird chorus went on for more than five minutes continuously before silence returned andthe ground ceased to tremble"b) In the lab: A rhythmic thrum of a few hundred Hz like the rubbing of fmgers on a washboard. producing noticeable vibrations in the hand.
Several theories have been suggested to explain the sounding mechanism. classified generally:I) with respect to the sources, i.e. the particles of sand or the interstitial air. into friction or cushion
theories and 2) with respect to their physical characteristics such as grain shape. texture. degree of sorting.
moisture content and number of adhered solid contaminants and other properties which fonn the basis of thecoherence theories which postulate that all grains vibrate in phase and move in large masses. as proposed by
Reynolds (1885) and Poynting and Thomson (1909).
It seems however that no individual theory can explain the phenomenon adequately and there exists. to
our knowledge. only one report (qualitative) by Lewis (1936) of what may have been successful reproduction of booming in the laboratory. where the vocal quality was quickly lost unless kept in airtight containers and which could only be restored after special heat treatment AE was not recorded nor is there any mention of a discernible beat.
The following is a report on a quantitative study of AE from two closely matched sands, in an attempt
to help fonnulate a comprehensive theory of the booming mechanism.

S18

2. Procedures
1) Microscopic examination of the sand grains has revealed that a) Booming sand is reasonably spherical, rounded, wilh a smooth polished surface and b) Ottawa sand is also spherical. rounded, with a rougher
textured surface. But booming sand is no more spherical and rounded than other sands or than ordinary glass
beads. and no smoother than the latter.
2) Numerical size analysis by image splitting analysis shows that:a) booming sand has a uniform Gaussian distribution over a size range of 120 - 560 J1IIl with an average diameter and standard deviation of 321 ± 84 J.U11, typical of other dune sands. This agrees withresults obtained by conversion of a weight size distribution obtained by sieving where between 60 to
69% of a sample of booming sand was retained within the SO to 70 sieve range. (See Fig. I.)b) Ottawa sand extends over a size range 160 - 640 J1IIl with an average diameter and standard deviationof 358 ± 115 J.UI1, deviates slightly from a uniform Gaussian distribution by having a higher proportion of smaIl sizes. (See Fig. 2.)
Thus this sample of Ottawa sand is slightly coarser than the booming sand, not quite as uniformly distributed and has a slightly higher standard deviation. These results agree with those obtained by sieving analysis. However. the size distribution of booming sand is no more uniform and narrow than many ordinary
sands, and considerably less so than many samples of ordinary glass spheres.
3) Large samples of each sand (approximately 375 - 740 g) were inserted into glass 675 ml coffee jars.
Shaking by hand produced very different audible signals. the Ottawa sand generating a low intensity swishing sound while the booming sand produced a much louder thrum and generated vibrations in the hand.
These sounds were picked up by a small microphone secured through a hole in the jar covers and fed into
the input of a storage oscilloscope to produce the following records, at same sensitivity level (20 mV/div.)
and same sweep rate (20 msldiv.): Ottawa sand emitted low intensity. typical random noise patterns as seenin Fig. 3 and .booming sand emitted high intensity beat patterns. which are believed to be the flJ'St recordedin the laboratory from air-borne signals produced.by relatively small samples of booming sand. Carrier andbeat periods are shown in Fig. 4.
4) Each sand sample was then sieved into 8 narrow fractions ranging in size from particles retained on
sieve 35 to those passing sieve 80. Each subsample was hand shaken and the emitted signals captured on
the storage oscilloscope as before.
5) Each of the subsamples of booming sand was fractioned successively into 1/2, 1/4, 118 ... of its
original size and its emission examined and recorded.

3. Results and Discussion
The beat signal shown in Fig. 4 is typical of the AE recorded for those fractions of booming sandwhich contained approximately one million particles or more. which occurred for 5 of the 8 fractions.
Among these five fractions which produced beats. there appeared variations of the carrier period and, much
more noticeably. very large variations in the beat period depending on the grade of the fraction. Carrier andbeat periods were then averaged over 60 signals for each of the booming samples.
Figure 5 summarizes results for the average carrier period (te) versus nominal sieve opening size. where
nominal sieve size has been used to represent particle size (0). The dots refer to the five booming fractions.
The range of variation of te is its standard deviation over 60 measurements while that of D has been extended over the nominal size of grids neighbouring those of each retained sample. The open circle is for data
given by an unfractioned sample of booming sand, where the variation in D is the measured standard deviation.
There appears to be a linear relationship between these variables. in agreement with similar results
which we have obtained for glass and steel spheres. Projections onto the y-axis and conversion into the frequency domain indicate that the carrier frequencies of these booming samples range from 172.4 to 294 Hz.

519

600

7
6

500

-;;;

.§

4

,~ 3

N =250=321 urn
S.D.=tS4.l.lffi

o200

1~.9999.9

99

90

50

lO

I

2

o0'---.....
60--'20'---1.....
80----::24':-:0-~300~-:-360:::!

OJ 001o (urn)Cumulolive %oge Greater 1I1On or Equollo a Staled SizeFig. 1 Numerical size distribution of booming sand.

Fig. 5 Average carrier wave peri¢ vs. particle size.
90
B

E

70

3450
-;;; 60

.s

~

~ 350

,..!' 50

i:::i

N =250=358urnSD.= "U5 urn';9.99 99.9

4

o

2

99

90

50

10

30

' 0 ' 001

2°0

60

120

Cumulolive %oge Greater Ihan a Equal to a Slaled SizeFig. 2 Numerical size distribution of Ottawa sand.

180

240

300

360

o (um)

Fig. 6 Average beat period vs. particle size.
9075l------i

1 divFig. 3 Typical signal emitted by Ottawa sand.
fe&. 1:( IBOJJm-----1e 2: 180JJm
eEl 3: 212JJm-II-Iex 4: 250JJm

30

150

5:300um
2

3

4

5

6

7

B

9

Number of Particles (x 106 )..--------..

Fig. 7 Average beat period vs. number of particles
for each subsample.

1 div

Fig. 4 Typical signal emitted by booming sand.

920

Figure 6 shows results for the average beat period (tB) versus particle size. Again there is a reasonably
linear relationship between these two variables. Projections onto the y-axis yield beat frequencies for thesesamples ranging from 12.5 to 33.7 Hz, which all exceed the limit of perception by the human ear.
The result obtained for the unfractioned sample seems to indicate that, while no size fraction with a sufficient number of particles has a monopoly on booming, the fractions of largest sized particles andlor themost populated fraction are probably most active. It also seems that booming is not critically dependent on
the narrowness of the size distribution.
Deviations of the two coarsest samples from the straight line was believed to be attributable to thenumber of particles in each of these fractions, the coarsest fraction having many fewer particles than thethree frnest while the second coarsest contained many more particles than any other fraction. In order to test
this assumption, each booming subsample was further fractioned as outlined in Procedure 5. Results forthese fractions are plotted in Fig. 7; they show 1) a very strong dependence of the beat period on sample
size and provide a reasonable explanation for the fact that sands which produce audible beats in the field do
not necessarily generate beats which are discernible by the human ear when operating on small laboratory
samples, and 2) a dearth of particles beyond the 1I8th fraction also explains the eventual disappearance ofthe beat patterns.
4. Conclusion

From the experiments reported above, it seems that several agents are responsible for the booming
sands. It is likely that booming is mediated by factors such as grain shape, texture, degree of sorting, moisture content and number of adhered solid contaminants. The present study suggests for the fust time thatknowledge of the number of sand particles may also be necessary, if not sufficient, in order to understandbooming dunes and to identify the booming mechanism. It is hoped that the experiments reported here do
not muddy the water but rather encourage further inquiry aimed at solving the basic query into how
booming sands work and suggest new lines of thought for others who might be similarly intrigued with thephenomenon.
References
R.A. Bagnold (1954), "The Physics of Blown Sand and Desert Dunes", Chap. 17, Chapman Hall, pp. 247256.
D. Reynolds (1885), "On the Dilatancy of Media Composed of Rigid Particles in Contact", Philos. Mag.,
Fifth Series, 469-481.
J.H. Poynting and JJ. Thomson (1909), "Textbook of Physics, Sound", Chap. IX, Charles Griffin andCo., pp. 134-145.
A.D. Lewis (1936), "Roaring Sands of the Kalahari Desert", South Afr. Geog. 1., 19, 33-49.

821

Expected Acoustic Emission from Around a Shaft in Intact
Rock
B.J.S. Wilkins and G.L. Rigby
The authors are affiliated with Materials and Mechanics Branch, AECL Research, Whiteshell Laboratories,

Pinawa, Manitoba ROE lLO Canada.

Abstract
We are examining the possible disposal of nuclear fuel waste, in a vault, deep in plutonic rock. The
most probable way in which components of this waste could escape to the biosphere is by water ttansport
through cracks in the rock. Therefore, there is interest in the possibility of time-dependent changes occurring in the crack population. Below 100°C, the dominant mechanism of deformation in intact rock is microcracking. Hence, a model (Micro-Crack Driven Intact Rock Creep) has been developed, from which canbe estimated time-dependent displacements (creep) and acoustic emissions around openings in rock, in response to stress-state perturbations. In this model, the Sb'ess-state safety factor (SF) around openings is estimated. SF is the ratio of the maximum principal sttess at failure (alf) to the applied maximum principal
stress (al). Slow crack-growth data are used to estimate time-dependent changes in SF, from which displacements and the disuibution of acoustic emissions are estimated.. The model has been applied to a vertical shaft at the Canadian Underground Research Laboratory. The predicted displacements and acoustic emissions compare favorably with those observed, around the shaft.

1. Introduction
An opening introduced into a rock mass perturbs the in situ stress state. As a result suess is concen·
ttated around the opening. The sttess concenb'ation is expressed as a distUIbance in the strength safety factor.
Suength safety factor (SF) is defmed as the ratio of the maximum principal suess at failure (alf) to the applied maximum principal suess (0'1); that is au/al. A Micro-Crack Driven Intact Rock Creep (MCDIRC)
model has been developed to estimate the decline in both the uniaxial compressive sb'ength of the rock (~)
and SF, produced by time-dependent microcracking (Wilkins and Rigby, 1989). The model assumes that radial sttain (ft) results from rock dilation caused by the creation. extension and dilation of microcracks.
Microcracking has the additional effect ofreducing the rock stiffness.
At the Canadian Underground Research Laboratory (URL). AECL Research is studying the post-excavation response ofrock (granite) around openings by a variety of techniques. These techniques include recording acoustic emission (AE). measuring sb'ain (using extensometers) and recording the velocities of com·pressional and shear (P and S) waves in the frequency range of 10 to 15 kHz. In this paper, the spatial dis·
uibution and the time scale of the post-excavation response predicted by MCDIRC are compared to thosemeasured around the shaft, and at other openings. at the URL,

2. Input to MCDIRC
The model requires as input the following: the in situ suess state (the suess state prior to excavation),that is the vertical (Gz) and horizontal stress components (ax, ay); the compressive strength of the rock (~;the values of certain constants (m. n, and s); and the original radius of the shaft. From this information canbe estimated: the time required for any small wall movement postulated and the corresponding radial-strain(ft) disuibudon (alternatively, wall movement and Er can be calculated for any desired time), and'changes
everywhere of both SF and Gz.
822

The constants m and s are those required to estimate the maximum principal stress at failure. (alf) is
given by (Hoek and Brown. 1980).
alf '" I OJ I + ~ [m(o.Y~) + 5]112(1)
where OJ is the minimum principal stress.
Essentially. as slow crack-growth proceeds (in compression it is the cooperative growth of many microcracks). the strength of the rock decreases and SF falls to unity, at which time the rock fails. The derivative of SF with respect to time is given byd(SF) / dt .. [(SF)(3-n)] / (n-2)(2)
where t is in seconds. The constant n depends on the rock type and the environment (Anderson and Grew.
1977; Atkinson. 1987; Wilkins et aI., 1984). The procedures to obtain d(SF)ldt and n experimentally are
discussed elsewhere (Wilkins and Rigby, 1989).
3. How MCDIRC Works
The distribution of SF around the opening is estimated. A ray, out from the opening. is chosen in the
direction of interest For a vertical shaft, it is most useful to choose a ray out from the center-line in a horizontal plane. Then SF is determined along the ray. This is illustrated in Fig. I, where at t .. 0 and t > 0, SF
is shown as a function of the radial distance (r), normalized by the original radius of the shaft (ro). Att = 0(immediately after excavation), SF '" g(r/ro) and for t > 0, SF = f(r/ro)' The function f(r/ro) comes directlyfrom d(SF)/dt, and is given in terms of g(r/ro) byf(r/ro)(n-2) .. g(r/ro)(n-2) - g(a)(n-2) + b(n-2)(3)
where (a,b) are the coordinates of a point that lies on f(r/ro). Figure 1 indicates that at t > 0, SF has decreased to unity on the shaft wall, and the shaft wall has moved inward to (r/ro) .. l; (l; is just less than 1).
Strain rates in the radial direction (d£rIdt) are estimated from d(SF)/dt, g(r/ro) and f(r/ro)' This is discussed
elsewhere (Wilkins and Rigby, 1989).

4. Post-Excavation Rock Response Around Openings at the URL
The source location of the acoustic emission (from microseismic events) at the URL has been studied(Tabeli and Young, 1989). The main shaft was excavated incrementally using a series of full-face blasts. AE
was recorded for several hours after most of these blasts. Figure 2 shows some of the data. Figure 2(i)shows the number of events versus time for a typical blast (#65, depth 348 m, 1988 February 28, 23:29h).
Figure 2(ii) is a summary of all events from several blasts between the 324 and 443 m depths. It shows AE
versus rlro for a given time period. In both figures, the abscissa is normalized by the maximum value.
At the 352 m depth, time-dependent radial strain has been measured with three Bof-ex extensometerstwith a repeatability of ± I JlI1l (Martin, 1988; Thompson and Lang, 1987). The excavation to the 352 m
depth was completed on 1987 February 4. The extensometers were put in place and tested by February 8.
Immediately after further blasting (-10 minutes) the output from the extensometers were recorded
continuously until February 26 (February 28 for extensometer 1). In Fig. 3 are reproduced the results fromextensometer 2 (the results from extensometers 1 and 3 are similar). These results showed that the radialstrain rate (d£rIdt). over any radial gauge-length for a given extensometer, decreased to a constant value after
about 6 days. In Table 1. the constant values, measured for the three radial gauge-lengths (which were 0.1 to
0.45, 0.45 to 0.8 and 0.8 to 5.0 m), are given along with the d£p'dt values predicted by MCDIRC.
The shaft at the 352 m depth is circular in cross-section with a radius of 25 m. The in situ stress state,
the uniaxial compressive strength of the rock, m and s are estimated to be ~"" IS, ax "" a y"" 35 MPa; ~"" 200 MPa everywhere; m "" 28, and s "" I, respectively (Martin, 1990). The MCDIRC predictions in Table
1 were based on this input with selected values ofn :: 17, 18, 19,20,22 and 24.

623

D

,;

..
e;::
.. .
...
.
::: .
......
.

g(r/c o ); t .. 0

on

~

.;

f(r/r o ); t

~

>0

l'II

II.

011

on

..

(1 100.0t) is small
100.5

t,l

101.0

10'.5

r/r o

1~2.0

101.5

(XlO-1)

fjg. I Safety factor versus normalized radical
distance (r/ro), for t =0 and t > O.
c: 1

~

(1)

01
<II

....
c9

1

5

Hours

1
(ii )

In fig. I are shown the Sf distributions fromMCDIRC, for when Sf has decreased to unity at
the wall (t > 0) and for Ihe original SF distribution(t = 0), both for n = 18. In Fig. 4, normalized £r andnormalized ~ are plotted against rlro, when Sf hasreached 1 at Ihe wall. The normalized £r plots are forn = 17 and 24, and Ihe normalized <\: plot is for n =18. In these examples, for small wall movements(up to a few mm), the normalized strain plots are
independent of time. This is because the value ofd~dt, at any rlro, is constant for small wall movements. When ~ = 0.9988, the wall has moved inward by 3 mm, and £r = 0.10 and 0.16 at the wallfor n = 17 and 24, respectively (MCDIRC).
At the URL, well after excavation (1 year) inroom 205 (depth 240 m), P and S wave (10 to 15
kHz) velocities were measured at various distances
into the rock (Hayles, 1990). Typical results areshown in Figs. 5(i) and (ii), where normalizedwave-velocities are plotted against distance into theroom wall.
P and S wave velocities offer a measure of rockintegrity because more attenuation is expected wilh
more cracks. The velocities of the recorded P waves
appeared less in the first metre into the wall, whichis consistent with the rock there containing morecracks. The S wave results showed no particulartrend, which is not surprising because their arrival
times are less certain.

5. Discussion and Conclusions

-.,
c:

o

<II

:5

-......
~

...o

..;

1 4 8

r/r o

fjg. 2 (i) Normalized acoustic emission versus
time after blasting; (il) normalized total acoustic
emission after blasting versus normalized radial
distance (r/ro)'At the URL, all the measurements indicate asimilar pattern of post-excavation response, wilh
time and with distance into the rock around openings. The main features of the pattern are, a rapid
decrease in response with distance into the rock,away from the opening wall, and a rapid decrease in
response with time everywhere. However, Ihere arelarge differences in scale for bolh the distance into
the rock and the time. The AE decays to smallvalues in just a few hours after blasting and originates from as far out as (r/ro) = 5. In contrast, theradial strain, as the extensometers indicate, took
several days to decrease to small values, and was
hardly detectable at rlro > 2.
Most probably, the initial high strain rates(measured by extensometers) and the AE indicate
mainly the settling-in immediately after the blasl924

250.0200.0... 150.0
I:

e

..,

0.10-0.4~

lI'l

....

0

I.l
CJ

...

....

100.0

:I:

50.0

t1"" .........

,V ...
., .....- - -- -- .

0.45-0.85 m

4Il

...

.....

'"

r-

0.0
Febl04....

Febl14~... .........

0.85-5.0 m

," -

~

Febl09. y

Febl19

-- -

1-

..

-

Feb/24

FebJ29TillieFig. 3 Microstrain (radial direction) (Er) versus time (reading date) for extensometer 2.
Table 1 Data of d£rIdt from URL at 352 m depth; Measured by extensometersand predicted by MCDIRC.

Extensometer 1.
ExteDsometer 2.
Extensometer 3.
n", 17n", 18n", 19
n= 20

n 22
CI

n",24

Radial Gauge-Lengths
0.10 to 0.45 m
0.45 to 0.85 m(rc:/r '" 1.04 to 1.18
1.18 to 1.34
Constant d£,Idt (h-1) from Extensometers1.24 x 10-7
9.80 x 10-9
7
1.07 x 102.62 x 10-8
7
1.48 x 101.22 x 10- 8
d£rIdt (h- I ) from MCDIRC
6
2.6 x 109.7 x 10-9
6.9 x 10-7
1.7 x 10-9
1.8 x 10-7
3.1 x 10- 10
8
4.9 x 105.6 x 10- 118
1.8 x 10- 11
3.6 x 102.7 x 10-8
5.9 x 10- 12

0.85 to 5.0 m
1.34 to 3.0)
5.56 x 10-92.80 x 10-9
3.50 x 10- 10
9.6 x 10- 11
1.4 x 10- 11
1.9 x 10- 12
2.7 x 10- 13
5.4 x 10- 14
1.1 x 10- 14

However, the decreases in the strain rates that occurred over a much longer time period were probably due totime-dependent strain produced by ongoing microcracking and were not associated with the blasting directly.
Presumably, AE also was occurring over the longer time period. but at a lower level than the installedsystem was capable of recording.
MCDIRC predicts the rapid decrease in the response with distance into the rock. However, it does notdirectly predict the rapid decrease in the response with time. This is because stress relaxation (produced byclosure of the opening and by stiffness reduction through microcracking) and blast damage are excluded fromthe model.

825

.....

W..

......

"c; t.

< 0; D • 18o"0........
CII
N

..:t

c
z

...ci

...,

........

CII
N

..

N

~

ci

~

:+---r---r---r--r-~===;:=~~
100.0
100.5
101.0
101.5
IO~.O
IO~.5
103.0
103.5
JOq.O
Normalized Radial Distance (XIO-2)

Fig.4 Normalized uniaxial compressive strength (~) and normalized radial strain (£r) versus normalizedradial distance (r/ro), where SF(wall) '" 1 and t > O.
::

;T------------...,

.....

(i)

... .

'0 ;:
......

.
.....
-... ..
:I><......

.....

...... ..:~

~:~

'"

"-

E
......

>,

;:

:

...c

.,;

CI

...c

0

> ::

(,I

CII

>

(il)

0

.....

(,I

~--------------,

CII.:!-:-:--""':"":':-----:r':":"'"""~~-__._-~,",:00
1.00
a.aa
~.DD
•. 00
s.aa
Depth (Ill)1"a+-.a~a-~I~.o~o-""::'Z.~DD---:"~.-:-:ao:----.•. -ao-~z.oa21

Depth (m)Fig. 5 (i) P-wave velocity versus nonnalized radial distance (r/ro); (li) S-wave velocity versus normalizedradial distance (r/ro).
The normalized £, versus rlro plots shown for n = 17 and 24 are similar. They both indicate a rapid decrease in nonnalized £, (1 to -104) in the fllSt -15 cm of the wall. The actual amount of wall movement
and the strain everywhere for a given time depends on the value of n assumed. For example, when the wall
has moved in by 3 nun. and at n '" 17 and 24, the strain at the waD is 0.10 and 0.16, respectively. Hence,some decrepitation at the shaft wall is bound to happen, no matter how carefully the shaft is excavated.
The major influence of n is on the strain rate. As n increases the predicted df/dt decreases. H 17 ~ n ~20, for the parameters assumed for the 352 m depth at the URL, MCDIRC predicts dE/dt values similar tothat measured by the extensometers (for the 0.1 to 0.45 m gauge-length). Judging from the literature, thisrange of n values is plausible for the rock at the URL (Atkinson, 1987; Wilkins et at., 1984).
526

In intact rock at < lOO°C. the response to blasting and to the SF disturbance around an opening should
be both immediate and time-dependent microcracking. All of the post-excavation responses are consistent
with this idea. The PIS wave and the AE results. and the agreement between MCDIRC and the fmal extensometer readings Oast 12 days), suggest that the extensometers did provide eventually a measure of the timedependent strain. produced by the stress concentration around the shaft, rather than simply a measure of theenergy ttansferred to the rock by the blasting.
The extensometer8 did not measure e, for the flI'St 10 cm into the shaft wall. However, the MCDIRC
prediction can be used to position the normalized Er (measured) versus rlro plot to allow a comparison with
the normalized AE versus rlro plot (normalized by the reading at the wall). The long-tenn extensometer data
and the predictions of MCDIRC can be compared on a quantitative basis. It seems improbable that AE ande, can be equated for a given volume of rock on a general basis. However, on a site-specific basis it may bepossible.
Acknowledgements
The work reported here was funded by the CANDU Owners Group (COG).
References
O.L. Anderson and P.C. Grew (1977). "Stress Corrosion Theory of Crack Propagation with Applications toGeophysics". Rev. Geophys. Space Phys., 15.77.
B.K. Atkinson (1987), Fracture Mechanics ofRock, Academic Press, New York.
J.G. Hayles (1990), private communication.
E. Hoek and E.T. Brown (1980), Underground Euavations in Rock, Inst Min. Metall., London.
C.D. Martin (1988), "Shaft Excavation Response in a Highly Stressed Rock Mass". Proc. NEA Workshop
on Excavation Response in Geological Repositories for Radioactive Waste, DECO, Paris, France, Winnipeg, Canada.
C.D. Martin (1990). "Characterizing In Situ Sttess Domains at the ABCL Underground Research Laboratory", Can. Geotech. J.• 27. 631-646.
S. Talebi and R.P. Young (1989), "Failure Mechanism of Crack Propagations Induced by Shaft Excavations at The Underground Research Laboratory". Proc. Int. Symp.• Rock Physics at Great Depth, Pau,
France, A.A. Balkema. Rotterdam, Netherlands.
S. Talebi and R.P. Young (1989), "Final Repon on Source Locations of Microseimic Events Induced by
Shaft Excavation at The Underground Research Laboratory", Conttact Rept to ABCL Research, Pinawa,
Canada.
P.M. Thompson and P.A. Lang (1987), "Geomechanical Insttumentation Applications at the Canadian
Underground Research Laboratory". Proc. 2nd Int. Symp. Field Measurements and Geomechanics, Kobe,
Japan, A.A. Balkema. Rotterdam, Netherlands.
BJ.S. Wilkins. A.R. Reich and W.R. WaUace (1984). "Slow Microcracking in Plutonic Rocks". Atomic
Energy of Canada Umited Tech. Rec., TR·264. Unrestricted, unpublished repon available from SDDO,
Atomic Energy of Canada Research Company, Chalk River, Ontario. KOJ 1LO, Canada.
827

BJ.S. Wilkins and GL. Rigby (1989), "MCDIRC - A Model to Estimate The Creep of Intact RockAround a Shaft", Proc.lnt. Symp. on Rock Physics at Great Depth, Pau, France, A.A. Balkema,
Rotterdam, Netherlands.
BJ.S. Wilkins and G.L. Rigby (1989), "MCDIRC - A Model to Estimate Creep Produced byMicrocracking Around a Shaft in Intact Rock", Atomic Energy of Canada Limited RepL, AECL-l0068,
Pinawa, Canada.

928

Acoustic Emission Monitoring during Microseismic Activity
Caused by Mine Subsidence
Brian R. A. Wood and Robert W. Harris
The authors are affiliated with CSIRO Division of Geomechanics, Lucas Heights Research Laboratories,
PMB 7, Menai 2234, Australia

Abstract
An underground coal mine was operational until 1979 and was then closed and the entry sealed. Someseven years later a residential subdivision was developed in the area above the previous mine. The development included a major shopping area and many hundreds of homes. On December 7, 1988, rumbling noises
were heard in the night by residents, and the following morning it was observed that in selected areas cracks
had appeared in lawns, gardens and roadways. An acoustic emission study of the area was commenced on
December 13, 1988 using two monitoring locations, and continued until the end of February 1989. The detected signals were in some cases probably associated with primary defect activity, while others were
thought to be secondary as they were consequential to the initiating defect activity. A range of signal analysis techniques applied to the captured waveforms indicated that a number of different source mechanisms(about 4 or 5) were operating. Activity from a nearby fault line was also detected which in a few instances
was seen to be activated by surface loading. Core samples taken from a number of drill holes into the minearea 155 m below the surface confmned that there had been some pillar collapse in some selected areas andsome rock movement in the 145 m of rock between the mined area and the surface.

1. Introduction
Subsidence of the ground in a residential subdivision had occurred which caused damage to property andwas therefore of considerable concern to local homeowners. The subdivision was constructed over an underground coal mine which was operational until 1979 when it ceased operations and the entry was sealed.
After a period of approximately seven years a residential subdivision was developed in the area above thisprevious mine and included a major shopping area and many hundreds of homes. On December 7, 1988,
rumbling noises were heard in the night by residents, and the following morning it was observed that in
specific areas cracks had appeared in lawns, gardens and roadways. The type of surface deformation occurring
is shown in Fig. 1 which shows the cracking along the roadway and Fig. 2 which shows damage to a housein the area.
An acoustic emission study of the area was commenced on December 13, 1988 using two monitoring
locations, and continued until the end of February 1989. Initially a simple long term monitoring project
was carried out and at a later time there was also an intensive monitoring phase for a period of one week.
The mine layout and the subdivision roadway is shown in Fig. 3.

2. Instrumentation.
Since it was not possible to place transducers directly onto bedrock, waveguides constructed of 25 mm
steel rod were employed. Holes were bored so as to reach bedrock at depths of 9 to 12 m and then eachwaveguide was then anchored into the bedrock by grouting with cement. Three monitoring locations wereprepared by drilling holes and grouting steel waveguides into these holes, however due to local conditions
and traffic movements only two monitoring locations were used which were about 200 m apart. One location which was in the grounds of a church was referred to as "Church" and the other located in a nearby829

Fig. 1 Surface deformation on roadway.

Fig. 2 Damage to a house.

Fig. 3 Section of long tenn monitoring record.
street referred to as "Rush Court". Bruel and Kjaer model 4335 accelerometers were then attached to the endsof the waveguides with silicon cement and the above ground assembly contained so as to be made waterproof and protected from the weather and environmental interference.
The signals detected by the transducers were amplified 40 dB by a preamplifier situated adjacent to thetransducer. and then this amplified signal was passed along a co-axial cable to the central monitoring location where the signal was boosted a funher 30 dB. The amplified signal was routed to a digital oscilloscopeso that complete waveforms could be captured and stored on computer disk (intensive monitoring phase) andenvelope detected for analysis by the CSIRO-EAR equipment (Wood and Harris. 1984) (intensive monitoring phase) and/or a counter/chart recorder combination (long term monitoring).

530

PSI _ling on
4.193593\

Data.t •• B:1UES14 .DAI

Fig. 4 One typical low amplitude event.
f-~

no _ling on
263.9838~

DataAt •• B:YED?

.lIAr

Fig. 5 A typical high amplitude event.

3. Results and Discussion
The monitoring program was divided into two sections, one being a 10 week period where data wasrecorded automatically, and the other being a 7 day period where data was observed as it was recorded andsome on-line analysis carried out.
The simple long term monitoring program indicated that activity was sporadic and when activity com·menced there was generally a pattern exemplified by a series of low amplitude events which increased in fre·quency of occurrence until there was a higher amplimde pulse and then there was little activity until the se·quence commenced again. A section of the recorded data which shows the spasmodic acoustic emission activity from both the monitoring locations as a function of time is shown in Fig. 3. This type of activity
could be considered to be indicative of some sort of 'stick-slip' phenomenon where activity increased until
there is an avalanche when a much larger energy release occurs and then there is a quiescent state until theprocess recommences.
Unfortunately very little of the avalanche effect was observed during the intensive monitoring program
however a variety of different waveforms were observed together with some waveforms which were similar
to the low and high amplitude signals observed visually during the long term monitoring when an observer
was present. These waveforms were typified by Fig. 4 for the low amplitude events (0.5 to 1 V peak ampU.

831

tude) and Fig. 5 for the higher amplitude events (2 to 5 V peak: amplitude). The right hand side of the
figures show the waveforms from the two locations with the upper trace associated with the location designated "Church" and the lower one associated with the location designated "Rush Court". The left hand sideof the figures provides the power spectral densities of the signals .for the range 0 to 50 kHz, and alongside
the spectra are the frequencies of the first two dominant spectral peaks.
An examination of Fig. 4 showed that the flCSt excursion is sometimes positive and sometimes negative so that the sources producing the acoustic emission will sometimes start .off as a compression andsometimes as a tension (the identification between the sign of the excursion and the tension/compression
nature of the source requires an accurate determination of the phase characteristics of the system which could
not be carried out at the time).
Figure 6 shows a higher frequency transient with a 4 V peak: amplitude which may arise as a result of
the excitation of the higher order vibration modes in the steel waveguide as a consequence of the deformation of the waveguide and grout composite due to bedrock motion resulting from the higher level of energy
released by the active source. An idea of the range of lower frequency transients that were observed is shownin Fig. 7. These again show different polarities for the first excursion. The slow ringdown and 'erratic' nature of the pulses given in Fig. 7 would be consistent with larger source to transducer separations associatedwith deeper or more distant rock defonnationlmovement.
The well defined envelope of Fig. 8 indicates that the signal originated at some distance from themonitoring location and there were no major discontinuities or boundaries in the propagation path to diston
the final signal.

PSD _ling on1l") IAI,/,

Dataset •• B:tuES62

Fig. 6 High frequency transient.

PSD sea ling on
83.1B166

Fig. 7 Typical low frequency events.

S32

.DAT

PSI) _lingon

Datant .. D:nJES73.M!

Fig. 8 Transient with well defmed enelope.

PSI _Hng an
5.696685

Dataset •• D:1UESS8.IIA!

Fig. 9 Unusual transient.
The unusual waveforms typified by Fig. 9 were often detected and considered a mystery until it wasobserved that pulses similar to those in Fig. 9 occurred when a heavy truck was driven along the road andpassed over the location where the known fault crossed the road. The pulse velocity and attenuation measurements indicated that there was a significant attenuation for any propagating signal. Thus, the similar
peak amplitude and arrival time of the signal at the two separated monitoring locations indicate that there
was a plane or zone of movement along a significant length of the fault line rather than only localised
deformation under the truck. This effect was observed for several positions of the truck indicating that while
the trigger location may be different, the actual fault movement or weakest points of contact on the fault
faces was the same.
The locations of the surface movement varied, and while in some cases adjoining buildings and structures were affected, it was more usual for the surface deformation to be completely random. This was evidenced in a number of cases where subdivision drains were severely damaged (Fig. 1), and houses 10 m
away were free of any damage (Fig. 2). In another case where a row of houses existed, only one home wasseverely damaged.
A series of bore holes were put down to the mine area about 155 m below the surface at locations
where solid pillars of coal were known to exist. Thick continuous sandstone was found with some faulting
and an acquifer was found in one area at a depth of 30 m. At a depth of 155 m, solid black coal was found,S33

and the fragments had bright surfaces. while another nearby borehole indicated broken, oxidised and fragmented pieces of coal.

4. Conclusions
The results of the investigations showed that there were a number of different deformation mechanisms
operating in the monitored region which were mirrored in the variety of AE signals detected
The earlier investigations indicated that a stick-slip type of mechanism could be operating in some circumstances and associated with this mechanism is an avalanche effect where a larger increase of energy waspreceded by a series of lower amplitude events having an increasing frequency of occurrence. This is thought
to be particularly relevant to the fault area.
Any further investigations need to be extended so that there is available a means of calibrating the systems to distinguish between tension/compression at abe source initiation and also data should be available
from a basic laboratory investigation on the behaviour of the typical bedrock material. Artificial signals
need to be injected into the bedrock using a technique such as the 'buffalo gun' used in a borehole so abat abe
signals detected by a transducer array would provide data on elastic wave propagation.

Acknowledgements
The authors wish to acknowledge the assistance and participation of abe Queensland Mines Department
in funding this project, and the co-operation given by the Collingwood Park Anglican Church, whose property was used as a base. The assistance of Hollingsworth Consultants and the valued discussions are appreciated. The support and assistance of the csmo Acoustic Emission Team who provided the necessary backup is acknowledged.

Reference
B.R.A. Wood and R.W. Harris (1984), "Acoustic emission event counting using an Apple microcomputer",
Progress in Acoustic Emission //, eds. K. Yamaguchi et al., JSNDI, Tokyo, pp. 351-358.

834

Acoustic Emission/Microseismic Activity Monitoring of Salt
Crystallization for Stone Conservation
M. Montoto, R.M. Esbert, L.M. Suarez del Rio,
V.G. Ruiz de Argandoiia and C.M. Grossi
The authors are affiliated with University of Oviedo, Dept of Geology, Group of Petrophysics, 33005
Oviedo, Spain.

Abstract
The spontaneous crystallization of salts in the void spaces of building stones creates stresses which canexceed the local rock strength and contribute to mechanical deterioration of the stone mass. This phenomenon constitutes one of the most severe and worldwide deterioration process affecting relevant historicbuildings of the world cultural heritage. Thus, bener knowledge of the stresses inside the stone must be
gained in order to apply appropriate conservation procedures. Future objective of this research is in-situ
AFJMS monitoring of treated and non-treated stones to assess the effectiveness of the applied conservation
techniques.
Stones selected for this study belong to the Murcia Cathedral (Murcia, Spain), which is a GothicBaroque XlV-XV century monument Some of the sculptures, which are composed of high porosity (110 =30%) limestone, are highly deteriorated by salt crystallization. To evaluate this mechanical deterioration
process, episodes of salt crystallization in samples of the above mentioned limestone have been carried out
in the laboratory. The AFJMS was monitored in the 100-300 kHz range. The preliminary results show theeffectiveness of AFJMS to follow the evolution of the internal stresses in the stone during the hydration
process suffered by the Na2S04 crystals.

1. Introduction
Salt crystallization phenomena can be observed on the surface or in the extemallayer of many buildingstones. These salts are observed in the form of efflorescences or whitish crusts which contribute to an ugly
appearance of the original surface of the stone. They are soluble salts formed by a partial solution of thestone and the mortars by circulating water; later they precipitate into the pore space of the stone due tochanges in the thermohydric conditions and water evaporation (Arnold and Zehnder, 1989; Zehnder andArnold, 1989). This deterioration is important in historic buildings and sculptures made of stone and is theresult of a physico-chemical mechanism.
Very often the new salts so formed are hydrated and during their crystallization they stress the walls of
the pore system. Such stresses are large enough to develop microcracks into the stone contributing to its
deterioration. (Fig. 1).
Acoustic emission has been selected as the most appropriate NDT technique to monitor those internal
stresses, as part of the Stone Conservation Program at the University of Oviedo (Spain). To evaluate themechanical deterioration process, episodes of salt crystallization in samples of the Murcia Cathedral limestone have been carried out in the laboratory. The AFJMS was monitored in the 100-300 kHz range. The
preliminary results show the effectiveness of AElMS technique to follow the evolution of the internal
stresses in the stone during the hydration process suffered by the Na2S04 crystals.
The Murcia Cathedral stone (Murcia, Spain) has been selected for the fonowing reasons:• It is made of a calcareous stone with a low degree of cohesion; it is poorly cemented and has an elevated porosity (110=30%). Therefore it tends to disaggregate. Consequently, it is very sensitive tostresses and AElMS signals are easily developed.
835

I

,
3

4

Fig. 1 Sketch of damaging effects due to salt crystallization inside monumental stones. (1) Salt crystallization starts inside the pore space of stone. (2)Local stress fields start to develop associated to themechanical stresses. (3) Microseismic activity(acoustic emission) is generated. (4) Depending on
the local stress and the local strength of the stone,
cracks are fanned and grow.

Fig. 2 Salt crystallization phenomenon developed in
the limestone of the Cathedral of Murcia (Spain).

Fig. 4 Some textural aspects of the Murcia
Cathedral limestone (SEM).

• In the Murcia Cathedral this kind of deterioration is frequently found.
• The Cathedral is placed in downtown Murcia. a city having mild and relatively humid climate (60%
R.H.) with sea-influence as well, implying the presence of sea-salts in the air - mainly chlorines - andatmospheric pollutants (SO:z, C02, dust, smoke, fumes, etc). As a consequence salt crystallization is
one of the most frequent deterioration mechanisms developed in the Cathedral's calcareous stone asshown in Fig. 2 (Esbert et al.• 1990).
2. Petrophysics

Limestone from the Murcia Cathedral has a poorly-cemented detrital texture. Main physical properties
controlling its weatherability are: high open porosity (no= 30%), low density (Pd= 1,813 kglm3), highwater imbibition capacity (C.I.= 11.7 %) and very high capillary suction (C.A.= 1.73 x 10-1 kg/m2s 112)
(Esbert et al.• 1990). The mean grain size is about 150 J.UD. Its uniaxial compressive strength is 3 MFa.

836

Its composition. deduced from the x-ray diffraction analysis. is: calcite (89.5%). quartz (7.5%), dolomite (1%) and accessories (tourmaline. iron oxides and micas) (2%). Fossils (mainly algae, foraminifers andechinoderms). peloids and older rock fragments can be observed in the stone (Figs. 3 and 4). Eventuallyargillaceous minerals (smectite group) are present as well. It is light-colored stone. varying from nearly
white to light yellow (10 YRI2 and 8/3) (Goddard et al.• 1979). and showing a fme grained and quite uniform aspect.

3. Experimental
The deterioration process due to the crystallization of salts into the pore space of building stones. has
been reproduced in laboratory by a well known ageing test: salt crystallization. Different test conditions ofsalt crystallization have been proposed by different authors (Alonso et aI.• 1987); the fmal results being
clearly dependent on the experimental procedure. In our experiment one complete cycle includes: immersion
for 4 hours in an aqueous solution of 14% decahydrated sodium sulfate, renewed each three cycles; drying for
14 hours, at 60°C in an oven and cooling for 6 hours at 18°C and 75% relative humidity (Fig. 5). Elevencycles of the saIt crystallization were performed on cubic specimens having 5 cm edge of the Murcia
Cathedral limestone. The weight loss was 5.5% at the end of the ageing tesL

4. Acoustic Emission Monitoring
DR'l'ING
IIIIIIERSIONTOC If--~, 1---------+.+:----11
100

OV£N

SALT:

:

This technique has not been very often applied
to stone conservation programs; only some papers(Caneva and Maravelli, 1983; Accardo et al.• 1983)
deal with in-situ applications. while others (Fukuda,
1985; Faria Santos and Bieniawski, 1986; Ruiz de
Argandona et aI.• 1986; Calleja et al.• 1986; Esbert
et aI., 1988; Faria Santos and Mrugala. 1989)
considered AElMS monitoring in laboratory tests.
Faria Santos and Mrugala (1989) related the degreeof experimental deterioration to AElMS activities
for a sandstone.
During our experiments the AElMS activities
were continuously monitored. using counts per
second, with a Spartan 3000 instrument (PhysicalAcoustic Corporation, NJ. USA) using a 150 kHz
transducer and 100-300 kHz filter; gain was 80 dB.

ROOM

l1'EJlI'ERA1UlE

~
;

14,.

eo Na, so.

I
I

IOH,O

.
I

eo
40
20

"

8

12

TIME'tolFig. 5 SaIt crystallization cycle for the Murcia
limestone.

TRANSnJCER - -ROCK
SPECIMEN\J

IJ·.-.-

TRANSDUCERr--

I-WAVE-GUIOE..
N0 2 SO •SOLUTION./'ROCKSPECIMEN".

ELECTRIC

OVEN

Fig. 6 Experimental setup of transducers attached to the specimens.

S37

In order to avoid the heating and wetting of the U'ansducer during the tests, it was placed outside the oven(during drying and cooling) and outside the salt solution (during
immersion) and it was connected to the rock surface through a glass waveguide to pick up the AFlMS
signals as shown in Fig. 6. This glass waveguide was attached to the specimen surface using an Ag-glue
interface during the drying and cooling steps, and with vacuum grease during the immersion step. In order toavoid AElMS signals due to the emission of air bubbles, the salt solution reached up to about 1 mrn fromthe upper surface of the cube during immersion.
The AElMS proved again to be useful for monitoring stressed porous rocks. 'When subjected to uniaxial compressive stress, the highly porous (30%) Murcia limestone exhibited immediate AElMS at the very
moment of starting the loading cycle; that is, at very low stresses. This arises from textural rearrangement
inside the rock, followed by a stage of more or less constant AE rate and that of a gradual increase due to theinitiation and growth of microfissures. Then, a fast increment of the AFlMS occurs as failure approaches(Fig. 7); a similar behavior has been observed in an altered granite (Montoto et aI., 1984).

5. Results and Petropbysical Interpretation
During the saIt crystallization cycles, the acoustic emission generated by the specimens mainly appears
during the cooling stage, and minor and irrelevant acoustic emission is monitored in the immersion and drying stages.
A.E.( co""IIII04 )
0

II

10

III

2

4

,

ZO

40

N

'E

z

::I

20

~

o

o

II

2

4

,

w

•

~

£(JldIlIO )

u

~

~--_/

40

~

Fig. 9. Variation of the temperature at the center ofthe specimen during drying.

Fig. 7. AElMS behavior of an altered granite under
uniaxial compressive stresses.
10~

TIME I h)

I

10
~

~

•

i •

~ so

~

.

"Q

..

&

4

z
Ia.i

~ 20

.... 10

I-

0

2

I

4

II

TIME (h).......-.-............,....--rl,.............

I.-,.......-.............,...."T"""'~-

o

2

,

4 • •

Fig. 10 AE rate vs. time in the 4th cycle duringcooling.

TIMElhlFig. 8. Variation of the temperature at the surface ofthe specimen during drying.

838

70

U

!.. 110'"I

10 1--.......- - " ' . . . " , - - - - - - - - - 32,"-C

.'•"

10

0

I

S

4

S

•

0

110

TIME (min)TIME (h)Fig. 11 AE rate vs. time in the 10th cycle duringcooling.

JO

Fig. 12 Variation of the temperature of the specimen during cooling: (l) center, (2) surface.

a. Inunersion
During immersion in the salt solution, the monitored AE/MS is irrelevant, with few AE peaks,
smaller than 10 counts/so From this, it can be deduced that, at this stage, no salt hydration is produced or, atleast, even if some hydration exists, stresses are not high enough to produce AElMS. This hydration seems
to be completed before the immersion in the cooling stage. The few AE signals could be due to other phenomena; e.g., the expansion of clayey minerals due to uptake of water is able to produce local stress concentrations and cannot be neglected.
In some cycles (not included in the present study). in which the specimen and the lower pan of the
waveguide were covered by the solution, a higher AElMS rate was monitored during the first moments ofabsorption of the salt solution. These initial AE registers are due to air ejection from the sample interior, inthe fonn of air bubbles that hit the base of the waveguide. In this case, the curve of total AE counts had asimilar fonn to that of the free absorption of water.
b. Drying
In this stage, in which both water evaporation and salt crystallization are occurring, no relevant AElMS
is recorded. The crystallization pressure of the sodium sulfate is considered to reach insufficient intensity to
produce enough deformation on the rock and no AElMS is generated. Figures 8 and 9 present the variationof the temperature of the surface and center of the specimen when drying (the oven was preheated to 60°C).
The maximum heating rate of the surface reaches about 7°Clmin and that of the center is less than l°Clmin.
The thennal shock on the rock surface is considered to be not intense enough to produce AElMS.
c. Cooling
The more important AElMS is recorded in this stage. In each cycle. the AElMS starts from the very
beginning of the cooling. reaching the maximum about 30 minutes from the start of cooling, and progressively decreasing. After about two hours. the AEJMS is almost nil. although some isolated peaks can bemonitored (Figs. 10 and 11). The shape of the AE rate curve can be explained by the dehydration of the
Na2S04'10H20 (Duval, 1963) and the variation of the temperature in the specimen during cooling. Whenthe decahydrated sodium sulfate is heated, the dehydration is not completed until 90°C. At noc the main
composition is Na2S04·4.75H20, and below 32.4°C it is completely hydrated as Na2S04·10H20. Thus,
when cooling the specimen from 60°C. the partially hydrated sodium sulfate progressively reacts with environmental water. increasing its volume and stressing the rock.
The maximum value of the AE rate (at about 30 minutes from the start of cooling) coincides with thetime, when the temperature at the geometrical center of the specimen reaches 32.4°C (Fig. 12), the moment
at which the salt completes its maximum hydration. The AE events registered after the maximum value areinterpreted as textural modifications due to the "crack equilibrium configuration" not previously reached.
Similar results have been obtained in the heating tests of a granodiorite (Ruiz de Argandona et al., 1986).
After two hours. the temperatures inside and outside the specimen become equal and the AEJMS ceases or isvery low.
When the AE rates of the consecutive cycles are compared. the AElMS increased from the first to thethird cycles: in the following cycles, the AE rate decreased (Fig. 13). Such a behavior shows that the great

539

Z

10,...---------------,
~
I!

..

..
IOi

•

I

"l
! 0

III
g

~

~ .1

>

~'l

W

-i

~

.....

o

I

I

4

e
TIME (hIFig. 13 Cumulative AElMS plot for different aging cycles during cooling.

.,
0

I

S

4

IS

•

• •
CYCLE NUMBER

Fig. 14 Weight variation during eight salt crystallization cycles.

est damage is produced in the flISt few cycles and the stresses generated in the specimens decreases beyond
the 3rd cycle. This behavior of the AElMS seems to be contradictory to the weight variation of the specimen during the cycling. The specimen loses more material after the Sth cycle (Fig. 14). From the point ofview of stone deterioration, it is interpreted that a higher concentration of stresses and associated microcracking occur during the flISt few cycles. The later disaggregation stage proceeds with a lower energy consumption.

6. Conclusions
The monitoring of the acoustic emissionlmicroseismic activity has proved to be a valuable technique inStone Conservation, to evaluate the damaging stresses developed inside a monumental stone (Murcia
Cathedral limestone) when subjected to salt crystallization. For this stone, the most important damage is
produced during the cooling stages of the flISts cycles, due to the hydration of the salts; that hydration begins at the very moment in which cooling starts. Acoustic emission is produced when the partially hydrated
sodium sulfate progressively takes up environmental water, increasing its volume and stressing the rock.
No significant AElMS is detected during the drying and immersion stages.

Acknowledgements
The authors wish to acknowledge: "Consejeria de Cultura" (Comunidad Autonoma de Murcia, Spain)and "Comision Interministerial de Ciencia y Tecnologia. (Cl.C.Y.T)u. Spain. Project no. 485/88, for supporting this research.

References
G. Accardo, C. Caneva. and S. Massa (1983), "Stress monitoring by temperature mapping and acoustic
emission analysis: A case study of Marcus Aurelius", Studies in Conservation, 28, 67-74.
FJ. Alonso, J. Ordaz, L. Valde6n and RoM. Esben (1987), "Revision critica del ensayo de cristalizaci6n desales". Materiales de Construcci6n, 37, 206,53-60.
A. Arnold and K. Zehnder (1989), "Salt weathering on monuments". 1st L'Int. Simp. La conservazione deimonumenti nel bacino del Mediterraneo (Bari), eel. F. Zezza, pp. 31-58.

S40

L. Calleja, V.G. Ruiz de Argandofta, LM. Su4rez del Rio, and M. Montoto (1986), "Resistencia a la tracci6n en rocas sometidas a ciclos rermicos", BoL Gool. y Minero, 97(2), 84-93.
C. Caneva and M. Maravelli (1983), "L'emissione acustica per la diagnostica negli interventi di conservatione: iI ponaIe di S. Zeno a Verona", First Int Conf. on NDT in Conservation of Works of Art (Roma),IV/6.1-6.22.
C. Duval (1963), Inorganic Thermo-gravimetric Analysis, Second and Revised Edition. Elsevier Pub!.,
Amsterdam, 722 p.
R.M. Esbert, C. Grossi, L.M. Su4rez del Rio, L. Calleja, J. Ordaz and M. Montoto (1988), "Acoustic
emission generated in treated stones during loading", Proc. VIth Int. Congo on Deterioration andConservation ofStone, Toron (poland), pp. 403-410.
RM. Esbert, C. Grossi, L. Valde6n, J. Ordaz, FJ. Alonso and R.M. Marcos (1990), "Estudios sobre laconservaci6n de la piedra de la Catedral de Murcia", Materiales de Consttucci6n, 40, 217, 14p.
C.A.F. Faria Santos and Z.T. Bieniawski (1986), "Effect of artificial weathering on properties of
Loyalhanna sandstone", 27th U.S. Symposium Rock Mech., (Alabama), pp. 147-154.
C.A.F. Faria Santos and M. Mrugala (1989), "Variations of AElMS activity with weathering in Loyalhana
Sandstone", Proc. 4th Conf. on Acoustic Emission/Microseismic Activity in Geologic Structures andMaterials, Trans Tech Publications, Clausthal, Germany, pp. 15-25.
M. Fukuda (1985), "Acoustic emissions from porous rock while freezing. Conservation and Restoration ofStone Monuments", Scientific and Technical Study on the Conservation and Restoration of Monuments
made of Stone or Related Materials, pp. 19·22.
E.N. Goddard, PD. Trask, R.K. De Ford, O.N. Rove, IT. Singewald and RM. Querbeck (1979), "RockColor Chart", The Geological Society of America, Boulder, CO. USA.
M. Montoto, L.M . Su4rez del Rio, A.W. Khair and H.R. Hardy (1984), "AE in uniaxially loaded granitic
rocks in relation to their petrographic character", Proc. 3rd. Conf. on AEIMS in Geologic Structures andMaterials, Trans Tech Pub!., Clausthal, Germany, pp. 83-100.
V.G. Ruiz de Argandofta, L. Calleja, L.M. SuArez del Rio and M. Montcto (1986), "Emisi6n acus·
ticalactividad rnicrosfsrnica generada bajo ciclos termicos en una roca granitica", Bol. Goo!. y Minero,97(1), 96-102.
K. Zehnder and A. Arnold (1989), "Crystal growth in salt efflorescence", J. Crystal Growth, 97, 513·521.

541

Acoustic Emission Monitoring during In-situ Heater Test ofGraniteT. Ishida, K. Kitano, N. Kinoshita and N. Wakabayashi
T. Ishida is affiliated with Department of Civil Engineering, Yamaguchi University, 2557 Tokiwadai, Ube,
Yamaguchi 755, Japan, K. Kitano is with Central Research Institute of Electric Power Industry, 1646 Abiko,
Abiko, Chiba 270-11, Japan, and N. Kinoshita and N. Wakabayashi are with Shimizu Corporation, 3-4-17
Ecchugima, Kohtoh, Tokyo 135, Japan.

Abstract
In order to clarify the mechanism of rnicrocracking due to thermal attack in inhomogeneous granite,
acoustic emission (AE) events were monitored during the small scale in situ heater test in an old quany of
Inada granite. A 2 m long electric heater was set at the depth from 3.3 m to 5.3 m in a 100 mm diameterbored hole, and the temperature of the heater was increased up to 800°C. AE events were monitored for 60
hrs with 12 sensors buried in 4 holes bored I m from the heater hole. 636 AE hypocenters were located.
From the result, it was found that AE events started to occur in the pegmatite veins intruded into granite,
and then these occurred in the pre-existing joints. These AE clusterings could result from the fact that thepegmatite veins have particles larger than those of the surrounding normal granite and the pre-existing
joints have the lower frictional resistance than the surrounding normal granite.

1. Introduction
Acoustic emission (AE) monitoring is an effective method to clarify the mechanism of microcracking
due to thermal auaek in rock, because it can monitor at locations away from a heated region where conventional measurements are not viable. Recently, Ohtsu and Ono (1986) and Ohtsu (1988) proposed a source
inversion procedure for determining crack types and crack orientations as the quantitative waveform analysis
of AE based on the integral representation and the moment tensor. Moreover, Ohtsu et al. (1989) successfully applied the procedure to AE events monitored during the pull-out tests of hooked anchor-bolts from aconcrete block. In light of the development, AE monitoring method has become worthy of investigating the
microcracking mechanism of heated rock.
Majer et al. (1981) and Majer and McEvilly (1985) carried out large scale in situ AE monitoring with
the increase of temperature in quartz monzonite, and compared the location and activity of AE with the temperature change in rock. To our best knowledge, however, no researcher has found the relation between thepositions of clustered AE sources and the inhomogeneity of the rock including intruded veins and pre-existing joints. Thus, the mechanism of microcracking due to thermal attack in inhomogenious granite has notbeen clarified yet.
We monitored AE during a small scale in situ heater test in an old quarry ofInada granite. In this paper,changes in the distribution of located AE sources as the temperature increases are compared with the positions of veins and pre-existing joints. The mechanism of microcracking due to thermal attack in inhomogenious granite is discussed below.

2. In-situ Experiment
2.1 General View ofSite and Experiment
The site of the experiment was on a small hill covered with 40 - 50 em thick surface soil containing
pieces of granite, as shown in Fig. I. A few joints in granitic rock mass were observed at outcrops along
542

5

76

4

0

3

5

10m

====::iI'- _.'

1=1

21

o -2

-7

-6

-5

-4

Y
AE3

AE2
0

0
TH6

TH5

1'H3 TH 1

TH4

o
I

0.5
:

1m
!

0
AE4

H

/

X

1'1-120
AEI

Fig. 1 Topography and locations of bored holes at the site.
the brook, about 10 m apart and 4 - 5 m down from the site. At the site. one hole (H) of 100 mm diameterand 5.65 m depth was bored to heat the rock. Four holes CAE1 - AE4) were bored surrounding the heaterhole (H) at about 1 m distance to install AE sensors. These holes had 66 mm diameter and about 6.8 m
depth. To measure temperature change in rock due to heating, six holes (TII 1 - TII6) were bored for thermocouples along the perpendicular bisector of the line segment between the positions of AE3 and AE4 holes.
These six holes of 32 mm in diameter and about 6.8 m in depth are 20. 40, 60, 100, 200 and 300 em apart,respectively, from the heater hole as shown in Fig. 1.
Figure 2 shows the spatial arrangement of the heater and AE sensors. The heater is composed of six Ushaped electric pipe heaters covered with a stainless steel pipe for protection. The diameter. total length. and

843

y

z

total maximum elecbic capacity of the heater are 90mm, 2.0 m and 20 kW, respectively. The heater
was embedded at the depth from 3.30 to 5.30 m
below the surface in relatively homogeneous rock.
The under-ground water level in the heater hole atthe time of the experiment was 5.45 m below thesurface. Twelve AE sensprs were arranged in thefour AE holes from 2.40 to 5.84 m below thesurface, so as to surround the heated region threedimensionally.

AE4
AI': Sl!1I!\OI'
Z=-3.3healer
Z=-4.311

2.2 AE Monitoring Method

AE sensor is composed of a piezoelectricelement and a brass case. The piezoelecbic element
has a disk shape of 10 mm in thickness and 30 mm
in diameter, and is polarized in thickness direction.
Its resonance frequency is 67 kHz. This element is
attached to the bouom of the brass case, which isfilled with silicone rubber for waterproofing. The
outer diameter and height of the case are 40 mm and
34 mm, respectively. A pre-amplifier of 40 dB gainis also put into a brass case, and made waterproof as

Zo-5.:J
Zo-5.812

Fig. 2 Locations of the heater and AE sensors.

0-[> [>
AE

pre·amp

sensor(40dB)

signal-conditioner(40dBIAD

H

disk unit·converter(lJlSx 2048word)

Fig. 3 Block diagram of the AE measuring system.
the AE sensor. The AE sensors and the pre-amplifier were flXed in wooden frames to coordinate them indesired positions of the bored holes and to direct the sensitive direction of each AE sensor normal to the axisof the heater hole. These wooden frames were buried in the holes with cement paste.
Figure 3 shows a block diagram of AE monitoring system. The system is composed of an AE sensor,
a pre-amplifier, a signal conditioner, an NO converter, and a disc unit It can be equipped with twelve AE
sensors at the same time. In this experiment., AE signal received at each AE sensor was amplified 80 dB (40dB in the pre-amplifier and 40 dB in the signal conditioner). The AID converter is of 8-bit resolution in theamplitude and of 2,048-word record length for each channel. The sampling time was one J.lS and the prebigger point was set to be a half of the total record length. AE signals detected at No.2 AE sensor, buriedin AEl hole, were chosen for the trigger of the AID converter. Every time the AID converter was triggered,twelve traces of AE signals received at twelve sensors were displayed. H P-wave arrival times were readable
at more than five traces out of twelve, me data of full waveforms were transferred via GP-IB to the disc unit
and stored on a floppy disc. It took about 6 s for the AID converter to display the traces, about 18 s totransfer and store the data onto a floppy disc. Therefore, the undetectable duration corresponded to about 24 s
if the data was recognized and stored onto the floppy disc, and about 6 s if the data was not stored. Because
memory buffer of the floppy disc was 710 kbyte, 14 AE events (each event had 12 wave traces) could be
stored in one floppy disc. Four floppy discs can be mounted in the disc unit and continuous monitoring was
possible by replacing a ftlled disc. The frequency range of this AE monitoring system was S to 100kHz.
844

50

2
.....

180

900

160

800

140

700

40
120 ;..;

~

~

:-

~

600 ~

-

500

..

80 .:i

400

«u

60

.::

300

!!:

100 I

i=

ii> 30
,
<
"E 20
(J

=

-=

'5

40

~§ 10

iii

"'~"

«

~

~

~

=t

200

§

.,
"'=
"
;;

..

~

:::=

::

20
TH5-4 and TH6-4

0

U

0

10

20

m

VI

40

30

0

VB

50

~

100
0

60

elapsed time lh)

Fig. 4 Count rate of located AE events and temperatures of the rock and the heater at 4.3 m below thesurface, the center of the heating range, as a function of elapsed time.

3. Results
3.1 AE Rate and Heater Temperature
Figure 4 shows AE count rate and temperatures of the heater and rock with time elapsed. The horizontal
axis indicates hrs elapsed from the start of heating. The vertical axis indicates numbers of located AE events
per hr and the temperatures measured on the heater and in the holes from TIll through TIl6 at 4.3 m below
the surface. The scale for the heater temperature labelled by H3 is five times larger than those of rock temperatures, such as TIll-7, TH2-7 and so on. Roman numerals shown in the bottom of the graph indicate
results of the source location mentioned in the next section.
The heater temperature was controlled by the following steps. The temperature was raised to 180°C
over 9 hrs from the start of heating. However, AE activity was very low and the temperature was raised to
700°C over the next 13 hrs, and kept for the subsequent 8 hrs. Even at the end of this stage, AE did notoccur actively. The temperature was then raised to 800°C (the heating limit of the heater) in 2-112 hrs andkept for the subsequent 27-1/2 hrs.
AE started to occur after 11 hrs of heating, when the heater temperature reached about 250°C. The
maximum AE count rate per hr was recorded from 33 to 34 hrs, just after the heater temperature reached
800°C. After this time, AE activity decreased step by step, showing periodical increases and decreases of AE
count rate.
3.2 Distribution ofLocated AE Sources
Figure 5 shows the distributions of located AE sources projected to the plain view (x-y plane) and twoelevation views (x-z plane and y-z plane) for each period from No. I through VII indicated in the bottom of
Fig. 4. In the right side of the figure, the geological section-views of the two elevation views are shown,determined from the observation of the cores taken from the heater hole and the four AE holes and from the

845

,

AE3

o

o .\,1'

AE4

01
B

I

o

o

~

~~

0

-3

•

g

~

o

o

AEI

0 0
0

.

GeologIcal

~

o

o

Sectionr:-il @~·'~t. pegmalile_._.- <D<%lC3ltl>

I

pre·exisllni ioinl

o

!(m

-4

0

:qll~~-~

I

I

-5 •

-3 - .'/Z-plane

!(mll

..

n

I

;,- ~.

..

-

1.11:00-16:3(1(661"".

o· m::':,.~~. jl~·l·o~,?: 0::

-4 ~

AEI

1

oIII

AE3
AE4

00«
oj

•

'I'

-

:JT jV II

· :.

I

rI In

]

if ·
o:~

r100 ::0

;

8

~

0

1"

II@

o

II .16:30-21:47(42) 1m .21:47-30:()()(10ti11 rl.30:OO-3S:00U21l1 V.3S:00-40:()()(10) 1W.40:00-50:OO1I39)1 \11I.50:00-60:00(921Fig. 5 AE hypocenters located in each period from I to VII and the geological view. projected to the planeview (x-y plane) and the two elevation views (x-z plane and y-z plane). In the bottom of each figure. theelapsed time and the numbers of located hypocenters are indicated.

Photo 1 Core sample of the heater hole recovered from 3.0 to 5.65 m below the surface.
observation of the wall of the heater hole using a borehole television. The numerals in the bottom part of
the figure indicate the elapsed time and the number of located AE events for each period. In the left side of
the projection on the x-z plane for the periods from No. ill through VII, 6QOC temperature contour line wasindicated. derived from the temperatures measured in the rock at the end ofeach period, which corresponds to
30. 35.40.50 and 60 hrs from the start of heating, respectively. The 60°C is the temperature at which AE
starts to occur in granite. reported by Johnson et at. (1978). Yong and Wang (1980) and Eharaet at. (1983.
1986) in their laboratory experiments. It is noted that all AE sources are located inside the contour lines; in846

other words, all AE occurred in the region whose temperature exceeds 60°C. It implies the results are
consistent with the previous fmdings.

4. Discussion
In this experiment, AE started to occur at 11 hrs after the start of heating. At the period No. I (from 11
to 16-112 hrs), when AE started to occur, the located AE sources clustered around the heater hole at 4.8 m
below the surface, which is the lower part of the heater. In the next period, No. n, they tended to cluster inthe rock at 3.2 m below the surface near the upper end of the heater. Comparing to the geological sectionviews in the right side of Fig. 5, it is found that the clustered region at the period No. I corresponds to thelocation of the pegmatite vein (b) at 4.8 m below the surface, and that of the period No. n corresponds tomainly the location of the pegmatite vein (a) at 3.2 m.
Quartz and feldspar are main mineral compositions of granitic rock, but the coefficient of liner thermal
expansion of quartz is from 1.5 to 4 times larger than that of feldspar (Skinner, 1966). As a result, with theincrease of temperature, microcracks accompanying AE occur between quartz and feldspar. This is the reason
why AE occurs in granitic rock with the increase of temperature.
Photo 1 shows a core sample drilled from lite heater hole. The two white portions, (a) and (b), taken at
3.2 m and at 4.8 m below the surface, are the pegmatite veins. Although the pegmatite has the samemineral compositions as the surrounding normal granite, lite particles are larger than those of the granite.
When AE events were caused by microcracking between quartz and feldspar due to the increase of
temperature. it is expected that the AE events are predominantly generated in a portion composed of large
particles, or pegmatite. Among the pegmatite veins, AE events were observed in the pegmatite vein (b)earlier than in (a). This could be explained by the fact that the pegmatite vein (b) was closer to the center ofthe heater than that of the vein (a), and the temperature of the vein (b) increased earlier causing AE events.
In the period No. III, AE events clustered in the the heater hole from 3.50 to 4.25 m (the center was at
3.90 m), and in the period No. IV, they clustered from 4.20 to 4.60 m (the center was at 4.40 m). In the
period Nos. V to vn, clusters were observed in the same region as the period No. IV. In Photo I, we can
fmd four horizontal pre-existing joints indicated by (1) through (4), three of which are located from 3.55 m
to 3.75 m and one is at 4.0 m. Two inclined pre-existing joints are indicated by (5) and (6) at 4.3 and 4.4 m
and their dip angles are both about 30'. Comparing the distribution of located AE sources to the geologicalsection view in Fig. 5, the locations of the joints from (1) through (4) correspond to the range from 3.35 to
4.25 m where the AE events clustered in the period No. m, and the locations of the joints (5) and (6)correspond to the range from 4.20 to 4.60 m where the AE events clustered in the period numbers IV to
vn. These imply that AE events occurred along the pre-existing joint planes or very close to them,following the pegmatite veins. Thus, the mechanism of AE clustering along the pre-existing joint planes is
as follow: Thermal stresses are released at the pre-existing joints, which have relatively low frictional
resistance. This stress release causes microcracks, accompanying AE events in the rock near the pre-existing
joints. In addition, pressurized water vapor emitted from the heated rock penetrates the pre-existing joints
and further causes the microcracks accompanying AE events.

5. Conclusions(1) With the increase of rock temperature. AE started to occur in the pegmatite veins intruded into granite.

Although the pegmatite has the same mineral compositions as the surrounding normal granite, theparticles are larger than those of the granite. Main mineral compositions of granitic rock are quartz andfeldspar. The coefficient of liner thermal expansion of quartz is from 1.5 to 4 times larger than that offeldspar. In the case that AE events were caused by microcracking between quartz and feldspar due to theincrease of temperature, it is expected that AE events are predominantly generated in a portion
composed of large particles. Therefore, it is reasonable that AE events began in the pegmatite. because847the particles of the pegmatite are much larger than those of the granite.
(2) AE events occurred along the pre-existing joint planes or very close to them in Ihe surrounding nonnal
granite only after they occurred in the pegmatite veins. The mechanism of AE clustering along the preexisting joint planes is predicted as follow: Thermal stresses are released at the pre-existing joints,
which have relatively low frictional resistance, and the stress release causes microcracks accompanying
AE events in Ihe rock near the pre-existing joints. In addition, pressurized water vapor emitted from theheated rock penetrates the pre-existing joints and further cause microcracks accompanying AE events.
Acknowledgements

We would like to thank Messrs. T. Kanagawa and S. Sasaki of Central Research Institute of ElectricPower Industry for their useful discussion throughout this investigation. We also thank Dr. Y. Kanaori ofGifu University for providing valuable comments on microfracturing mechanism of granite.

References
S. Ehara, M. Terada. and T. Yanagidani (1983), "Thermal properties of stressed rocks", Proc. 5th lot. Congoon Rock Mech., pp. EI37-EI40.
S. Ehara, T. Yanagidani and M. Terada (1986), "AE and thennal expantion of Oshima granite during slow
cyclic temperature change between 210 K and 370 K", Journal of the Society of Materials Science Japan,
35, 490-495. (in Japanese)
B. Johnson, A. F. Gangi, and J. Hamlin (1978), "Thermal cracking of rock subjected to slow, uniform
temperature changes", Prot. 19th U.S. Symp.on Rock Mech., No. I, pp. 259-267.
E.L. Majer, T.V. McEvilly and M.S. King (1981), Monitoring an underground repository with modem
seismological methods", Int. J. Rock Mech. Min. Sci. & Geomech. Abstr., 18,517-527.
EL. Majer and T.V. McEvilly (1985), "Acoustic emission and wave propagation monitoring at the spent
fuel test: Climax, Nevada", Int. J. Rock Mech. Min. Sci. & Geomech. Abstr., 22, 215-226.
M. Ohtsu and K. Ono (1986), "The generalized theory and source representations of acoustic emission", J.
Acoustic Emission, 5(4), 124-133.
M. Ohtsu (1988), "Source inversion of acoustic emission waveform", Proc. of Japan Society of Civil
Engineers, No. 398/1-10, pp. 71-79.
M. Ohtsu, M. Shigeishi and H. Iwase (1989), "AE observation in the pull-out process of shallow hookanchors", Prot. of Japan Society of Civil Engineerings, No. 408N-ll, pp. 177-186.
BJ. Skinner (1966), "Thermal expansion", in Handbook of Physical Constants, edited by S. P. Clark, Jr.,

Geological Society of America Memoir 97, p. 75.
C. Yong and C. Wang (1980), "Thermally induced acoustic emission in Westerly granite", GeophysicalResearch Letters, 7, 1089-1092.

848

Using Acoustic Emission Testing in Seepage Investigations

Andrew R. Blystra
The authors are affiliated with A. R. Blystra and Associates, Ltd., 1481 Ring Road, Calumet City, IL
60409, USA.
Abstract
Using acoustic emission (AE) testing, we can locate the source or exit point of seepage through a dam,its foundation, or its abutments. This procedure can also be used to detennine if seepage or groundwaterflow is transporting soil particles. The typical AE test system used in seepage investigations consists of asensor, an amplifier and a signal processor. The cable connecting the sensor is sealed to pennit underwater
usages. To obtain baseline data, laboratory testing was done to measure counts for clear flowing water andfor flowing water which was transporting soil particles. A triaxial test cell was used to study flow at various velocities. Field calibration studies were made at several hydropower projects where seepage could beobserved and measured. These field studies included several boils where upward groundwater flow was transporting soil particles. Based on laboratory testing and field calibration studies, it was found that 300 counts
per minute represents a threshold indicating seepage. Seepage investigations have been conducted at several
dams using AE testing. This method was successful in locating seepage entrance points in both earthen
embankments and concrete structures. Testing also has been conducted in boreholes in earthen embankments
and foundations where high seepage flows were suspected of transporting soil particles. Additional researchis being conducted to provide a larger database for interpreting AE test results in seepage studies.
1. Introduction
Locating where water is seeping through a dam, its foundation, or its abutments has always been a difficult task for engineers and geologists. Once the source and the path have been identified, the seepage canbe intercepted and stopped. Use of acoustic emission (AE) testing has proven to be a reliable technique for
identifying the source of seepage and for evaluating whether the flow was transporting soil particles. The
application of AE testing in seepage investigations is based on an understanding of the AE source and theinterpretation of the data obtained in the field. Laboratory testing was conducted to provide baseline data forinterpreting field information.
2. Theory and Technique
To apply AE monitoring in seepage studies, it is necessary to understand the AE source. A variety of
AE sources generate a stress wave that has distinct properties. A few examples include a flowing fluid, theyielding of a material such as steel, large strains occurring in soils, and negative pressure or cavitation. The
AE source in seepage is generated at the boundary layer between the flow and the concrete and soil. AnotherAE source can be the impacting of soil particles on the sensor if the flow is causing a piping condition
whereby soil is being eroded.
A boundary layer exists in the zone of the flow in the immediate vicinity of a solid surface or boundaryin which the motion of the fluid is affected by the frictional resistance of the boundary on the fluid. The
boundary may be a solid such as concrete or it may be a soil such as gravel, sand, silt or clay. It has beenshown that a no-slip condition exists between the fluid and the boundary at the contact surface. This requires849

Fig. 1Development of a laminar boundary layer downstream from the leading edge ofa flat plate held parallel to a uniform flow of fluid (Middleton and Southard. 1984).
that the fluid velocity and the boundary velocity be the same at the contaCL Therefore. the velocity is zero atthe boundary contact. The boundary layer is the region of fluid next to the boundary across which the velocity of the fluid grades from zero at the boundary to that of the unaffected flow some distance away from theboundary.
As an example of a boundary layer that develops on the surface of a stationary flat plate is shown in
Fig. I (Middleton and Southard. 1984). The plate in this figure is positioned in a uniformly flowing fluid.
Inunediately downstream from the leading edge of the place. the boundary layer is thin and the shearing required by the transition from zero velocity to free-stream velocity is compressed into a very thin zone ofstrong shearing action. Near the leading edge the shear stress at the surface of the plate is relatively large.
Farther downstream along the plate. the boundary later is thicker. the shearing action is weaker. and theshear stresses at the surface of the plate is smaller. Placing the AE probe in a flowing fluid such as water isanalogous to the plate in the example. However. in the case of the probe. the boundary layer not only is being developed along the probe. but it also is being developed along a concrete surface or a soil surface.
The boundary shear stress along the boundary layer provides the AE source for a flowing fluid. If thefluid. in this case water. is transporting particles. another AE source is presenL The boundary shear stress isthe shear stress which acts along the boundary and has a distribution as shown in Fig. 2. This figure also
shows the distribution of the velocity from the boundary to the point where the velocity becomes the freestream velocity.

Fig. 2 Distribution of velocity and shear stress. in steady. uniform laminar flow along an inclined plane.
The basic resistance equation for steady. uniform flow along a boundary is:'to == g . RH . sin ~(1)
where 'to is the shear stress along the boundary. g is the weight per unit volume of the fluid. RH is the hydraulic radius and ~ is the angle of inclination of the channel slope. Equation (1) is applicable to not only
steady. uniform flows and to flows that are steady and uniform in a time-average sense. but also to flowsssothat show turbulent fluctuations in velocity. A shear
stress also is developed as turbulent fluid flow occurs
along a boundary. The resulting velocity pronIe forturbulent flow is different from the proflle for laminarflow as shown in Figs. 3 and 4; however. both
velocities cause a shear Sb'ess to develop, thereby
providing an AE source.
Fig. 3 Comparison of laminar and turbulent
velocity profiles.
Transition
Zoneu

TUrbulent
Boundary Layer

LaminarBoundary Layer ~

~
~
-~~-~
~
~

I--x
Fig. 4 Transition from laminar flow to turbulent flow in the boundary layer along a flat plate (Middleton
and Southard, 1984).

At the location where water is seeping through an earth or concrete dam, lhe flow may be either laminar, turbulent, or a combination of the two. Regardless of the type of flow, shear sb'ess along the flowboundary is occurring, and this stress provides an AE source. Measurements along the flow boundary with
the AE sensor provides the means of locating and identifying the seepage problem. The flow boundary may
result from either the sensor itself, which is analogous to placing a plate in the flow, or from the surface ofconcrete or soil.
To obtain as accurate record as possible of AE activity associated with seepage, other noise must be filtered out. This is especially important since the extent and severity of the seepage problem are related to thenumber of AE counts per unit of time. In general, the higher the counts, the more severe the seepage problem. At hydropower projects, several noise sources are present including those caused by the generating
equipment and vehicular ttaffic. Since each type of noise source and associated Sb'eSS wave has its own distinct properties, the various typeS can be differentiated and unwanted waves can be filtered oul
3. Equipment
The typical AE test system used in seepage investigations is composed of three elements: a sensor todetect the AE, an amplifier to boost the signal energy for b'ansmission, and a processor to quantify the signal. All three elements are interconnected by a shielded cable. The connection between the sensor and thecable is sealed so that the sensor can be used underwater. Figure S shows the basic pieces of equipment thatwere used in these investigations.
The sensor can detect AE signals of about 30 kHz. This is the frequency found to be associated with theshear sttess at the boundary layer. The amplifier boosts the signal, and the processor provides a digital readout of the AE counts. A single channel system was used, since only one location along the boundary was
being studied at a time. The length of cable varies depending on the depth of water at a particular site. The
longest cable used between the sensor and the amplifier was approximately 3S m. If a longeTcable is re551

SEAl.£D COtfNECTlON

d

/CABU

1

~

-L-----<-SENSOR'-----J

Acousnc EMISSION AMPUFIER

ACOUSTIC ENlSSIONPROCESSOR

Fig. 5 The basic components used in the AE system for seepage investigations.
quired, the amplifier would have to be connected directly to the sensor to prevent the loss of a significant
amount of signal.
4. Field and Laboratory Testing
To date over 100 field calibration studies have been made at approximately 20 locations where seepage
could be observed and measured. AE measurements have been made on both the upsb'eam and downstreamsides of structures. at locations where water was flowing out of joints and fractures in rock formations. andat places where groundwater was flowing out of the ground. AE counts have been measured at active boils
where seepage from a reservoir is flowing upward, exiting the ground, and transporting soil particles. Flowvelocities at the boil locations were measured and then AE testing was conducted to obtain counts. For
small boils having flow velocities of approximately 0.3 mis, readings ranged from 500 to 900 counts/min.
Very active boils having flow velocities of approximately 1 mls yielded 3,000 to 12,000 counts/min. It
was determined that the seepage velocity affected the number of counts. However, the amount of sedimentbeing transported by the flow also affected the number of counts.
In laboratory studies, AE counts have been measured for clear flowing water and for flowing water
which is transporting soil particles. For both cases, the flow rate was varied from 0.1 to 0.3 mls. Theseflow studies were conducted in a triaxial test cell so that velocity could be controlled and visual ohservations
could be made. Testing was done with the same equipment used in the field studies. The sensor was placedin the flow similar to the plate described in Middleton and Southard (1984).
After background noises were filtered in the laboratory, it was found that counts ranged from approximately 300 per minute for clear water flow at a rate of 0.1 rnls to over 10,000 counts/min for high velocity
flow transporting soil particles. The transport of soil particles increases the count because soil particles impact against the sensor. The shear stress along the boundary also contributes significantly to the number ofcounts. The higher the veloity, the higher the shear stress along the boundary. The greater the shear stress,
the higher the AE activities.
Based on the results of field calibration studies and laboratory testingg. it appears that about 300
counts/min represents a threshold value indicating seepdge. This value represents data obtained after background noise had been reduced or eliminated. Laboratory testing and field investigations are continuing sothat additional baseline data can be used to correlate and quantify the results from individual sites.
S. Seepage Investigations
Seepage investigations have been conducted at several existing hydropower dams in the midwestern
United States. The studies conducted at three projects are described to illustrate the technique and the results.
ProjeclOne
At the first project. it was necessary to prevent the fluctuation of the phreatic surface, or free water surface in the embankment. The upstream slope protection of the embankment consisted of a sloping concreteslab. This slab extended downward about 10 m where it joined vertical concrete core wall. Piezometer readings in the embankment indicated that water was seeping through this joint at various locations. resulting
in higher than normal phreatic surface. The net effect of this condition was a reduction in the stability of the
552

500

200

100

o

'"+o

.. +"' '"6

o

o

o+

o

o

o

'"c!i
STATIONS

Fig. 6 Acoustic emission test results from Project One.

. ...

~SlEELSHEET PILING

Fig. 7 Cross section of the spillway at Project Two.

S53

~.

embankment. As a first step in determining where seepage was occurring through this joint, AE testing was
conducted along the entire length of the joint. Figure 6 shows some of the test results. The high count areas
along the survey line were interpreted as probable seepage locations. Once these locations had been identified, electrical resistivity and salt tracing was used to confllJ11 the seepage. The next step was to seal the
joint where it was open and to eliminate the seepage.
Project Two
During the installation of piezometers to measure the uplift. beneath the spillway at this project. it was
discovered that water was flowing beneath the concrete. Additionally, voids were found beneath the spillway
ranging from 4 to 10 cm. It was necessary to identify the seepage path so that the flow could be intercepted
and the piping potential reduced. It was apparent that the seepage beneath the spillway had transported soil
resulting in the voids. The cross section of the spillway at this project is shown in Fig. 7. The upstream
and downstream sheet pile cutoff walls should have prevented flow beneath the structure. It was suspected
and later confirmed using AE testing that the sheet piling on the upstream side was not completely embedded in the concrete. Acustic emission counts were obtained along the entire length of the upstream along
downstream sides of the spillway. The results showed high count areas. An underwater inspected later confmned that the piling was not embedded along the entire upstream length and flow was coming beneath thestructure at that location.
Project Three
At the third project, very loose. saturated soil was encountered in a boring adjacent to the powerhouse.
During a foundation exploration program. the drill rods dropped approximately 3 m under their own weight.
The loose, saturated soil was in an area where seepage had been observed for many years. Since no soil wasrecovered in the sampling spoon at this location, it was suspected that piping was occurring. This happens
when water flowing through the embankment picks up soil particles and transports them. resulting in thedevelopment of a void. The AE sensor was lowered into the drill hole. Testing was conducted in the veryloose zone where seepage and piping was suspected. In all tests, the counts were relatively low, much lower
than the counts obtained during the field tests of boils. AE testing confU'med that seepage occurring, butthat there was no active piping.
6. Conclusions

Acoustic emission testing can be used successfully in seepage investigations. especially to locate theorigin of a seepage path and identify piping. Additional laboratory testing and field verification are needed toprovide a greater database for interpreting results. To be accurate. AE investigations for seepage must eliminate as much background noise as possible. AE testing should be used in conjunction with other methods
such as electrical resistivity. salt tracing. and ground peneuating radar to confU'm results.
References
Gerard V. Middleton and John B. Southard (1984), "Mechanics of Sediment Movement". Lecture Notes for
Short Course No.3, Eastern Section of the Society of Economic Paleontologsists and Mineralogists,
Providence, Rhode Island, March.
American Society for Testing and Materials (1972). Acoustic Emission. S1P 505, eds. R.G. Lipati et aI.•
Philadelphia.
American Society for Testing and Materias (1975), Monitoring Structural Integrity by Acoustic Emission.
STP 571, ed. J.e. Spanner and J W. McElroy, Philadelphia.

854

Acoustic Emission of Penetration Experiments to JUdge Soil
Conditions. Naemura, M. Tanaka, S. Nishikawa, M. Nakamura,
K. Jo and T. Kishishitas. Naemura and M. Tanaka are affiliated with Public Works Research Institute, Ministry of Construction, S.
Nishikawa is with Chugoku Region Construction Bureau, Ministry of Construction, M. Nakamura, K. Jo and T.
Kishishita are with Technical Research Institute, Fujita Corporation, Ohtana 74, Kohoku, Yokohama 223, Japan.
Abstract
This study was carried out to examine the applicability of acoustic emission CAE) to judge the groundsoil condition for the boring of small-diameter holes. Four soil samples were put in a tank in compact orloose condition. A rod equipped with an AE sensor was pushed into the soil sample at a specified speed.
Results showed that AE activities and amplitudes of AE signals were markedly different according to
the soil type and packing condition, compact or loose. The frequencies of AE signals from compact soil
were different from those of AE observed in loose soil.
1. Introduction
When a small-diameter pipe is pushed into soil, the pushing force depends on the soil condition at theworking face. However, it is normally difficult to know the soil conditions and in most cases the pushingforce is controlled through engineers' experience. Therefore, we need a method to estimate the soil conditions such as those using a sonic detector or other instruments, but these are still unsatisfactory.
This repon shows the results of penetration tests where frictional sounds of soil panicles were detectedby an acoustic emission CAE) sensor while the small-diameter pipe was being pushed. The applicability of
the AE measurement is examined

2. Outline of Test Method
Figure 1 shows a schematic view of the test device. The test device comprises of a 90 em-cubic box, apenetrating rod with an AE sensor installed at one end, a hydraulic cylinder, a displacement gauge as aposition sensor, a load cell, and a loading frame. The AE sensor detects AE while the rod is penetrating
into soil. The hydraulic cylinder allows the rod to penetrate into the soil at a specified rate. In the test, soil
was prepared into the box at.the depth of 70 cm under two different conditions: compact and loose. Soilsamples used in the test were sand, clay, gravel. and mountain sand. Table 1 shows physical properties ofthese samples.
The AE sensor detects weak sounds generated while the rod is pushed into the soil. Table 1 also shows
the measuring condition. The amplification is set at 60 dB for all samples. The threshold is set as indicatedin this table. depending on the AE activity. The sensor was installed as shown in Fig. 2. The penetrating
rod comprises of a cone section, a rod section and an outer pipe. The rod section and the outer pipe form a
double pipe with rubber packing-material placed between them to prevent noises from the outer pipe.
Tests were carried out after soil was packed compact or loose for each sample. The compact conditionmeans that the maximum dry density is 85 % or more, while the loose condition inplies that soil is put
into the box by free fall from 1 m above the box. The penetrating speed was 10 or 20 mm/s. The rod was
pushed twice in each test.

555

Table 1 Physical propenies and measuring condition.
Maximum dry Optimum moisture Specific
Soil
Gravitydensity, glcm3
Samplecontent. %
2.71
75.7
0.795
Clay
16.5
2.77
1.63
Coarse Sand
0.235
2.67
Fine Sand
1.64
2.63
1.85
1.69
GravelParticle size
at 50%, mm
0.04

0.18
0.3
15

Threshold, V
Loose Compact
0.1
0.3
0.3
1.0
1.0
1.0
0.3
0.3

3. Results of Testing
The data of total AE counts and maximum signal amplitude indicates that the AE counts are higher inthe case of compact soil than that of loose soil. The AE counts are smaller at penetrating speed of 20 mmls
than at 10 nunls. When the rod is penetrating too fast. AE events overlap and the AE monitoring systemcannot recognize AE signals separately. In such a case, the number of AE counts might be less than theactual number. Therefore, data obtained by the tests of 10000s penetrating speed will be mainly discussed.
Figure 3 shows the AE amplitudes detected in soil samples. The vertical axis shows the maximum
amplitudes while the rod is penetrating into the sample. The amplitudes for gravel are the largest of all andbetween 1.6 and 2 V. The amplitudes for sand and mountain sand stand between 0.4 and 0.8 V. It implies
that AE amplitudes depend on the particle size of the sample. Figure 4 shows the relation between total AE
counts and the particle size (in the logarithmic scale), which exceeds SO % of the sample soil. The total AE
counts increase with the particle size. As the threshold level was varied for each sample, it is difficult tocompare directly among different samples based on the AE counts. However, if the threshold level for gravelor sand were set as low as that for clay and mountain sand, AE counts are expected 10 be higher and better
correlated with the particle size.
As AE events and counts are obtained with a different threshold level for each sample soil, it is difficult
to judge the quality of soil only from AE counts and amplitude. Therefore, we recorded AE waveforms andcarried out the spectrum analysis of the waveforms by using an FFr analyzer. Figures 5 and 6 show the frequency spectra of the AE waveforms, which were detected from sample soils. Figure 5 shows the spectra
when the soil is compact and Fig. 6 shows those when the soil is loose. For compact clay, mountain sand,sand and gravel. the dominant frequency was 850 Hz, 800 Hz, 3.65 kHz and 850 Hz, respectively. For theloose case. the corresponding dominant frequencies were 100 Hz, 100 Hz, 3.30 kHz and 3.35 kHz. For
compact samples, the spectra for clay and mountain sand seem to be different from those of sand and gravel.
For loose samples the spectra are again different between the cases of clay and mountain sand and those ofsand and gravel. In both cases, spectra around 6.0, 11.0, 15.0 and 19.0 kHz were similar. This may be
attributed to the influences of characteristic frequencies for the rod in the penetrator.
4. Conclusion
We conducted tests using samples whose qualities were very different from one another. The penetration
tests were carried out in two different conditions: when the sample was compact and when it was loose.
From the results of these tests, we concluded that different kinds of soil show different AE counts, ampUtude and spectra, which are due 10 the soil characteristics.

SS6

I

~

.
I

I

I

I ...............

I

r:... ---C Y I I n IS" r

1'1 s IonII

I f"r:lrne

I

( II

100

I

~

100)

Load

AE

yS1l1

F
250

*

Z50

...
GO

~. j
I...
. ... .. ..
.........0......
~ ::.. ::: :::::: .: :::::. ~.:'.
Tank

.

-...
c:o

BrackeGauge

eerne r I

400

Cell

L

.~

I

Rod

~

•• ••• ••••• o:lo~~:! 'i~00•• ••••

c:o

:I

c:o
....

::::::: . :::::•••••••

•

•••••

:::::
0: :00••• :::::::•• qpiqjo ••;:d.::::~

or ••
•

• ••••••••• 0

1'1 p"••••••• • • • • • • 0000000 • • •

• 0
••

0.0
000

• • • • • • • 00 • • • • • • • •••••••• 0 •••••••••

••

GOO

00.00.00 • • • 0 • • • • •"IUD lil---- Con e

Fig. 1 SChematic view of experirnenL
1.~,._...

-----.

1.0 -

>

6

Crovel0Fillc Sa'KI[J

Coone Salld

0

CI~y

u_,

_

Fig. 2 Schematic view of peneuation rod.
8000 .....- - - - - - - - - - - - - - - - - : : . - - - . ,

I

.. u-

~

GRAVEl.

"
Q

€

•

600lI

~

!

!

1.0

-.::

0.5

I
C I a yCoarse•~FINE SANDCOARSE SAND

III

C

2OllO

CLAY

fine
Sand

0+------,_---_-----,.-----4
o
·2

Sand

2

Log aralft Stu . - )GravelFig. 3 The AE amplitude according to the soil
samples.

·1

Fig. 4 The total count according to the grain size.

857

~lll

CO"''''~IJ,

CO"'I~·C'"

:a~.

;.

,

III

1.4.

~IU

••

10lr--:.------------,-llno III:>~v'

'12. 22 CJ

.:J

"v·

II

·.
E
>

E
>

:; III

.·..
··
'"
u

»

II

:

.1

II

·

II

I~'-

,'"

0

;

o

,

I

II

II

II

Power

II

II

II

'I

SpectrumPower(ClOY)cCompte' I Y

lJ>:). 0'

1ft"

!

II

II

II

II

Ii

(OK.l

Ftc-tlme.C,l . 110'II

Spectrumo",,~. t:(Coarse

,•• ,

&I
kill

:"0 II.

>

.. v·

Sand)

S2. GS3

o! iO

.. v·

·.

8

e

·.·.
·
.·»
2

1I

u

JI

...
u

u

IJ

·.

u

II

... "
»

0

0

F .. ~q.~.e1

PowerSpectrum

••

(Fine

II

Ie

(tt"".".,

1011.1

Sand)

Power

II

II

11

(llHI)

Spectrum(Gravel)Fig. S Power spectra (compact).
1.00' eo I ,1111,--------------..
: 10 D fll

;.
.:Jln

83. )55

.. v·

-'
~

-

E

~ UI

.·..
·
..·
u

..

.. II

...•

loo.~I.,

>

II

1I

"f:~"t'.C'

Power

II

II

II

IOG.)5

"'v·

IJ
lC

I

II

(kill)

Spectrum•(Cloy)

Power

IJ

e II
2

U

·.
·
..·• uh....~l \

·
.·...
·
.·•
u

... U

I.e

It

II

II

/I

(MUI)

(CODrse

Sond)

II

II

r,cQutnc,

Spectrum

II

II

II

n

IS

.1

I

v~j
..
•

1I

f'llqUlnc:,(011.'(Fine: J. ) 5 k'tI
U. lOS ",v'

IS

0

0

rower

Spectrum1ft • •UC

II

Loo,.I.,: 3. 30 leUa
I. • 16 ..v'

;.
o!

I

II

II

"'0.

;. I.eI.

..l
Fr f:Q " t h e ,looa.l,U-·:laOlit0.u....~....J..__'_"___'__'__.o.~'_"""_...J

•

e

IM_.

IJ

» Il

• II
o

.!

II

Sond)

I'owerSpectrumPig. 6 Power spectra (loose).

S58

IC II
10lhl

II

n

(Grovel)A Laboratory Investigation of AE from Coal

R. W. Harris, B. R. A. Wood and T. Flynn
The authors are affdiated with CSIRO Division of Geomechanics. Lucas Heights Research Laboratories. PMB 7.
Menai 2234. Australia.

Abstract
A laboratory investigation was carried out to study the acoustic emission (AE) generated by sized coalsamples. The samples were stressed by crushing using a special mandrel in a tensile testing machine. Large
sized coal samples were crushed and a specific size range was obtained using metal sieves. Some of the sized
samples were then separated according to the percentage of ash using standard float/sink techniques. A predetermined mass of coal was placed in a special mandrel to which AE transducers were attached. and as thecoal was crushed data was acquired. A range of AE parameters computed from the acquired data were plotted
as a function of load and also as a function of ash content for given loading conditions. The graphs for AE
parameters as a function of load indicated the loading region where most energy release associated withcrushing was occurring. The graphs of AE parameters as a function of ash content indicated that there was avalue for the percentage of ash where the maximum energy release occurred on crushing.

1. Introduction
Most investigations on AE from coal have considered the behavior of underground coal seams understress (McCabe. 1979). but this investigation considers another aspect of the total coal mining industry,
namely how the coal behaves during crushing which is important for the processing of coal to obtain a homogeneous product with a specific amount of ash (material left after combustion).
Most of the laboratory investigations on crushing of coal have been concerned with the determinationof the strength of coal and the size distribution that occurs as a result of the comminution process (Bennet,
1937; Bennett et al., 1941; Brown, 1941; Pomeroy. 1957). The conclusions of most of this work as wellas producing plausible distribution laws for coal sizing after breakage. are that coal is(i) a heterogeneous structure where the cohesive forces between grains is small compared with theforces required to fracture grains.
(il) the grains are quasi-homogeneous.
(iii) fracture and cracking occurs at points where the material is weakest and aU indications are that alump of coal contains preformed cracks and fissures, and(iv) a particle of coal fractures to form aggregates.
There has been a laboratory investigation not specifically using coal but considering the comminution process in a cone crusher and using AE to identify the operations in the crusher (Harrington et al., 1980).

2. Experimental Arrangement aod Procedures
Samples of coal having ash contents in the range 5 to 33% were obtained from different coal mines inAustralia. After coarse crushing the samples were sieved so that the larger particles would not pass througha 2.4 rom mesh and the smaller particles would pass through a 1 mm mesh. The samples were then separated according to ash content using the flaaf/sink method. This method utilizes the fact that there is a simple relationship between the percentage of ash and the density of the coal. A mass of 5 g of the samples wasSSgplaced in a mandrel comprising a shallow piston and cylinder which was placed in an Instton testing ma-chine driven at a crosshead speed of 0.025 mm1min. The mass forms only a single layer of coal particles between the faces of the mandrel. The mandrel assembly was mechanically isolated from the tensile machineusing teflon sheets.
Two different ttansducers (a B&K 8301 accelerometer (sensor 1) which has a resonant frequency ataround 25 kHz and a usable frequency range from 1 to 8,000 Hz, and a B&K 8313 AE ttansducer (sensor 2)
which has a resonant frequency around 200 kHz) were coupled to the mandrel cylinder using silicone grease.
The detected signals were amplified 40 dB in preamplifiers and a further 30 dB in main amplifiers. The signals were observed using a digital osciUoscope and also passed to the SIROEAR unit (Harris, and Wood,1984) for the determination of peak amplitude statistics.
The first group of experiments increased the load in 1 to 2 leN steps for sized only samples of coal. The
second group of experiments acquired data from coal samples having different amounts of percentage ash fora specific load increment in a sensitive region as determined from the first group of experiments.
1.21110
1. Blilo

1.611100Do1.111110oo1.41110

D

D

.c
,

0111.91110

o,"l.allloc

DOI:1.11. . 0111.'1110.

.•.

.1I.UIo

Co

I.. 11.61110

C

D

C

o

o

11.4'10o~ 11.41110

II. a..

•. an
II • lillie mT'TT'lTn"T'TT'lTT1"1ITTTTT1"1rTTTTTT'1lTTTTT1"1rTTT1TT1rTTTT1

II. 1I'.+rrnT1rTTTTT'T"1'TTTrTT"TTlTrTTTTT1"'rTTTTTT1rrTTTTT1MTTn

11.11

5.11

18.8

15.11

n.1I

n.1I

II.'

Fig. 1 AE counts/l leN increment as a function ofload for the sensor 1.

5.11

18.'

15.'

aI ••

Fig. 2 AE counts 11 leN increment as a function ofload for the sensor 2.

3. Results and Discussion
Various AE parameters were compared to determine those most sensitive to changes in coal characteristics and crushing regimes. The final choice of parameters were the total AE counts and three quantities
derived from the SIROEAR peak: distribution; namely, indicative energies 1 and 2 and the skew (Wood andHarris, 1986). These parameters are related to the zeroth, first, second and third order moments of the distribution of peak amplitudes.
Figure 1 gives the counts per I leN increment of load for sensor 1 and Fig. 2 for sensor 2 for a sized
only sample. It is apparent that there is a peak at about 3 kN. This effect is not surprisingly most pronounced for sensor 1 since it has the best low frequency response. (In subsequent figures, only sensor 1 data
will be given.) The peak at 3 leN was also apparent in the plots involving indicative energies 1 and 2 andthe skew as given in Figs. 3 - S. The effect is most pronounced for indicative energy 2 indicating an increase in the large amplitude pulses. The 3 leN peak: in the AE responses corroborates the earlier finding thatcoal fractures along the existing cracks in a brittle manner into many small aggregates. Mter that initiallarge fracturing, there will be only smaller releases of energy since the fracturing is now associated with thesmaller aggregates.
The results for AE as a function of ash content are given in Figs. 6 - 9. The count rate and indicative
energy 1were most sensitive to the effects of changeing ash content This indicates that the best discrimination occurs when the chosen parameter does not give a greater weighting to the larger amplitude pulses.
Several sets of data were available for low ash contents and the plots show the scatter that may occur for560

this data. The existence of a peak. in the plot is considered to be associated with the structure of coal and theway that the ash is bonded into that structure so that most energy release occurs at an ash content near 10%.
o

1.aOk

1.11110

1. ••10.... Uk

II~

0~0.8IJk 0~•~1I.Uk

o

N

o

II

0

••

o

II

~

IIlI.6lJk

o

..

o

~ •• 41k

o

~

00

M

•• uil

o

C

o

Co

oooooc•••1k+"'rrrrrrrr'l'"rr'I'"rrrrr'l'"rr'I'"TTTTTTTTTTTTTnTnTnTnTl"nas.lI
alI.lIll1.lI
15 ••lI.lI

Fig. 3 Indicative energy 111 kN increment as afunction of load.
1.8.10Fig. 4 Indicative energy 2/1 kN increment as afunction of load.

a

1.Uk

c

'·' '1

1.allk
1.4l1k
1.allkoc.

001.1l1k

0~~~l.lInOll.9l1k0,l(M" lI.8l1k~ lI.7l1ka..

olI.60k

o

5.lI

0

0
0

0 0

ll1.lIn.lIoolI.allk0

lI.lIOk
lI.lI

a

0

0

•• an

o

lI.5lJko•• 4l1k

0
0

o
o
0

o

o

a
o

0 0
all.lIn.1l

Fig. 5 Skew!l kN increment as a function of load.

O.lI

la.lI

18.lI

23.lI

28..

aa.1l

Fig. 6 AE counts as a function of % ash.

90ll.1lo0lI.95k.. 78l1 ••

N

•

II

0

all.15k

a

•••

••

C

•
~

~Ull.8II

C

C.lI.55k

..

C

M

·..
a

8

~

0

allll.lI

0

0

0

0
0
0

~

0

0

0

0

0

0

M

0

0

0
0

8.3510

0

0
0

0

D

0

0

ll1l1.lI
a.lI

8.8

n.lI

&I ••

22 .•

aa.8

lI.15k3.lI

n .•Fig. 7 Indicative energy 1 as a function of % ash.

a.a

13.8

n.lI

n.1l

88.0

n.lI

Fig. 8 Indicative energy 2 as a function of % ash.

561

1.'1I1e0c

LillieC

II. II Ilk
C

I

C

CD C

.

:1.Uk

c

C

C

1I.4111e0Dc1.'1I1eoC

II. IIl1k 1mm'ITl'TJT'mTTTTlf'mTTTTTT'JTIT'I'mTl'fTTTm"T1TJTTmTlTTT"m
1.1
8.11
13.11
18.8
'3.11
'8.11
33.B

Fig. 9 Skew as a function of % ash.
4. Conclusions
AE provides information on the crushing of coal in laboratory relating to the load at which maximum

fracture occurs for a given sample of coal and to the ash content associated with the maximum energy release on fracturing. The best sets of data were obtained using a sensor responding in the low frequency
ranges.
References
J.G. Bennett (1937), "Broken Coal", J. Inst. of Fuel, 10.22-39.
J.G. Bennett, R.L. Brown. and H.G. Crone (1941). "Broken Coal II-The relation between size distribution
and breakage process". J. lnst. of Fuel, 15, 111-128.
R.L. Brown (1941). "Broken Coal III - the Generalised Law of Size Distribution", J. Inst. of Fuel. 15,
129-134.
C.D. Pomeroy (1957) "A simple method for the Assessment of Coal Strength". J. Inst. of Fuel, 31. SO54.
N.M. McCabe (1979) "Acoustic emission in coal". Ph.D. thesis. University Microfilms.
T.P. Harrington. P.G. Doctor. and K.A. Prisbrey (1980). "Analysis of the Acoustic Emission Spectra ofParticle Breakage in a Laboratory Cone Crusher". Trans. Soc. Mining Eng. of AIME, 270, 1879-1883.
R.W. Harris. and B.R.A. Wood (1984) "Acoustic Emission event counting using an Apple computer",
Progress in Acoustic Emission 11. JSNDI, Tokyo. pp. 351-358.
B.R.A. Wood, and R.W. Harris (1986). "Acoustic Emission applied to railways". Progress in Acoustic
Emission Ill. JSNDI. Tokyo. pp. 125-135.

S62

Determination of the Initial Stresses on Rock Mass using
Acoustic Emission Method
K. Michihiro, K. Hata, H. Yoshioka and T. Fujiwara
K. Michihiro is affiliated with Department of Civil Engineering, Setsunan University, K. Hata, H. Yoshioka andT. Fujiwara are with Research Laboratory, Ohbayashigumi, Shimokiyodo 4-460, Kiyose, Tokyo 204 Japan.

Abstract
Large scale underground cavern such as underground power stations and oil storages, could be designed
and constructed rationally, if the initial stresses of the site are known. Authors have carried out fundamental
studies on the detection of the initial stress using the Kaiser effect by which the stress history of rock could
be estimated. In this study, characteristics of the Kaiser effect on the rock material have been investigatedexperimentally. It has been found that the (1) most rocks show that Kaiser effect phenomena; (2) stressdetennined from the Kaiser effect is not always identical to the maximum stress of the rock; and (3) Kaiser
effect is dependent on the time period of unloading of stress.

1. Introduction
When subjected to applied stress, one part of the stored energy in the material is released in the form of
the elastic waves, called acoustic emission (AE) and could be a powerful tool in assessing the mechanicalchange of internal characteristics in the material. Ullrasonic waves have been utilized for non-destructive
testing of materials, in which waves are transmitted through the material and the characteristics of reflected
waves are used to study the behavior of the material. On the other hand, AE method utilizes the waves dueto the defect generated by loading. Its use is found in various fields, such as atomic reactors, pipelines,
bridge structures, airplanes, etc. AE method has caught attention of civil engineers, especially, in the fieldof geotechnical engineering and rock mechanics. A state-of-the-report is already published on the application of AE methods in the field of geotechnology and rock mechanics (Hardy, 1981).
Once the initial stresses are determined accurately, rational design and construction of large caverns,
such as underground power station and oil storages, could be accomplished. Further, the shape, location andselection of the layout can readily be detennined (Hoek and Brown, 1980). By using finite elements methods(FEM), it is possible to analyze the stability of a structure. Referring to the initial stresses as one of theparameters, rational and economical design can be carried out In addition, rock burst phenomena can be predicted. For the determination of the initial stresses, over-coring method (Sugawara et al.; 1986; Kanagawa
et al., 1986) and hydraulic fracturing method (Mizuta et al., 1983) have been currently in use.
Kaiser (1953) developed AE method to study material properties using tension tests of metals and foundthe Kaiser effect Rusch (1959) and Goodman (1963) found the Kaiser effect in concrete and rock, respectively. Goodman's results suggested that initial stresses can be determined in rock. Kanagawa et al. (1977)
flfSt determined the initial slresses on rockmass using AE method. Kanagawa (1977) used rectangular specimens and estimated the initial stresses. Yet, many aspects of the Kaiser effect have not been clarified.
Yoshikawa et aI. (1981) used repeated loading on the specimen and found that there exists remarkabledifference in the Kaiser effect if the specimens have different loading histories. The relation between theresidual stresses and the Kaiser effect has been studied by Murayama (1984, 1985, 1986) and Michihiro(19858, b).
In the present study, the influence of various parameters on the Kaiser effect in rock is discussed.
Then, using AE method the feasibility of determining initial stresses has been examined.

863

o
f

3

/

-2r

m
"'C

D

,. SPECIMEN
7. STRAIN METER
2. STRAIN GAGE
8. PRE-AMPLIFIER
3. LOADING PLATE 9. DISCRIMINATOR
4. LOAD CELL
10. DUAL COUNTER
5. RAM
II. PERSONAL COMPUTER
6. AE TRANSDUCER 12.0SCILLOSCOPE

Fig. 1 Block diagram of AE measuring system.

It

m
-4 0
c

Ai ~

[V v

o

Q.
ell

,,~

"\
f\ ""\ M

Q)

Cl::

\

-6 0

\,,\ (

0

2

4
6
8 ~ V 10Frequency (X50 kHz)

Fig. 2 Spectroscopic characteristics of transducer.

2. Experimental Equipment and Rock Samples
An outline of AE measurement system is shown in Fig. 1. AE system consists of a transducer, anamplifier, a discriminator and a counter. Resonance frequency of the pzr transducer was 140 kHz and was
fixed by a silicone adhesive to the center of the specimen. In the case of a cylindrical specimen, thetransducer was attached via a 3 mm thick stainless steel jig (Fig. 1). A band-pass fllter (100 - 200 kHz)
matched the frequency response characteristic of the transducer shown in Fig. 2. In the present study, theevent counting method has been utilized to measure the AE signal occurrence. A fixed threshold level was
generally used. To minimize the effect of reflected waves. two threshold levels, high and low, were also
employed.
Several loading machines have been utilized. They are an Amsler-type universal tester (max. loading
capacity 2 MN); a high stiffness testing apparatus (max. loading capacity 1.5 MN with 7 GN/m stiffness);a triaxial compression tester (max. axial loading 0.1 MN, confined stress 10 MPa) and a creep tester (max.
loading capacity 14 kN). Accumulated AE counts and the stress-strain curves obtained from these tests were
processed by a micro-computer.
One of the problems in the event counting is the discrimination of noise which results from various
electronic instruments and the outside. The effect of the noise can be minimized to a greater degree byusing proper grounding. Another source of the noise generation could be the gap between the loading plate
and the sample. The noise due to this effect could be removed by various means. For example, Kanagawa
et al. (1977) used a mixture of cement and araldite called hunch and pasted it on the top of the surface of thespecimen. The use of blotting paper can also reduce the noise. Authors have used a relatively simple
method to minimized the noise due to the gap, using a sponge pad with air bubbles.
Many samples were prepared. The cylindrical samples had the following dimensions; the diameter; 30
or 50 nun. the height: 60 or 100 mm. The cubic samples have the edge dimensions of 50 mm or 150 mm.
Table 1 shows the various rock types used in the experiment with their original location, related project andthe mean values of the unit mass and the unconfined compressive strength. Except for mud stone, all rocktypes (ranging from medium to hard rock) were loaded at the rare of 9.8 MPalmin, whereas, the mud stone
specimens were loaded at the rate of 0.98 MPalmin. The results reported here are based on the experiments
carried out at least five different times on the specimens of the same rock type.

564

3.

Evaluation Method of the Kaiser EffectUsing mortar and rock specimens, Kanagawa et al. (1977) carried out basic experiments to study the
Kaiser effect. They recognized the presence of the Kaiser effect due to the rapid increase in the AE
accumulated count rate as shown in Fig. 3. When two rapid increases in the AE count are observed, it isdifficult to recognize the Kaiser effect In order to recognize the previously applied stress or Kaiser stress insuch cases, we used the following observation; Le., the AE energy (maximum amplitude) observed before
the Kaiser stress is reached has a lower value than that observed above the Kaiser stress. Various combinations of sensitivity values and threshold values were used with the rock samples.
Samples with the ratio of diameter to length greater than 1:2 were used in the present experiments.
Pre-loading was done under unconfined compression up to 15 kN by repetitiously loading and unloading.
Once completed, samples were loaded monotonously to the higher load values than the pre-loading values
with various combinations of AE sensitivity and threshold values. Typical result is plotted in Fig. 4.
Table 2 shows various conditions and the results obtained. From the table, we can see that the condition (a)is the most sensitive to the Kaiser effect, whereas condition (c) shows the least. Figure 4 shows the results
at the monotonous loading on load·displacement vs.
AE count rates. The arrow shows the pre-loading_6.51---.-------...-------r---·T-II)
value and the dotted line is the value of the onset of•C
the continuous AE occurrence.
::JoFrom the figure, it is found in the condition (a) Jl)u 6.0
that the occurrence of AE begins at 12.7 kN, but o
Estimated pre-loadthe actual loading was up to 15.0 leN. In the condi>-5.5
2 ton -'1---1---;tion (b), pre-loading and the occurrence of AE be--higher than pre-loading value (IS kN). Consequently, the condition (b) is appropriate for determining
pre-loading value. Under conditions (a) and (c), it isdifficult to determine the preloading value. In othercases with condition (a), the occurrence of continuous AE began at 16.0 kN. Thus, we can nearly
determine the preloading value. It is necessary to
select appropriate sensitivity and threshold in orderto determine the preloading condition using theKaiser effect
4.

g

0;

gins almost at the same value and in the case (e) the

occurrence of AE is observed at 17.2 leN which was

g5.0
w

«

'E 4.51-·=-="........-1
~
o
3

I ~

E 4 'O........._~_--a..--L----I_~~O

<3

400

500

0
...J

600

Ti me T (sec)Fig. 3 Evaluation of the Kaiser effect by Kanagawa
et al. (1977).

Experimental Method and Results

4.1 Characteristics of the Kaiser Effect Under Repealed and Constanl Axial Loads
Purpose of this experiment is to investigate the effect of residual strain of the Kaiser effect when rockis subjected to repetitious loading, with the strain increasing gradually. After strain reaches the maximum
value, it becomes constant as shown in Fig. 5. Here, the strain of unsaturated and of saturated conditions
are defined as the states where strain continues to increase and where strain becomes constant, respectively.
Samples were first subjected to uncontIned repetitious loading (sample size: dia. 30 mm, height 60mm), triaxial loading (dia. 50 mm, height 100 mrn), or constant axial loading (dia. 30 mm, height 60 mm)
to prepare the samples to strain-saturated and strain-unsaturated states. The Kaiser effect was studied for bothstates afterward. Unconfined repetitious loading was carried out on all rock types shown in Table 1.
Triaxial repetitious loading and constant axial loading were carried out on only granitic rocks and mudstones. Figures 6 • 8 show typical results obtained on granitic samples. These figures show the relation-

865

Table 1 Details of sample location. project. uniaxial compressive strength (cr) and unit weight ('Y).
Mean values
Location

Identification

Projecty (kN/m 3 )a (MPa)I

Ikoma

Tunnel

25.8

90

II

Inada

Quarry25.8

132

Granite-porphyry

Akikawa

Dam

26.3

83

Porphyrite

Hyogo

Tunnel26.5

200

Rhyrolite

Minakami

Tunnel26.4

120

Sand-stone

Aiki

Rock slope

25.9

210

Tuff

Dojo

Tunnel24.3

84

Clay-slate

Uji

Tunnel26.9

95

Crystal-schist

Fujioka

Quarry28.9

71

Marble

Italy

Quarry26.5

74

Andesite

NaganoTunnel

26.0

112

I

MachidaTunnel

18.3

4

II

NaganoTunnel

18.0

8

III

Gifu

Tunnel

16.7

6

GraniteMudstone

Table 2 Various combination of sensitivity and threshold values.
Total
amplitudeThreshold level
(mV)

(dB)

VH

VL

(kN)

Loading value of beginnin gof the continuous
occurrence of AE
(kN)

(a)

80

400

380

15

12.7

(b)

80

450

430

15

15.7

(c)

80

500

480

15

17.2

Pre-load

ship among the axial strain. axial stress and cumulative AE count. Figures 6(a) - 8(a) show results obtained on strain-unsaturated samples and Figs. 6(b) - 8(b) show results obtained on suain-saturated samples. In
these figures the arrow indicates pre-axial stress and the dotted line indicates axial stress estimated by the
Kaiser effect. The application of the pre-axial stress is the same for all types of samples. It is found thatthe stress determined from the Kaiser effect is quite different from the pre-stress. while these stresses foundto be equal for the slrain-saturated samples. Other rock samples also show similar results. Therefore. it is
566

(a)

300 _

Lood-Oisplocemenl
Kaiser ellec!
-Pre-load

U

III......

30

summarized that under the strain-saturated condition,
where the residual strain is left to be of high level,
the applied stress level could be estimated using theKaiser effect This is not the case, if the rock samples are not strain-saturated. Thus, it is concluded
that the axial stress estimated from the Kaiser effectis found to be dependent on the residual strain of thesample.

no

II)

E 200

8

w

!;i 100
a::

~

o

50

100

150

DISPLACEMENT (x 1/IOOmm I

4.2 The Kaiser Effect on Three Dimensional StressConditions

300

30

c200

20Z

In situ stress conditions are so complicated thatthey should be considered in three dimensions.
Hence, it is necessary to examine the Kaiser effecton the three dimensional stress conditions. The AE
charac-teristics were examined experimentaUy under
three dimensional stress conditions by using cubic
and cylindrical specimens. Cubic samples of dimensions 5 x 5 x S cm and 15 x 15 x IS cm wereprepared in strain-saturated condition by loading
differently in each direction (X: 9.8 MPa, Y: 14.7
MPa and Z: 19.6 MPa). The Kaiser effect wasinvestigated for each individual direction under theuniaxial monotonous loading condition. Three kindsof rock samples; i.e., (1) granitic rock samples, (2)sand rock samples; and (3) tuff rock samples were
tested. Samples of IS cm cube were prepared fromthe granitic rocks, while 5 cm cube samples wereused for the sand rock and tuff rock. Results obtained on the granitic rock samples are shown in Fig. 9,
showing the relationship among the uniaxial stress,
uniaxial strain, cumulative AE counts under uniaxial monotonous loading for X, Y and Z directions. These results show that the pre-axial stressis estimated well by the Kaiser effect. The sandrock and tuff rock samples also showed the sametendency. Thus, it is concluded that the loading
directions have no effect, i.e., the loading in Y andZ directions have no effect on the Kaiser effectobtained in X direction.
Although the apparatus employed is not capable of simultaneously loading in three directions,the present experiments could provide enough information on three dimensional loading conditions.
Strain-saturated cylindrical specimen (SO mm and100 mm height) were prepared by using triaxial
tester under various repeated loading conditions bychanging principal stress ratios (O'J/01). The principal stress conditions used in the experiments are

(b)
~

~
II)

B
U

.>t:

......

o

W
I-

et
100

et 100
0:::

...J

W

et

o

50

100

150

DISPLACEMENT (x I /IOOmm I

(e)

300

30

20~
o

w

et
10 0

!;i 100'

...J

0:::
W

et

, I .

l---J---L--~

o

50

100

150

DISPLACEMENT (x 1/IOOmml

Fig. 4 Relationships among axial load, axial displacement and AE activity under various combination of sensitivity and threshold values.
0.034 .

!!
z

~0033V
-J

<t
::>
o

__

"* ~ 100· 77 1".1II)w

a: 0.032 _
10
20
30
40
REPETlT IOUS NUMBER ICYCL.£SI

50

Fig. 5 Relationship between repetition numbers
and residual strain.

867

450 - Slress- Strain"""" AEaclivily- -- Kaiser elletl
c

40

-

600 -

Slress- Slrain
AE aelivily--- Kaiser elfeci

en

c

0='

a..

-:: 300-

I-

w
«

20

-.

0
Q.

::!.

(J)
(J)

0, =4.90MPo
03=4.90MP 10

W

04.7MPa
150

III

§400
0

:;E

-oJ

i:!
0

20

c>-o

U)

en

ILl

~

12.6MPo

::E

0::
I-

l-

en

(J)

ILl
~

0.09

0

Q?~.MPJl
0.04
0.06

0.02

STRAIN (%)(a)-

150 -Stress-Straino--<l AE oclivily-- Kaiser effect

40

:::l

-

B

~

en
c

150

.
-

c

a..

0

-l

«

20

I0

15.0MPaIW§0 100u(f)(J)

Siless-Slroin
AE ael i v ity.-- Koiser effect

20

w

~

I0
~

::E

\0

~

(J)

0
Q.

0, =4.90MPo
03:4.90MPo

J

I-

«

-

<>-<l

50
____ nILl'!:I~MP.9

___

U)

en

IJJ
0::
I-

en

~

0.06

0

009

0.04

STRAIN (%)

STRAIN (%)

(b)

(b)

Fig. 6 Relationships among axial stress, axial
strain and AE activity in a granite specimen subjected to cyclic uniaxial compressive stress (14.7
MPa). (a) strain-unsaturated. (b) strain-saturated.

0.06

Fig. 7 Relationships among axial stress, axial
strain and AE activity in a granite specimen subjected to cyclic triaxial compressive stress (Ol =4.90 MPa, 0'3 = 4.90 MPa). (a) strain-unsaturated.
(b) strain-saturated.

summarized in Table 3. Granitic and mud-stone samples were used in these experiments. Typical results
obtained from the granitic samples are shown in Fig. 10. Figures lO(b) and (c) show the results obtained
when the principal stress ratios (0'3/01) were 1.0 and 0.25. From both figures, it can be seen that the axial
stress estimated by the Kaiser effect is the same as that of the maximum principal stress (crl). Undervarious principal-stress ratio conditions, axial stress estimated by using the Kaiser effect are found to besame as those of pre-axial stresses. Similar results were obtained for mud-stone cylindrical samples.
Therefore. it is concluded that the Kaiser effect occurring in each individual direction; e.g. X. is independentof the loading in other directions (e.g. Y and Z).
4.3 Effect of Sample Coring on the Kaiser Effect

In order to determine the initial stresses of rockmass by AE method, it is necessary to know the appropriate sample size once the samples were core-drilled from the field. When samples are taken from underground, various types of core samples are obtained by the pushing of boring drill bit These influence on
the Kaiser effect was examined. Cyclindrical samples (dimensions dia. 30 mm and height 60 mm) were
drilled from cubic samples (egde dimensions 15 cm) which were subjected to three dimensional stressconditions.

S68

(a)

..
§

150

- Stress-sHain

The cubic granitic rock samples used have thesame three dimensional stress conditions as described in the previous section (X:9.8 MPa. Y:I4.7
MPa and Z: 19.6 MPa). In all three directions. strain
was saturated. Cylindrical samples were cut fromthe three directions as the axial direction of cylindrical specimen. Results are shown in Fig. II.
Figures tt(b-d) show the results obtained under themonotonous loading conditions for samples obtained from X-, Y- and Z-d.irection. respectively. Fromthese results it can be seen that the axial stress estimated by the Kaiser effect in the individual directions are the same as pre-axial stress in the individual directions. Other rocks show similar results.
Therefore, the treatment of core samples has little
effect on the Kaiser effect

20.-oAE actlvlly... Kaiser eflecl

o

~IOO
...J
<l
I-

~

.., 50'<l2.15 MPa~-----------------

0.02

004

0.06

STRAIN ("41

(b)

45 -

Slress-Slroln

20~ AE oClivily.

- -- Kaiser ellecl

"

E 30.

Q.

8

:!:

~
...J

..,a:gj

10

Ol-~ 15

..,

I;;

<l

002

0.04

0.06

ST RAIN ("4)

Fig. 8 Relationships among axial stress. axial
strain and AE activity in a granite specimen subjected to constant uniaxial compressive stress (14.7
MPa). (a) strain-unsaturated. (b) strain-saturated.

4.4 Effect of Change ofLoading State on the Kaiser
EffectInitial stresses of groundmass are subjected tovarious changes due to a variety of geologicalmovement on the rock. These are concentration due
to diastrophism and relaxation due to weathering.
Thus. the stress history of rockmass changes due tovarious geological activities. One question is how
these changes in loading conditions affect the Kaiser

z

x
(a)

-S
VI

30

30 -Srress -Srraln
0-0 AE CCTlVlty--- Kaiser erie::

c:
5

0
So 20

~
.J

.J

<::

t0
t!oJ

::0
VI

15
9 ?MPo-----------(14.?MP~)

;-

O

10

!oJ

<:

<:

0.02

004

STRAIN (%)
(b)

0.C6

=0

20

<::

I--

Srress-Srraln
aCTivity
KOlser ette:T

0-> AE0---_.!~_-:.~~£..

r:

:~

VI

0

a..

~

cn

<n

IJJ
0::
I--

<n

i

0.02

0.04

STRAIN (%)
(c)

o.os

C

is

~
.J

30

Stress-Srram

0-> AE aCTiviTy- - KQlser effeCT

"2

a..
:;
----------j1
19;"Po

20

15

<::

t0
I--

w

UJ

Q:

10

I-

<n

<:
0.02

0.04

006

STRAIN (%)(d)

Fig. 9 Experimental results in a granite cubic specimen subjected to cyclic compressive stress of eachdirection. (a) Directions of stress applied. (b) Relationships among axial stress, axial strain and AE
activity in X-direction. (c) Relationships in Y-direction. (d) Relationships in z-direction.
S69

<n
<n

150 -

Siress- Siroin

o I

n_

~

i:
:;)
a 100

~

10

~

~

eI)

c::

0

l-

I-

eI)

:E
en

W

I-

l-

________ ~~H~!!1P.SJ

15

~

a:

50

a

Q.

.2

en
eI)
W

I-

w

30

C
:::I
a

Q.

~

0

Stress-Stroin

VIaO. =4.90MPo
03=4.90MPo

-...

-

0-0 AE octivity_n Koiser effect

Koiser effect

VI

I-

60

20

0-0 AE octivity

eI)

w

«

~

0.04

0.02

0

(a)

0.06

0.06
STRAIN (%)

STRAIN (%)

( c)

(b)

Fig. 10 Experimental result in a granite specimen subjected to triaxial cyclic compressive stress. (a)Direction of stress applied. (b) Relationships among axial stress, axial strain and AE activity (cr} = 4.90
MPa, OJ = 4.90 MFa). (c) Same as (b) (0'} '" 19.60 MPa, 03 = 4.90 MPa).
z

y

(a)
30

Iii
C

5lress- Strain

30~AE aCTiviTy---Kaiser effecl

'0
Cl.

5

~

!

20

...J

<:

15

I-

0

IUJ
~

19.BMI'al
10 I

97MPa

-----7--------

:8.g

cn
cn
UJ

a:

f-

en

.c:

30 -

SIren-STrain

"

""

oJ

a..

:;:
(I47M~~__ ,!,4.,;,~~f~ __ I 5

0

III
<l:

~

LoJ

0:

to-

10

CIl

.

'h
o

--- Kaiser effeCT

Q04

0.06

0.02

0.04

STRAI NSTRAIN (%)(b)

C

20.1 MPa-. I -------

u

I

~

15

I

oJ

<l
to-

I

~ 10

r

to-

Ul

I
I

1.

0.02
0.04
STRAIN!"k)0.06(0..'oJ

0.06

(d)

(c)

Fig. 11 Experimental result in a granite specimen cored from cubic specimen subjected to cyclic com·pressive stress of each directions. (a) Cylindrical specimen cored from cubic specimen. (b) Relationships
among axial stress, axial strain and AE activity in specimen cored from X-direction. (c) Same in specimen
cored from Y-direction. (d) Same in specimen cored from z-direction.
Table 3 Subjected principal sttess (cr}, OJ) in triaxial experiment
Ol (MPa)OJ (MPa)
OJ/O'}

4.90

4.90
1.00

7.84
7.84
1.0

7.84
4.90
0.63

6.86
4.90
0.71
970

9.80
4.90
0.50

11.77
4.90
0.42

14.70
4.90
0.33

~
LoJ

a:

I

III
<l

I'

-a..

(I9.6MPa)

Co

0.02

o

Stress-Straine>--o AE aCTiviTy-_. Kaiser effecl

~20

~

30 -

30

0-0 AE aCllvily

19.60
4.90
0.25

(a)

006

(b)

60 -

SlreSs-Slroin
activity_u Kaiser ellect

l:I

'"z
I_-~~

30

0-0 AE

o

o

o

:£

2

~

Q.

>-

15 ~
iii

It:
lll)

o

I~

300
4~
T1MElhl

600

7~0Fig. 12 Effect of a change of load on the Kaiser effect (a) Relationships between time and axial strain. (b)Relationships among axial stress, axial strain and AE activity subjected to strain recovered after axial stress
reduction.
0.06

0.06

~

~004

~
o

CREEP

z

~

'">OIl

i!
z

>-

:

L---==----t 2'"~
0.02 ~

~

:;;

0-

...

W

~

lFa;CREEP

0.Cl2~

C>

z

~

OIl
W

;;
z

~

:i

>-

!:

0.04

~

~ RECOVERY. 1. ~
Cb

..J

o

150

300

450

c

:J

(a)
30

600

60 -

Stress-Slrain
AE activiry-- Kaiser effecr

20

o-<l

...

0

40

§c 40

Q.

~

0

~
..J

~

450

(a)
Stress-Strain
activity--- Kaiser effect____~~~fE

0300
TIMElh)

~ AE

:!

150

TIME lh)

60 -

-

o

600

~
..J
ct
I-

15 m
lI)
w

l-

It:
I-

11.52MPa---------

!
10 ~

iii

It:
I-

g 20

m

LlJ

c

Q.

m

ILl

ct

ct

0

0.02

004

0.06

006

STRAIN (%)

(b)

(b)

Fig. 13 Effect of five changes of load on the Kaiser
effect (a) Relationships between time and axial
strain. (b) Relationships among axial stress, axial
strain and AE activity subjected to strain recovered
after axial stress reduction.

Fig. 14 Effect of time lapse after stress release on
the Kaiser effect. (a) Relationships between timeand axial strain recovery for a period of 7.5 days. (b)Relationships among axial stress, axial strain and
AE activity subjected to strain recovered after axial
stress reduction.

effect To model the geological stress history on the sample prepared in the laboratory, samples were
subjected to 14.7 MPa loading condition to obtain strain-saturated samples. These strain-saturated (14.7
MPa) samples were unloaded till 9.8 MPa and once again samples were strain·saturated. Then the samples

571

0.06

15(

_0.04
~

z

~

~

v

vu
0

-

0

0

RECOVERY RATE OF RESIDUAL STRAIN
Eb
ro,100'84%
r.;:------I E"a:CREEP__ -J:r. 0,02 ~

~

~

o.

lJ")
lJ")

0

a.

t-

0

0

w 5
ct:

l-

~

V)

.......
W

<l

oJ

150

.6

~

8 RECOVERY'tbT ~°0"':!".
~

g
z

z;

-; 10

;;

IS

:!

~

300

TIMElhl

450

600

ThO

o

5

10

15

20

TIME (d)

(a)

(a)
15

20
o

-

a.

(/) 10
(/)

0

I.LI
0::
l-

10 (/)(/)

Q.'l~ME'a

0

::l;

o
Q.
:::E

________

u

V)

llJ

0

o
~ 5

0::
lV'

~

I-

<II

0.02

004

.....

nOG

o
o

STRAIN (0,(,)

e

~

~

~

RECOVERY RATE OF RESIDUAL STRAIN (%1(b)

Fig. 15 Effect of time lapse after stress release on
the Kaiser effect (a) Relationships between timeand axial strain recovery for a period of 20 days. (b)Relationships among axial stress, axial strain and
AE activity subjected to strain recovered after axial
stress reduction.

(b)

Fig. 16 Effect of times lapse after stress release on
the Kaiser effect. (a) Relationships between lapse ofdays and estimated stress by the Kaiser effect (b)Relationships between recovery rate of residualstrain and estimated stress by the Kaiser effect.

were tested under uniaxial monotonous loading and the stresses were determined using the Kaiser effect.
Figures 12(a) and 13(a) show two unloading proce-dures employed from 14.7 MPa to 9.8 MPa. Fourdifferent rock type samples of 30 mm diameter and 60 mm height: (1) granitic rock; (2) tuff rock; (3) sandrock; and (4) crystalline schists; were tested. Figures 12(b) and 13(b) show relationship obtained on granitic
samples among axial stress, axial strain, and accumulated AE counts. These results show that the apparent
axial stress determined from the Kaiser effect is not identical to that of maximum pre-axial stress (14.7
MPa), but is the same as that of stepwise decreased final axial stress (9.8 MPa). From the strain curve one
may notice the recovery when the unloading was done from 14.7 to 9.8 MPa. It follows that the stressestimated using the Kaiser effect is not the maximum pre-axial stress, but is the same as fmal axial stress.
These results show a remarkable similarity to results obtained in the previous section where the Kaiser
effect was found to be dependent on the residual strain of the sample. If we compare the geological stresshistory on the rockmass with that of the modelled sample, results imply that we can determine the prestress on the rockmass at the moment and not the maximum stress to which rockrnass was ·subjected togeologically.
4.5 Effect ofthe Durationfollowing Stress Release on the Kaiser EffectOnce the sample is obtained from in-situ with the removal of the overburden pressure, samples go
through the stress release process and strain recovery occurs. We have shown that the residual strain is
associated with the Kaiser effecL It is important to investigate how the removal of overburden pressure872

affects the estimation of stresses obtained from the
Kaiser effect Cylindrical (diameter 30 mm; height
60 mm) samples were prepared under 14.7 MPa
constant axial stress conditions. Once the samples
were strain-saturated, unloading was done and thesamples were left for strain recovery for various
durations. Then, pre-axial stress was estimated using the Kaiser effect Granitic and mud-stone rocksamples were used for the present experiments.
Experimental results were obtained on the granitic
Drilling diameter:75mm
samples experienced the strain recovery for 7.5 days
Drilling length: 11m
and 20 days, respectively. Figures 14(a) and 15(a)_____ nshow a strain vs. time curve. Figures 14(b) and----=L....J15(b) show relationship among axial stress, axial
sttain, and cumulative AE counts obtained under
uniaxial monotonic loading. From the results, the Fig. 17 Location ofrock sample coring.
sttess obtained by using the Kaiser effect is not thesame as that of maximum pre-axial stress (14.7~Diretion of drilling
MPa). Samples left for 20 days recovery, the sttess
Dire tion m DirecUon nobtained by using the Kaiser effect shows less thanhalf the value of maximum pre-axial stress. Figllfe
16 shows the relationship between the sttess obtained by the Kaiser effect and the time elapsed forsttain recovery after the stress was released. If thesamples are left for more than four days (recovery of
Direction I : horizontal direction
the residual sttain of about 25%), the stress estiDirectionD : direction inclined 1/4)[radmated from the Kaiser effect tend to be smaller withfrom horizontal
the increase of time period for strain recovery. From
Direction m: vertical direction
these results, we conclude that the duration forstrain recovery has the significant influence on the Fig. 18 Directions of shaped cylindrical specimens.
Kaiser effect
The duration of constant axial stress loading, for the experiments described above, was only about 10
days which is much smaller than the geological loading period of rockrnass. Therefore, the Kaiser effect andtime relationship was investigated for the samples which were stress-released after obtaining from the insitu. Samples used in the experiments were porphyritic granite, mudstone and andesite. Among the rocktypes described, porphyritic granite was used for the detailed investigation. Porphyritic granitic rock had
overburden pressure of 80 m depth and the horizontal drilling core bit of 75 mm were used to obtain thesamples. Figure 17 shows the schematic diagram of the boring location. Samples obtained from the field
were kept in a room with constant temperature and humidity. Cylindrical samples (dia 30 mm; height 60mm) were obtained with various length of recovery time from various. directions as shown in Fig. 18.
Samples were subjected to uniaxial monotonic loading immediately after recovery and the stresses were
estimated by using the Kaiser effect Figure 19(a) shows the result obtained after one day recovery and Fig.
t9(b) is the result obtained on the samples left for IO-day recovery. Figure 20 also shows a relationshipamong the axial stress, axial strain, and cumulative AE counts for samples obtained in Direction 1 of Fig.
t8. All these samples were subjected to uniaxial monotonic loading. Estimated stress from the Kaiser effect
was found to be 1.0 MPa for samples left for one day and 0.6 MPa for samples left for to days. It can beseen that the estimated stress is about half for the samples left for 10 days to that of samples left for only 1day. Figure 20 shows the relationship between the estimated stress by the Kaiser effect and the time after
the strain recovery. Triangles, squares and open circles are for the samples cut in I, II and III directions,

873

60
:>-<J

w

'040
:1

o

u

'020

~

0-0

t

I.LJ
<l:

Slress- strain
AE aCTivity
Kaiser effect

20'02<XX)

'"
E
:1
o

'"

~Icxx)
o
o
t-

Ul

~

_____________

o

3cxx)

/

<t

'"c::

20

Stress-strain
AE ac ttv i ty
Kaiser effecT

0.01
StrainJ~Q~JP9

0.02

( j')

_
003

e, %

Ul
Ul

...
Q)

U;
0.01
Strain

(a)

0.02
E, %

0.03

(b)

Fig. 19 Relationships among axial stress, axial strain and AE activity in a granite-porphyry specimen
shaped in the horizontal direction (direction I). (a) 1 day after rock sample coring. (b) 10 days after rocksample coring.
respectively (Fig. 18). For reference, results from Fig. 16 are also drawn in Fig. 20 (solid circles). Results
obtained in direction III (open circle) are for the strain recovery period less than 5 days. It shows that theestimated stress from the Kaiser effect is identical for samples cut in any direction. These results show
similar tendencies to those of solid circles in Fig. 21, though the magnitude is different. From the above
discussions, it is clear that the estimated stresses using the Kaiser effect tend to be smaller as the duration of
the stress recovery increases. Therefore, it can be said that once samples are recovered from the core, strain
recovery takes place. Hence, AE testing should be done within a short period of time. If this is not the case,the estimated stress using the Kaiser effect may underestimate the pre-stress.

5. Measurement of the Initial Stresses
The Kaiser effect is applied to estimating in situ stress. Eight samples were used to calculate principal
stresses. They are granite, granite porphyry, porphyrite, tuff, clay slate, mudstone (two samples), andandesite. Results obtained on granite, granite porphyry and andesite are shown in Fig. 21. Two dimensional
stress calculation were done on the granitic and granite porphyritic samples whereas three dimensional
calculations are carried out on andesitic samples.

6. Summary and Conclusions~:e
15.oe-........~--+---ti 10.0 I-----+-----"'-'<-r-..~--+---_T_-__l<II

5.0 1---~--......,....--.,.......-_;r_--1~&. : Direction

I. (Jz;;; 3.01------..,----;--- 0 : DirecTion n. Oll"t:l
0 : DirecTion m. Om
~ 2.0

I

I

;

1.0

I§

I.LJ

0

20

25

Fig. 20 Relationship between lapse of days andestimated stress by the Kaiser effect

For fundamental studies on the detennination of
the initial stresses of rockmass using the Kaiser
effect, we used eleven different types of rock (morethan 500 samples) and carried out various
experiments to examine various parameters on the
Kaiser effecL Following conclusions are drawn.
(1) Appropriate values of sensitivity and threshold are required to monitor the continuous
occurrence of AE Kaiser effect(2) All rocks examined show the presence ofthe Kaiser effect(3) The Kaiser effect mainly depends on theresidual strain in rock samples.

574

H =260m
01 =6.52MPa
03= 1.6OMPo=0.31 rode

H =80mO. = 2.12MPa
03=O.9IMPoe =Q.28rod

(a)

(b)

/

z

/

/

a
H =65m0, = 1.51 MPax

02= 1.40MPo
03=O.90MPo(c)

Fig.21 Estimated principal stress by the Kaiser effect. (a) 2 dimensional principal stress (tunne):granite I)(b) 2-dimensional principal stress (Dam:gtanite porphyry) (c) 3-dimensional principal stress (tunnel:
andesite)(4) Applied stresses in various directions have no effect on the stress estimated by the Kaiser effect on
any certain direction.
(5) Stress determined by the Kaiser effect tend to become smaller with the passage of time.
Using AE method to estimate the initial stress, it is possible to compare the results obtained in thelaboratory with field tests. Further research is required to find out how the water saturation affects the Kaiser
effecL Comparing with other methods, authors hope to establish a data bank to study the effective-ness of
AE method in detennining the initial stresses.

References
R.E. Goodman (1963), "Subaudible noise during compression of rocles", Geological Society of America
Bulletin, 74, 487-490.
875

H.R. Hardy, Jr. (1981), "Applications of acoustic emission techniques to rock and rock structures: A stateof-the-art review", Acoustic emissions in geotechnical engineering practice, ASTM-STP-750, ASTM,
Philadelphia, pp. 4-92.
E. Hoek and E.T. Brown (1980), "Underground excavations in rock", The Institution of Mining andMetallurgy, Chapter 4.
J. Kaiser (1953), "Untersuchungen tiber das auftreten gerauschen beim zugversuch", Archiv fur das
Eisenhuttenwesen, 24, 43-45.
T. Kanagawa, M. Hayashi and H. Nakasa (1977), "Estimation of spatial gee-stress components in rocksamples using the Kaiser effect of acoustic emission", Proceedings of Japan Society of Civil Engineers,No. 258, pp. 63-75. (in Japanese)
T. Kanagawa, S. Hibino, T. Ishida, M. Hayashi and Y. Kitahara (1986). "In situ stress measurement in theJapanese islands: Over-coring results from a multi-element gauge used at 23 sites", International Journal ofRock Mechanics and Mining Science, 23(1), 29-39.
K. Michihiro. T. Fujiwara and J. Yoshioka (1985), "Study on estimating geostresses by the Kaiser effect ofAE", The 26th U.S. symposium on Rock Mechanics, pp. 557-564.
K. Michihiro. H. Yoshioka, K. Hata and T. Fujiwara (1985), "Strain dependence of the Kaiser effect onvarious rocks", 4th Conference on Acoustic EmissioniMicroseismic Activity in Geologic Structures andMaterials, pp. 87-95.
Y. Mizuta, S. Ogino and O. Sano (1983), "A procedure for three dimensional stress determination by
hydraulic fracturing". Proc. of the 15th Symposium on Rock Mechanics, Japan Society of Civil Engineers,
pp. 116-120. (in Japanese)
S. Murayarna. K. Michihiro, J. Saito, T. Fujiwara, H. Yoshioka and K. Hata (1984), "The Kaiser effect ofa granite under various loadings", Progress in AE I/, JSNDI, Tokyo, pp. 586-593.
S. Murayama, K. Michihiro, J. Saito and H. Yoshioka (1985), "The Kaiser effect of granite cased byvarious loading methods", Proceedings of Japan Society of Civil Engineers, No. 364/III-4, pp. 107-112. (inJapanese)
S. Murayama, K. Michihiro, T. Fujiwara and K. Rata (1986), "Relationships between creep strain and
Kaiser effect on granite", Proceedings of Japan Society of Civil Engineers, No. 370/III-5, pp. 41-46. (inJapanese)
V.H. RUsch (1959), "Physikalische Fragen der Betomprufung", Zement-Kalk-Gips, I, 1-9.
K. Sugawara, K. Kaneko, Y. Obara and H. Okamura (1986), "Determination of the state of stress in rockby the measurement of strains on the hemispherical borehole bottom", Proceedings of the International
Symposium Large Rock Cavern. Vol. 2. pp. 1039-1050.
S. Yoshikawa and K. Mogi (1981), "A new method for estimation of the crustal stress from cored rocksamples: Laboratory study in the case of uniaxial compression", Tectonophysics, 74, 323-339.

576

u.s. Bureau of Mines Research on the Kaiser Effect for
Determining Stress in Rock
Michael J. Friedel and Richard E. Thill
The authors are afflliated with Twin Cities Research Center, Bureau of Mines, Minneapolis, MN 55417.

Abstract

The state of stress in a rock mass is of ubnost concern for mine design and stability analyses. Bureauof Mines researchers seeking a rapid and practical method of measuring stress in rock have investigatedacoustic emission (AE) and associated Kaiser effect phenomena in six rocks: SL Cloud gray granodiorite,
Barre granite, Dresser basalt. Salem limestone, Berea sandstone, and a volcanic tuff. AE signatures also
were used for interpreting the rock deformation stage and for classifying modes of deformation and failure inuniaxial compression. The advantage of using the stress-volumetric strain curve as an alternative to themore conventional uniaxial curve was demonstrated for correlating AE signatures with deformation and fracture processes in rock.
The Kaiser effect was shown to be capable of indicating pre-stress levels usually to within a few % inuniaxial compression tests. Pre-stress memory was shown to be retained in the rock for periods of up to atleast 5 months, the maximum period of testing. Confining stress also was shown to have a pronounced
effects on the Kaiser effect, increasing it with increasing confinement. Although Kaiser effect holds promisefor providing a less complex, indirect measurement of locked-in stress in rock, more research is required on
the effects of confinement and other environmental factors, such as moisture and temperature. for the accurate determination of in situ stress.
1. Introduction
Numerous approaches have been developed to determine in situ stress, but none are universally applicable in rock and all suffer from deficiencies and limitations. Technology is particularly deficient for delermining the stress state at depth in remote regions that are inaccessible from boreholes or mine workings. or forcorrosive environments, such as may be encountered in in situ leaching or waste disposal. Continued
research and development of new and improved technology is necessary for more accurate and reliable determinations of in situ stress in difficult mining situations.
A novel approach for determining in situ stress at depth or in corrosive and hostile environments is touse secondary effects of stress measurable by geophysical methods (Radcliffe et at.. 1986). The National
Research Council (1981) recently emphasized that geophysical methods likely will be the only tools forevaluating stress conditions at great depths where drilling is limited or precluded by costs. or where theinstallation of measuring devices might alter the ambient stress field. One of the more promising of thesetechniques relies on the so called Kaiser effect, obtained from measurements of acoustic emission (AE) instressed rock. Kaiser (1950) observed that AE activity exhibited an irreversible effect upon unloading andreloading metal specimens tested in uniaxial tension. Upon reloading a specimen. Kaiser noted that AE
activity was substantially quiet until the stress level of the previous maximum applied stress was exceeded.
This phenomenon. termed the Kaiser effect, suggests that previous maximum stress levels might bedetected by stressing a rock to the point where there is a marked rate of change in AE activity. Although the
Kaiser effect has been well established in metals. only limited research has been conducted in a few types ofrocks. Most notable is the recent work of the Japanese (Kanagawa et al.. 1976; Hayashi et al.• 1979; Kurita
and Fujii, 1979; Mogi, 1962; Murayama, 1985), Hardy et al. (1988. 1989) and Holcomb et al. (1983.
1985. 1986) at the Sandia National Laboratories. Little research, however. has been directed toward devising877

methods to apply this stress determination technique to practical mining situations and problems.
Two components of U.S. Bureau ofMines research are described in this repon: (1) To compare deformation modes and AE signatures in' different rock with those previously defmed by Mogi (1962) or Boyce et al.
(1981) for application ofpattern recognition technology to rock mechanics applications, and (2) to verify theapplicability of the Kaiser effect for predicting previous stress history in several types of rock common tomining. The results presented in this report represent an initial phase of research by the U.S. Bureau of
Mines on utilizing the Kaiser effect for in situ stress and stress change determinations in mining applications.
2. Experimental Procedures
Experimental procedures were implemented to provide more uniform testing procedures, to permit discrimination between AE signals and electromagnetic noise, to improve the signal-to-noise ratio, and tographically portray AE results to facilitate data interpretation. Several crystalline and sedimentary rocks wereselected for the experimentation. These included St. Ooud gray granodiorite. Barre granite, Dresser basalt,
Nevada volcanic tuff, Berea sandstone, and Salem limestone. Several of the rocks were included because they
were part of a standardized suite of rocks for which much propeny and petrographic data had been determined(Krech et al., 1974).
Cylindrical cores 2.54 cm in diameter by 5.08 cm long were taken in a single direction in rock blocks
to prevent variation in results due to anisotropy. Specimen dimensions and surface end finishing conformed
to guidelines for uniaxial testing recommended by the American Society for Testing and Materials (1970)
and the International Society of Rock Mechanics (1981). Precisely sectioning and polishing specimen ends
minimized the potential for noise generation due to platen seating at the specimen interface or other frictional movements due to end-surface irregularities. Rock cylinders were then weighed for initial density determinations and, subsequently, preconditioned in a vacuum oven at moderately elevated temperature ofl000C to remove moisture and obtain a standardized dry testing environment for the uniaxial loading experiments. Specimens were stored in a desiccator until the time of testing.
3. Mechanical Testing Procedures
Unconfined uniaxial compression tests were conducted using a closed-loop, servocontrolled materialstesting machine. Specimens were pre-loaded to levels of about 7 MPa for the crystalline rocks, and 0.5 MPafor the tuff and sedimentary rocks to initially seat the platens and close the loop for automated servocontrol.
Rocks were loaded at strain rates ranging between 2 x 104 and 2 x 1()6 per second, based on recommendations in earlier work (Boyce et al., 1981). Triaxial data are from the reanalyzed information presented in an
earlier study in which test procedures were similar to those used in the uniaxial tests (Thill, 1973).
AE sensing and processing was done with a wideband (100 kHz to 1 MHz) piezoelectric transducer andan AE analyzer, modified to operate in a frequency range down to 100 kHz. Silicone grease was used
between the aluminum front piece and the specimen surface to compensate for any asperities in the contact
surfaces.
The schematic for the AE detection system is given in (Fig. 1). Energy released in the form of elastic
waves in stressed rock is convened to an electrical signal by the transducer. This electrical signal is thentransferred via coaxial cable to a 6O-dB preamplifier and into the AE analyzer through an adjustable-gain,
linear amplifier, which provides up to 21 dB additional gain in 3-dB increments. The analyzer is equippedwith two floppy disk drives for the storage of AE data. External parametric input to the analyzer permits thesimultaneous input and plotting of load (stress) or deformation (strain) data as a function of the AE
response. Sampling rate for the parametric digital sampling was 1,000 samples per second. A digital oscilloscope is used for waveform capture and transfer to a computer.
The appropriate amplification level to provide the maximum amplification of AE signals, yet discriminate against transient and background noise was found to be 66 dB. To further discriminate AE signals from

878

Lo~

c.n

~

I
Rock
~

sP"'lI'Mn""'lal----tyariab~~.formallon
Iransduc...
Lat...alvariabJ.

I.formallon
Iransdvc.,.

Inc.~ctrtc

Hull1m.l...s

I

,-.t

For"

~

"""-I .fcrmalicn

I-

UI...al.'ormali4nH~Itc .mission ana~n~H

Di9llal DSCmCSCop.

Counls
E....nls
EnH'ftSIr.,sAxial slrain
Lal..~al sl~ain

Pnamp

I....tI~al\sclue.~

--l

Incl

ampllf....

Fig. 1 Acoustic emission test system.
15

3.0

12

2.4

9

1.8

6

1.2

3

.6

0

0

4.0

.25

3.2

.20

2.4

.15

1.6

.10

.8

.05

Barn 9rallll.

I"'l
0

vi

!z
w

>
.....
z

40

I:
w

120

u
IVI
~
Q

u

160

200

Dr.nH' basalt

Q

VI
VI

80

-<{

•

V'"

Ir"

..J

w

>
I-

0

0

-'
~
1:

2.0

30

u

1.6

24

1.2

18

.8

12

.4

6

100

200

300

400

~

180

240

300

<{

~

SI. CloucI 9r~

0

10

20

30

40

50

0

V IIlOdlortt.

60

120

STRESS.MPa

Fig. 2 Cumulative AE events as a function of stress for various sedimentary and igneous rocks loaded
under uniaxial compression.
background noise during the tests, analyzer threshold trigger levels were raised slightly above the background noise level. To discriminate AE signals from spurious noise. only those AE events having 5 or
979

F

z

Ruptur•• F

o

(f)
(f)

1:

Unsbbl.
'notur.
propagation •ti;

Slab!, orack gro....th.

w
u

D-F

~

C-D

«

S.millnurd,'armatiGn. B-Cou

Crack oloSlll". A-B

A~--------_..L-_---_--J

TIME, STRESS. OR STRAIN
Mogl-type !lcousttc
emission slgnolure (1)Bienl8wskl's
deformotlon process (~)

Fig. 3 AE response characteristics associated with the five regions of defonnational behavior. or the fracture process.
more counts (in excess of the threshold trigger level) and exceeding 100 kHz in frequency were used in thedata analysis.
4. AE Signature Patterns
Both general (primary) and specific (secondary) AE signatures were examined in the rocks tested to recognize diagnostic patterns related to rock behavior. Typical cumulative AE events in the six rock types as afunction of uniaxial unconfined compressive stress are given in Fig. 2. Several signatures or patterns wererecognized in the AE response curves. One type of primary-signature is referred to as the Mogi type of curve(Mogi. 1962), which relates the AE cumulative response to four generalized regions of defonnational
response for elastic-plastic materials.
The correspondence between the Mogi cumulative AE response signatures and Bieniawski classification
for the deformation process leading to fracture is given in Fig. 3. roUowing Boyce et al. (1981). In region I(A-B), crack closure andlor compaction occurs. resulting in a positive increasing slope in the cumulative
AE curve. In region II (B-C), the rock undergoes elastic deformation. with a correspondingly low. nearly
constant rate of AE. Hence. the cumulative AE curve in this region is nearly constant. Region III (C-D) is
typified by the onset of fracturing and stable crack growth and is represented by an abrupt increase in AE
activity (the Kaiser effect) with sharply increasing slope. Region IV (D-F) relates to critical energy releaseand unstable fracture in brittle materials. Region IV is typified by high rates of AE with steeply increasing
slope.
Boyce et a1. (1981) recognized variations of the Mogi pattern and re-classified signature responses forcumulative AE into four types (Fig. 4). The type I signature with four distinct slope changes was classified
as the Mogi signature. The type II signature was designated unstable, relating to the absence of the C-D
segment of the Mogi model. This implies that the material changes directly from linear elastic to unstablebehavior, without a phase of stable crack growth. The type III signature was designated dense by Boyce,
referring to the absence of the segment A-B of the Mogi model. The type III model implies that this type ofrock is compact (dense) in the initial stages of load application. The type IV signature lacks both the tlfStand third phases of the Mogi model and is designated dense, unstable, incorporating aspects of both type n
and type III behavior.
In applying AE signature classification to the rock tested, the Bureau found that all segments (slopechanges) of the Mogi type signature were present to some extent in each type of rock (Fig. 2). For some of
the rocks. however. it appeared that certain slope segments of the Mogi type of curve were almost absent.
S80

Type 1f

Type 11

f

z

2

l/l
l/l

1:
l&l

!::!

II. JL------~~----_:_t

til

:;)

o

U

<It

TINE OR STRESS

Fig. 4 Boyce signature classification based on shapeof cumulative AE response curve in different typesof rock (Boyce et al., 1981). Classifications: type I,
Mogitype curve; type II, unstable; type III, dense;type IV, dense unstable
Mechanicallesttng(various rocks)

1
Uniaxial andtriaxial comprasslon
loading1
Acoll3tic emission
Generation andcollection

1

Tralnlll9 lnput:
Acoll3t1c emission
respoMe curves
end other time orfrequencu allrl buies

1
Adaptive learnlll9
nelvorlc (trtlflclal
Inlelligence)

1

Predictive
cepabllltles:
Stress slale.
deformaliol\lll ,lll?e.
end rallure mechanism

resembling the Boyce types of signatures. The Berea
sandstone curve, for example, appeared almost linear
in the CD-OF segment and therefore could be classified as type I/-unstable in Boyce's signature classification scheme. Similarly, the St. Cloud gray granodiorite curve appeared to closely resemble the typeIII-dense signature. It was hoped that such AE pattern classifications might be useful in rock mechanics applications; for example, to indicate whether or
not a rock is burst-prone. Several problems werenoted, however, in attempting to use the Boyceclassification. Appearance of the curve shape, andtherefore its signature classification, was affected bychanges in the settings of the data acquisition andprocessing system. Setting the trigger thresholdhigher or lower or changing the sampling rate, e.g.,
could cause greater or less development of certain
segments of the curve. For instance, by changing
the sampling rate interval or the amplitude threshold, or by applying an initial preload to the sample,
the shape of the AE curve could be changed toreduce or eliminate the A-B and C-D segments ofthe signature. These problems indicate there isstrong need for standardization of the data acquisition and plotting parameters if AE pattern classifications are to be used in practical rock mechanics
applications.
In addition to the generalized (primary) patternof the AE response curve, which was shown to beclosely related to the deformation stages of rock at
different levels of stress, other diagnostic AE(secondary) signatures or patterns exist that are associated with the internal rock structure and mechanisms of failure. These patterns arise in the moredetailed AE rate response within each deformation
region and also from the acoustic waveform response associated with each event. Each type orfamily of rock appears, therefore, to have a distinct
pattern of AE that distinguishes it from others.
Some rocks are very noisy (high AE) in the early,
low-stress region of deformation, whereas others
may have few AE events in this region. Some have
low rate of AE into the very .high-stress region andthen suddenly burst with high rates of AE close to
the collapse stage (e.g., Dresser basalt). Other rocks(e.g., Berea sandstone or Salem limestone) may
have nearly constant rates of AE over wide ranges ofstress preceding failure. These individualized signatures for each type of rock are mainly diagnostic ofFig. 5 AE pattern recognition.
S81

500

KEY

30

n

u
400

Volumetric stress-

Acoustic emission
response curve

Ideollzed IIneor
Volumetric compression
o

!Z
U.I

"-

,

300

Crack cloJurt

24 III

>
U.I
Z

o

Q.

18

I:

~

F1

S

Stmntn.Ar
tJulio dtformltion

I:

(JI'
(JI

U.I

U.I

U

CI'

Ii;

li; 200

=:I

12

S
<

SIAb~cnck

voylh

U.I

>
~
....I

100

6 ~

=:I
U

o

o

2

3

4

D
IlIlpturt

5

STRAIN, 10 -3

Fig. 6 Cumulative AE events as a function of both axial and volumetric stress-strain for typical Sl Cloudgray granodiorite sample loaded under uniaxial compression.
the rock fabric, particularly microcracks, loading conditions, and mechanisms of failure (Thill, 1973;
Scholz,1968a, b; Brace et aI., 1966; Montoto et aI., 1984; Lord, Jr. and Koerner, 1983).
Since AE signatures are shown to be diagnostic of stress-strain behavior, the deformation process andmechanisms of failure in rock, computerized pattern recognition technology presents a viable approach forinterpreting the stages of stress and deformation, or mechanisms of failure in stressed rock. Some mining
applications of AE signature analysis and pattern recognition already have been described in Canadian
research (24). Figure 5 gives a flow diagram for a conceptual pattern recognition approach recommended by
the Bureau. This approach requires training input from the AE response curves or from captured AE waveforms, combined with the use of artificial intelligence to obtain predictive capabilities about the stage ofstress, and the defonnation or failure mechanisms in stressed rock.
Stress volumetric strain tests were conducted to correlate cumulative AE response to volumetric strain(Fig. 6). For brittle rock, initial nonlinearity in the stress-volumetric strain curve is associated with crackclosure and compaction. The initial deviation of the stress-volumetric strain curve from idealized linear
compression marks the onset of volume dilatation associated with crack formation. The transition fromstable to unstable crack propagation occurs at the reversal of slope in the stress-volumetric strain curve. The
volumetric strain categories are characterized in the cumulative AE events curve by abrupt changes in slope.
This could assist in stress-strain interpretation of AE in field monitoring situations. Since the onset of
dilatation and transition from stable to unstable fracture propagation are more apparent from the stress-volumetric strain than from axial stress-strain data, it is recommended that, whenever feasible, AE be plotted asa function of the stress-volumetric strain rather than the customary axial stress-strain curve.

5. Kaiser Effect Results
A typical AE rate response curve for the first cycle of uniaxial loading for Salem limestone (Fig. 7)
indicates several regions of defonnation: crack closure and compaction; semi-linear elastic; inelastic and
582

u

'So

"
'"
III
QI

I:lo

III

~

<10

C

QI

>
41

iii

~

30

ffi
~

:z:

20

0
....
tI)
tI)
....

is

...u

10

ti

;:J

0
U
0<

o

1.6

0.8

2.<1

3.2

4

STRAIN, 10- 3

KEYo(][) St~l. cr~.k Ilrowlh

Cnok ol.lur.

[ill StmlliNar tlulio dtrormalloft
Fig. 7 AE response and its correlation to brittle deformation criteria for typical Salem limestone sample.
failure (or postpeak behavior). The Kaiser effect, in which AE rate begins to increase above the backgroundlevel observed in the linear elastic region, occurs at a streSS level of about 19 MPa in this case. This apparently represents the peak, locked-in stress to which the rock had been subjected. Locked-in stresses similarly
determined for the six types of rocks in this study are given in Table 1.
Table 1 Peak stress history as determined by Kaiser effectRockClassification
Sedimentary

Igneous

Name

Salem limestone
Brea sandstone
Nevada tuff
St. Cloud gray granodiorite
Barre granite
Dresser basalt

Previous peakstress, MFa

Difference,
MPa

19

±t

31
33

±1
±2

liS
136

±S
±4

263

±12

Variation was as high as 12% in the Dresser basalt, but lower than ±5% for the other rocks. These
results support those of Holcomb (1983, 1985), Hardy (1985, 1988), and Kurita and Fujii (1979), whoestablished that locked-in stresses could reasonably be detennined by the Kaiser effect.
Since the Kaiser effect can sometimes be obscured by noise associated with crack closure or compaction
during the fIrst loading cycle, better resolution of the Kaiser effect can be made when such noise is sup
S83

..
...
u

"

.....

100

~

c
>

80

.

I

I

-

to

~

.....

40

...

0

III
III

...
is
...

u

"'"

....u

~

.c

.c

u
u

"'"

~

.. -

...
oJ

III

-

/

r

20

tJ

Iii:>

I

0

u

.c
0

-

>.

>.
u

>.

STRESS, KPa

t

z

>.
u

u

I

.....u

....u

>.

60

!oJ

>
W

..

...u.

W

I

.I'

225

I n cial!UU ....

III.~J U ..

......1· ..

.nn O

I'"

j'

'w I

450

675

900

1,125

TIKE. sec

Fig. 8 Reduction of AE events associated with crack closure. Subsequent cycling shows an advantage topreloading the rock into the linear elastic range to enhance Kaiser effect.
500

10

400

8

""'o
(,1)I-Z

UJ>

Axill ,tress-slreln curve

UJ

Z

300

o

Ullimote,Ire"
Prulreu

6

(/)

(/)

~
w

level

~

200

4

l-

(/)

:::>
o
u

-«
>
I-«
...J
UJ

100

2
AcoU1IiC embsion

response curve

:::>

o
o

1.2

2.4

3.6

STRAIN, 10 -

4.8

§
u

6

3

Fig. 9 Cumulative AE events as a function of stress-strain response for St. Cloud gray granodiorite under
uniaxial compression. Curve A-C represents initial stress-strain path taken; curve B is subsequent stressstrain path taken to failure. Curve D depicts cumulative AE response during the subsequent stress-strain
cycle.
pressed. This type of noise, fortunately, can be suppressed by subsequent unloading-reloading at stresslevels within the elastic range, thereby making the Kaiser effect more pronounced. This is illustrated in Fig.
S where it is observed that most noise is already reduced by the second loading cycle, with only minor
improvement in subsequent cycles.

S84

100

10

to"')

0
U>

I-

80

e

Axial strm-strai n curve

Z

UJ

>
UJ
Z

i1J

Q.

60

L

I-

U>

6

U>
U>

L

UJ

level

U>
U>

UJ
~

0

Ultimata
stre»
Prestress

U
IU>

40

4

::>
0

U

-<
>
I-<
-l
UJ

20

2

AcoustiC emission
response curve

o

3

2

STRAIN, 10

4

::>
L
::>
u

5

-3

Fig. 10 Cumulative AE events as a function of stress-strain response for Salem limestone under uniaxial
compression. Curve A-C represents initial stress-strain path taken; curve B is subsequent stress-strain pathtaken to failure. Curve D depicts cumulative emission response during the subsequent stress-strain cycle.
Examples of the sensitivity of the Kaiser effect corresponding to prestressed levels are given for St.
Cloud gray granodiorite (Fig. 9) and Salem limestone (Fig. 10). In both cases, cumulative AE was plotted
directly as a function of the axial stress-strain curve. The first loading cycle (A) prestressed the sample to a
known stress level. The stress was held constant at this level until creep occurred. The sample was thenunloaded (stress path B) and reloaded along stress path C while measuring cumulative AE (curve D). The
Kaiser effect is readily obtained from the point of intercept on the stress-strain curve associated with theonset in rate change in the AE curve. Table 2 gives results for Kaiser effect detenninations at several prestress levels in limestone and granodiorite. Stress levels could be reestablished to within a few % of the prestressed levels.
Table 2 Kaiser effect detection of prestress levels in Salem limestone and St. Cloud gray granodiorite
Rock

Salem
limestone
St. Cloudgranodiorite

Kaiser effectstress, MPa

Previous appliedstress, MPa

Difference,
MPa

37.5
47.4
58.2
132.0
143.0
285.0

39.0
47.0
57.0
136.0
142.0
278.0

- 1.5
+0.4
+1.2
- 4.0
+1.0
+7.0

An important question is whether the pre-stress memory of the rock is retained over periods of time oris only temporary. Uniaxial compression tests were conducted on several samples of Salem limestone thatwere all prestressed to the same level of 58 MPa. Subsequent loading cycles were performed on each sample

885

...0

2'S

P4

KEY

vi

Eo<

:z:

III

20

&l
:z:

...
... 150kl

Kaiser effect

i

Ul
III

stress path

~

...CJ
t;

::l

10

0
CJ

Acoustic emission
response curves

0(

III

...

>

j

5

::l

!!iCJ
0

20

60

40

STRESS,

80

100

MPa

Fig. 11 Cumulative AE as a function of stress for three Salem limestone samples. Each sample was loaded
to rupture under uniaxial compression at various time intervals after the initial prestress. Curve A-B represents initial stress path taken while prestress was applied. Curve A-e is stress path taken to detect Kaiser
effect (for three separate samples) at time intervals of 1 (kI), 3 (k3), and 60 (Ia) days following applicationof prestress.
to reestablish the pre-stress after periods of I, 3 and 60 days. Results showed that after I, 3 and 60 days, theKaiser effect predicted the pre-stress in aU three cases to within a few % (Fig. 11). Thus, even after periodsof up to 2 months. the locked-in stress in the rock could accurately be predicted using the Kaiser effect.
Another series of uniaxial compression tests was conducted using the SL Cloud gray granodiorite, prestressed to 130 MPa. Results showed that even after 150 days the Kaiser effect was clear, and that thelocked-in stress could be estimated (at 124 MPa). within 9% of the prestressed value. For these rocks. there
appears no evidence suggesting that the stress memory detected by the Kaiser effect fades within short periods of time. It remains to be established whether peak stress history is retained in rock over much longer
periods of time associated with geological events.
The Kaiser effect is known to be dependent upon the confining stress. at least in some rocks. Holcomb(1983) observed that the Kaiser effect increased nearly linearly. by roughly 3.4 MPa per megapascal increasein pressure. in Westerly granite for the range of confining pressures from 5 to 69 MPa. The Kaiser effect
was postulated to map a damage surface. similar to the Mohr-Cohlomb failure envelope. Accordingly, rockunder stress states bounded by the damage surface would Iemain stable (Holcomb and Costin. 1986). For
comparison. earlier Bureau results for AE response under triaxial loading of SL Cloud gray granodiorite(Thill, 1973) were reanalyzed. Cumulative AE response was determined at atmospheric pressure and confining pressures of 23, 46, and 69 MPa in conventional triaxial compression tests. A confining pressure wasapplied to the desired test level, and then an axial stress was applied to the point where the Kaiser Effectcould be observed from the text sample and continued through yield and failure (Fig. 12). The results plottedfor comparison with those of Holcomb and Costin (1986) for the Westerly granite (Fig. 13) show that the
Kaiser effect damage surface for the St. Cloud gray granodiorite increases roughly 7.9 MPa per megapascal
increase in confinement and has somewhat of a linear trend that roughly parallels the failure surface(ultimate stress). Bureau results for the SL Cloud gray granodiorite tend to support those of Holcomb(1983) in that both the Kaiser effect and the critical stress have a similar dependence on confining pressure.
Since the Kaiser effect is influenced by all principal stresses, as pointed out by Holcomb. field determina

886

700

T

600

T
"

"-

1:

500

vi
</,

."~

<100

v>

...J

::£
.-

300

...l::

200

Z

UJ

t

rY.

Ci

100

0

t

I

20

80

60

<10

100

CONfiNING PRESSURE, MPll

Fig. 12 Stress path used to induce damage in St. Cloud gray granodiorite sample to investigate usefulnessof Kaiser effect. while sample was under triaxial compression.
900

800

700

"

0..

I:

600

(~.

....
(~

g

500

(f)

J

.:!:

Z
....
cY.
....
'o.

....
(5

4(1)

KEY
JOO

200

$1, CIoul ....lI gr.-4lorll.

-0-hI.... ,'f~1 4f1'ct.4 'QIIf..........

1000.........-

20

40

Ie.......f'Ht4.'..ct.<Ifor......'.. . "'9"..n.(1")

hi""" ''''''W''or
It c.......

9""" 9""" It.

60

8t)

11)1)

CONfiNING PRESSURE. 11Pll

Fig. 13 Increase in ultimate stress and Kaiser effect as a function of triaxial loading for St. Cloud graygranodiorite.
tion of the state of stress using the Kaiser effect will need to separate the effects of the stress components.
Hence. more research needs to be done on this topic before the Kaiser Effect can be used with confidence todetennine in situ stresses.

6. Conclusions
The Kaiser effect was observed to be repeatable to within a few % for several types of brittle rock. The
AE activity displayed repeatable signatures (macro-patterns) diagnostic of deformation processes in the rock,587

suggesting that pattern recognition technology might be successfully employed to interpret the generalized
regions of deformation and associated stress in rock. It was found, however, that the shape of the AE
response curve can be sensitive to measurement settings, such as the sampling rate interval and the amplitude threshold, suggesting the need for standardized procedures when comparing AE patterns. First cycle
noise was successfully suppressed to enhance the Kaiser effect by cycling at low-stress level. Stress-strain
correlations for interpretation of the AE response were most comprehensive when the stress-volumetricstrain curves, rather than customary axial curves, were used for comparison. The Kaiser effect was shown toretain memory of peak prestresses over moderate periods of time up to 5 months. Kaiser effect also wasnoted to be highly sensitive to confining stresses, at least in some rocks, an item that requires further
research and consideration for the interpretation of in situ field stresses.

References
American Society for Testing and Materials (1970), "Special Procedures for Testing Rock for Engineering
Purposes", ASTM STP 479, Philadelphia, 529 pp.
J.F. Archibald, P.N. Calder, B. Moroz, T. Semadeni, and T.K. Yeo (1988), "Applications of Microseismic
Monitoring to Stress and Rockburst Precursor Assessment", Min. Sci. and Tech., 7, 123-132.
a.M. Boyce, W.M. McCabe and R.M. Koerner (1981), "Acoustic Emission Signatures of Various Rock
Types in Unconfined Compression", Acoustic Emission in Geotechnical Engineering Practice, ASTM STP
750, Philadelphia, pp. 142-154.
W.F. Brace, W.B. Paulding, Jr. and C.H. Scholz (1966), "Dilatancy in the Fracture of Brittle Rocks", J.

Geophys. Res., 71, 3939-3953.
E.T. Brown, ed. (1981), Rock Characterization Testing and Monitoring-ISRM Suggested Methods,
Pergamon Press, New York, 211 pp.
M. Hayashi, T. Kanagawa, R. Hibino, S. Matojioma and V. Tahara (1979), "Detection of Anisotropic
Geostresses Trying Acoustic Emissions, and Nonlinear Rock Mechanics on Large Excavating Caverns",
Paper in 4th International Congress for Rock Mechanics at Montreaux, Switzerland, A. A. Balkema,
Rotterdam, Netherlands, vol. 2, pp. 211-218.
H.R. Hardy, Jr. (1988), "Recent Geotechnical Applications of the Acoustic Emission/Microseismic
Technique", Proc. Conference on Nondestructive Testing and Evaluation for Manufacturing andConstruction (Univ. Ill. Urbana-Champaign, Aug. 9-12), pp. 189-201.
H.R. Hardy, Jr., D. Zhang and J. Zelanko (1989), "Recent Studies Relative to the Kaiser Effect in Geologic
Materials" Proc. 4th Conference on Acoustic EmissiOniMicroseismic Activity in Geologic Structures andMaterials (penn. State Univ., Oct. 1985), Trans. Tech. PubI., Clausthal, Federal Republic of Germany.
OJ. Holcomb (1983), "Using Acoustic Emission to Determine In Situ Stress: Problems and Promise",
Geomechanics, 57, 11-21.
DJ. Holcomb and L.S. Costin (1986), "Damage in Brittle Materials: Experimental Methods", Proceedingsof the 10th U.S. National Congress ofApplied Mechanics (June 16-20), ASME, New York, pp. 107·113.
OJ. Holcomb and RJ. Martin 1lI. (1985), "Peak Stress History Using Acoustic Emissions", Research andsaa

Engineering Applications in Rock Masses, ed. E. Ashworth (26th U.S. Symp. on Rock Mechanics, Rapid

City, SO. June 26-28). A. A. Balkema, Boston. MA, pp. 715-722.
OJ. Holcomb and L.S. Costin (1986), "Detecting Damage Surfaces in Brittle Materials Using Acoustic
Emissions". Trans. ASME, J. Appl. Mech.• 53, 536-544.
J. Kaiser (1950), "An Investigation Into the Occurrence of Noises in Tensile Tests or a Study of Acoustic
Phenomena in Tensile Tests". Ph.D. Thesis. Tech. Hochsh. Munich, Federal Republic of Germany. 190
pp. (Eng!. transl. from Lawrence Radiation Lab.• Livermore, CA, Rep. UCRLTRANS-I082-L).
T. Kanagawa. M. Hayashi and H. Nakasa (1976), "Estimation of Geo-Stress Components in Rock Samples
Using the Kaiser Effect", Cent Res. Inst. Electr. Power Ind. (Tokyo), Rep. 375017,26 pp.

W.W. Krech. F.A. Henderson and K.E. Hjelmslad (1974). "A Standard Rock Suite for Rapid Excavation
Research", Bureau of Mines RI 7865, 29 pp.
K. Kurita and N. Fujii (1979), "Stress Memory of Crystalline Rock in AE", Geophys. Res. Lett.• 6. 9-12.
A.E. Lord. Jr. and R.M. Koerner (1983), "AEs in Geologic Materials", J. Acoust. Emiss., 2(1), 195-219.
K. Mogi (1962), "Study of the Elastic Shocks Caused by the Fracture of Heterogeneous Materials and Its
Relation to Earthquake Phenomenon". Bull. Earthquake Res. Inst., Univ. Tokyo, 40, 125-173.
M. Montoto, LM. Suarez del Rio, A.W. Khair and H.R. Hardy, Jr. (1984), "AE in Uniaxially Loaded
Granitic Rocks", 3rd Conf. on AE/Microseismic Activity in Geologic Structures and Materials. eds. H.R.
Hardy and F. Leighton, Trans Tech Publications. Clausthal, Federal Republic of Germany, pp. 83-100.
S. Murayama (1985), "The Kaiser Effect of a Granite Under Various Loadings", Jpn. Soc. Civ. Eng.• 586593.
National Research Council. Panel on Rock Mechanics Research Requirements. Rock Mechanics Research
Requirements for Resource Recovery, Construction and Earthquake Hazard Reduction. Nat'} Acad. Press.
Washington, D.C., 1981, pp. 42-66.
K.S. Radcliffe, R.E. Thill and J.A. Jessop (1986), "Use of Acoustic Velocity for Predicting Stress Under
Uniaxial Compression", (Pres. at 115th Annual AIME Meeting, New Orleans. LA). Soc. Min. Eng. AIME
preprint 86-147, 11 pp.
C.H. Scholz (1968a), "Experimental Study of the Fracturing Process in Brittle Rock". J. Geophys. Res.,73. 1447-1453.
C.R. Scholz (1968b), "Correctional to "Experimental Study of the Fracturing Process in Brittle Rock", J.
Geophys. Res.• 73, 4794.
R.E. Thill (1973), "Acoustic Methods for Monitoring Failure in Rock", New Horizons in Rock Mechanics(Proc. 14th U.S. Symp. on Rock Mechanics), Am. Soc. Civ. Eng., 649-687.

SSg

Evaluation of Joint Properties of Anti-washout Underwater
Concrete by Acoustic Emission Measurement
Kazuya Miyano, Tatsuo Kita, Yuji Murakami and Takako Inaba
The authors are affiliated with Technical Research Institute, Hazama Corporation, Tsukuba, Ibaraki 305, Japan.

Abstract

Anti-washout underwater concrete, which has nonsegregating and self-leveling properties in water, hasbeen increasingly employed in underwater massive structures in Japan. However, in such structures, it isnormaHy required to form horizontal construction joints. In case of jointed concrete, it is difficult to elucidate the deformation behavior of the joint and to evaluate the joint properties by only rupture strength. In
this study, we perform direct tensile tests of the jointed segments by using acoustic emission (AE) and thetensile strain measuremenL This paper shows that AE propenies are related to the joint fajlure behavior, andthe joint failure could be located by using AE technique and strain measuremenL
1. Introduction

Anti-washout underwater concrete is expected to have greater joint strengths than conventional underwater concrete, because it has high resistance against material washout and hardly breathes (COTI, 1990).
Tests on large specimens have shown that the joint strengths (direct tensile strengths) could be further raisedby applying proper surface treatment to the concrete placed prior to the consequetive casting. Whenjointed
concrete is subjected to direct tension, however, the rupture energy due to the direct tension becomes concentrated on the jointed sutface, and cracks generated at one location join together resulting in rapid progressof the rupture. This makes it difficult to observe the deformation behavior during the rupture process and toevaluate the joint properties by only the rupture strength. In the present study, the joint properties are evaluated by using the acoustic emission (AE) method to track the behavior of the concrete leading to rupture,and to investigate the internal rupture behavior.
Due to the restrictions on the work underwater, good surface treatment prior to joint placement could behindered or interrupted depending on the types of structures, construction schedule and complexity of theprocedure. Investigation was made on the possibility for improving the joint properties by placement ofanti-washout undeIWater mortar prior to the joint placement, as an alternative to the surface treatment.
Experiments were carried out in two series. Series A was implemented along with direct tensile testingand some of the results on 91-day cured core samples taken from the jointed test specimens were alreadyreported (Miyano and Kita, 1989). The AE method was used in conjunction with the strain measurement toinvestigate the joint rupture behavior. In Series B, an attempt was made to locate cracks by using the AE
and strain measurement. This was to evaluate the rupture behavior in the vicinity of the joints which wascoated or not coated by mortar.
2. Behavior of Core Sample with Joints under Direct Tension (Series A)

2.1 Core Tests and Specimen Preparation
The surface of the large specimen used in the long-distance flow test (Kita and Fukudome, 1989) was
processed by 1) no treatment, 2) deck brush treatment and 3) high-pressured water jet treatment, prior to theplacement of concrete to the joint. The cement used was low-heat type cemenL Mixture of crushed sand andmountain sand was used for the fine aggregate and crushed stones (maximum size: 20 mm) for the coarse

590

Table 1 Mix proportion.
Series A
20mm

Max. aggregate size
Water/cement ratio
Sand/aggregate ratio
Unit weight (kglm3 )W: Water

C: Cement
S: Sand
G: Gravel

Water reducing agent
Superplasteizer
Anti-wash agent

Series B - Concrete
20mm

Series B - Mortar
68%

69%

68.8 %

40%

40%

221
320

220
320

485

654
988

649

1213

330

1012
0.004 xC

0.004 xC
0.02 x C
2.3

0.02 x C

2.3

Treatment Methods

0.004 xC
0.02 x C
4.0

Joining of NEW Concrete

deckblush--....&.~~-----------

<D

®

'"

Fig. 1 Treatment methods and joint placement.
aggregate. The admixtures included air-extrained water reducing agent, superplasticizer, and anti-washout
agent (cellulose-based). Mix proportion is given in Table 1. The concrete satisfied the conditions of a slump
flow of 52.5 em and air content of 4 %. Core samples were taken at 14 days after joint placement made in acylindrical shape (15 cI> x 30 cm), so that the joint surface was set at the center of a sample. Direct tensile
test was perfonned after 91 days. The surface treatment methods and the placement ofjointed concrete areshown in Fig. 1.
2.2 AE Measurement and Loading Method
AE measurement was carried out for tracking the behavior of internal rupture in concrete to detect slip,
separation and rupture inside the core samples. Although compressive, bending and splitting tensile tests on
cement paste, mortar and concrete were extensively reported (Uomoto, 1986; Kodama and Nakasone, 1986),
few reports have been made on the direct tensile strength test.
In the experiment, after drying the bonded surface of the specimen, jigs were attached with epoxy bondas shown in Fig. 2. Thus, rings were attached at the top and bottom, ensuring no eccentricity and applying
controlled tensile load (l to 1.5 tlmin.). For strain measurement of joint and monolithic portion, three 60
mm strain gauges were attached to each specimen at 30 mm intervals with the middle gauge covering thejoint. These were numbered 1, 2 and 3 from the top to the bottom. On the joint, 2 mm gauges were also
attached at 4 locations. Locations of strain gauges are shown in Fig. 2.
The AE monitoring system is illustrated in Fig. 3. AE sensors with resonance frequency of 150 kHz
were attached directly to the side of the specimen at 4 locations with grease and the AE events in the central
20 cm section of the specimen were counted. For AE detection, the amplifier gain at a pre-amplifier and amain amplifier was 40 dB, and the threshold value was set at 1 V.

591

Ring

..

Booo...-----------...

Z

5u 6000

NEW CONCRETE

Au Sensor

Strain Gauges(60 mm)
No.1
No.2
No.3

w

<
-1
<

Strain Gauges(2 mm)

bl-hi 'pressured "a ler
jot t roslment'000 _<

deck blusht roa lment

OLD CONCRETE
DI.--'==:::::::=;&,.....--~-~
D,S.D

Fig. 2 Direct tensile test.

STRESS RATIO(" ,I" ,•.).,....o TENSILE
0,...0;:=----°";',5'--=;1.0x......

~

Untreateddeck blush
SO - trealment
hi-pressured "aterjet treatment
IDll-

Unjointed~i
PREAMP

Fig. 6 Direct tensile test results.

Fig. 3 AE measurement system.

\--1/~ 0.04 Untroated!z O. 05

W

.....

~
:;)

8

jt- ... /

---

."""

UJ
<I(

... - .. 6Qrrm IBUI8(11)1111>3)/2

..J

g
.....

g, O. 01-,6~ lOUI'
o O··'--~_-.-_...----r-_-.--._Mo._2-.--.'o O. 2 O. 4 O. 6 O. 8 I. 0 1. 2 \. 4 \. 6
TENSILE STRESS (MPa)Fig. 4 Relationship between tensile stress anddisplacement.

~ 0.07r-----------,...... 0.06
J.
//.
!zw 0.05 Untreate d II Deck Brush
Unjointed:E 0.04~ 0.03~ 0.02~ 0.01
Water Jotc

7

Ul/.--~',.'"

0.03

u~ 0.02~~Z~Unjointed

Deck Brushu»'.------------,

ii)~

E O.07,-----/:---If-l\-t-or-J-et---,~ 0.06

2 ~

<2
a: x
In .....

0 -JoolGiiG:j-o...-...,r=::::.,;-j~-:.:-:.;:-~-.=..- ;:..:--=--,.---,.-~o O. 2 O. 4 O. 6 O. 8 \. 0 1. 2 1.4 1. 6
TENSILE STRESS (MPa)Fig. 5 Relationship between tensile stress anddisplacement.

1L......-====n====91•D
TENSILE STRESS RATIO (" ,/ " • _)Fig. 7 Relationship between total AE countper micro-strain and tensile stress ratio.

2.3 Tesl Results(l) Separation at Jointed Surface
The relationship between the tensile stress anddisplacement around the joint surface is shown forstrain gauges attached at different locations in Fig.
4. The variation of gauge displacements is larger
and their values are greater in the vicinity of thejoint than those in the monolithic portions whensurface treatments other than water jet were applied.
The relationships between the tensile stress anddisplacement around the joint sulface measured by a
2 mm gauge,and by the No. 2 gauge are shown in

S92

STEP ISTEP n
0.

STEPm
N

I

~

Rupture

~~=ld
Ei

e

L... --'

<:>
<:>

c

o

.....

I

..,

<)
Q)

s:a.
tn

II)

C
Q)

....

e

e
e
c;>

Joint Surface~! ~lO-0

Fig. 8 Joint rupture process model.
Fig. 5. In the cases of concrete untreated and treated with deck brush prior to joint placement, the displacements on the 2 mm gauges increase up to a half of the displacement measured by the No. 2 gauge. The
increase of the displacement is even smaller in the case of concrete treated with water jet. It implies thatwhen the direct tensile strength is small, concrete in the joint surface section could deform only half thedisplacement inside the 60 mm section. Thus. the separation readily occur at the joint surface under direct
tensile force. With water jet treatment, on the other hand, the displacement measured on the 2 mm strain
gauges accounts only for about 2 % of the total displacement in the 60 mm section, indicating that the load
is transferred to the whole section.
(2) AE Counts
The relationships of the total AE counts and tensile strain versus tensile stress ratio are shown in Fig.
6 where the tensile stress is normalized by the strength. Few AE events were observed in the specimen withuntreated surface, while sudden emission was observed just prior to rupture in the specimens with deckbrush treatmenL Significant AE events were observed with water jet treatment, with AE activity gradually
increasing after the tensile stress ratio of about 0.5. The monolithic concrete specimen showed a similar
pattern of the AE increase to the water jet specimens. with sudden AE increase around 0.8. AE generation
was also observed at low stresses. This may result from the engagement of the bonding surface, jigs andrings.
It has been reported (Ohtsu and Shimizu, 1986) that, in uniaxial compression tests. sound samples
generate no AE at the initial stage of loading, but immediately prior to the ultimate failure. Opposite
results were obtained in the present experiments. Under the direct tensile test, occurrence of AE was observed at the initial stage of loading in the specimens without joints, while AE activity was generally small in
specimens with joints. Investigation was made on the relationship between the tensile stress ratio and thetotal AE count per micro-strain on the No.2 gauge (cumulative AE/e).
Results are given in Fig. 7. The total AE count per micr<rstrain increases with the direct tensile
strength at the initial loading stage. Concrete specimens with different treatments show constant values in acertain range between 0.3 and 0.9. This implies that the tensile stress is concentrated on the joint surface
when the direct tensile strength is small. As the behavior of the specimen away from the joint is similar tothat of a monolithic specimen. the region resisting the tensile stress extends into the region away from thejoint surface and the total AE count per micro-strain increases.
From the above results, in the direct tensile tests on jointed concrete specimens, the increase ofultimate rupture strength can be expected from the occurrence of AE at the initial loading stage. The morethoroughly the joint surface is treated, the further the region in which the tensile stress is supported extends

S93into me region away from me joint surface. Thus, me behavior of me specimen starts to resemble mat ofme jointless monolithic specimen.
(3) Separation Model of Joint Surface
From the results, the rupture process of joint surface is modeled as shown in Fig. 8 . In Step I, thetensile behavior (displacement) in me jointless specimens is identical to that of the jointed specimen. In
Step II, the separation starts gradually at me joint surface in the jointed specimen. The AE sources areconcentrated in me joint zone. In the jointless specimen, AE is observed at the entire domain of a specimen.
As a result, a higher number of the AE events is observed in me jointless specimen.
3. Joint Behaviour ot Small Specimens under Direct Tension (Series B: Evaluation ot
AE Location)
3.1 Malerials Used and Mix Proportions
Mix proportions of anti-washout underwater concrete and mortar are given in Table 1. Cement used was
the same low-heat type cement as that used for the Series A test, but 4.0 kglm3 anti-washout agent wasadded to mortar.
3.2 Specimen Preparalion
Four types of small jointed specimens were prepared, with different surface treatment and me placement
method as test factors. Joint properties were examined by means of the direct tensile test as in Series A. In
preparing specimens, concrete was placed underwater in forms of 15 ~ x 15 cm container with the waterlevel at 20 cm. All specimens were removed from me form after 7 days. Specimens to be subjected to thesurface treatment underwent the slime treatment (2 to 3 cm removed) with wire brush to prepare the surface
for the treatment. The specimens of the surface treatment were placed back into forms of dimension 15 ~ x
30 cm together with the untreated specimens and cured for another 14 days. Then, the joint placement was
implemented. The joint placement was carried out either by simply placing the same concrete as in Table 1(Case C) or by placing 5 cm layer of mortar and then casting joint concrete an hour or so later (Case M).
3.3 Direct Tensile Test Method and A£ Measurement
The direct tensile strength test was implemented to examine the relationship between the strain and thelocation of AE sources. Strain gauges are attached to the specimen as shown in Fig. 9 at the age of 56 to
60 days after joint placement. The specimens were taken out of the water tanks 5 to 10 days before the test
and dried for 1 day before the jigs were attached to the top and bottom surfaces of the specimens with epoxy
adhesive. The loading rate of the tensile tests was 1 to 1.5 tlmin. The AE sensors, with resonance frequencyof 200 kHz, were attached to the positions shown in Fig. 9, by applying grease to me sensor surfaces andfixing them down with rubber bands. AE measurement system is illustrated in Fig. 10. After increasing theamplitude by 40 dB with pre-amplifier, it was further amplified by 60 dB in the AE signal processor. The

Ring

+p
CH.1

;r-~ID~~

CH.8

AE Sensor.. ---- .....

JOINT
OLD CONCRETE

Fig. 9 Direct tensile strength test.

SURFACEOAR Sensor

+p

••

·

AE SIGNAL
ANALYZERDISK

Fig. 10 AE measurement system.

S94

AE SIGNAL
PROCESSOR

M-Untreatedz~~!

100 Ollorlar • Concrete. Untreated• Concrete. Untreatc4
6 110rtar • Concrete. Trealed·Concrele. Treated

P<D.8

gOO

~

6

~ -450

~ -9
III

o

o

3~90

1.0

1.5

~ 0
w
0-45

Fig. 11 Relationship between tensile
strength and total AE count.

III~ -9 0

zg

z

ft

P<0.8

0....

t:
~

I..~

0.B<P<0.8

0 0

0"'" 0, [J:
o.€

•

~

() f:l . ':I'\'\t-'l00 50 100 150200 ~r:.\"~TENSilE STRAIN '\~(x 10'·)~ 90

5 10 15 20 ~"'\"~TOTAL AE COUNT '\~M-Treated

0....

t..

~\.~~~~

O.95<P

Fig. 12 Three dimensional evaluation of AE source
location.

C-Treated~

0,!3rc,"''''

00
00
0B

0.8<P<o.95

9 45

DIRECT TENSILE STRENGTH (MPa)I;;

0 50 100 150 200 ~9"~ I..~TENSilE STRAIN ~

oli

o

3-9

() \~ (,,9()~ ,. "'~~~""

z~

o
0.5

D.8<P<llJ

'

•

~

0

0.9<P<D.95.

oo.95<P.".'" [J: 8'

f:l ,. ",'\~~~o"l-9
5 10 15 20 o.~*-~ \:~ TOTAL AE COUNT '\~~

0..·'.

lI.95<P.,

B

Fig. 13 Three dimensional evaluation of AE source location.
signal data exceeding the threshold value of 0.4 V were analyzed in the AE signal analyser. Although theerror in evaluation of AE locations was found to reach 4.1 em in the vertical direction outside the region
surrounded by the sensors. the error in the section surrounded by the sensors was at most I em. Thus. locations of AE sources were taken into account only in the section to which the sensors were attached.
3.4 Test Results(1) Direct Tensile Strength
The relationship between the total AE counts and direct tensile strength is shown in Fig. 11. In the
direct tensile tests of the untreated specimens (Case C-untreated). rupture occurred where the jigs wereattached, where the rings for the tensile strength test were placed. and at the joint upon loading. In theuntreated mortar-concrete joint specimens (Case M-untreated). tensile strength could be determined. Observation of the rupture surface showed that both the upper and lower rupture surfaces were slimy in Case Cuntreated, while mortar could be observed in circular spots at the center of the surfaces in specimens forCase M-untreated along with some slimy parts. Although the direct tensile strengths could not be com-995

pared, it was concluded that the joint properties were improved by using mortar in joint placement
There was little difference in the direct tensile strengths between specimens for Case C- treated and Case
M- treated, indicating that the use of mortar makes little difference when the surface is treated prior to joint
placement.
(2) Evaluation of AE Locations
The results of the evaluation on locations of AE sources for Cases M-untreated, C-treated, and Mtreated are given in Figs. 12 and 13. The counts taken in these experiments were generally low. Even in thecase of M-treated which gave the largest count, AE was detected only after the stress ratio had reached 0.9,
making it difficult to investigate the rupture process. In all cases, in the vicinity of the joint surface, AE
could be detected from the initial stages. From the results of Series A, it is found that AE events areconcentrated on the joint surface. The strain measured at various points on the specimens is closely relatedwith the total AE counts. The specimen joined with mortar provided active AE generation on the joint
surface immediately before rupture. This is because mortar resists the direct tensile stress as the bonding
material between the concrete, and becomes subjected to cracking separation.
4. Conclusion

The results obtained from this study can be summarized as follow:
In the behavior ofjointed specimens under direct tension, the AE count increases as the joint properties
are improved by the surface treatment.
2) Thorough surface treatment increases direct tensile strength and result in increased AE per micro-strain.
It implies that the tensile force is not concentrated on the joint surface but is resisted by the whole
region around the joint surface.
3) In specimens without surface treatment, the joint properties can be improved by inserting mortar
between the concrete. When thorough surface treatment is applied, however, the effect of the use of
mortar on the direct tensile strength is small.
4) The joint rupture surface under the direct tensile testing can be estimated through evaluation of AE
locations.
1)

References
The Coastal Development Technology Institute (COTI) and The Japanese Institute of Technology on Fishing Ports and Communities (1990), Anti-washout Underwater Concrete Manual, pp. 5-32 (in Japanese).
T. Kita and K. Fukudome (1989), "Large Scale Flowing Experiment Report of Low-heat Type Antiwashout Underwater Concrete (No. I)," Proceedings of the 44th Annual Conference of JSCE, 5, pp. 258259 (in Japanese).
K. Kodama and S. Nakasone (1986),"AE Properties of Mortar and Concrete," 006 Committee Report, No.
87, Japan Society for Nondestractive Inspection, Tokyo, pp. 1- 6 (in Japanese).
K. Miyano and T. Kita (1989), "Joint Properties of Low-heat Type Anti-washout Underwater Concrete,"
Proceedings ofthe 44th Annual Conference ofJSCE, 5, pp. 264-265 (in Japanese).
M. Ohtsu and E. Shimizu (1986), "Estimation of Deterioration of Core Samples by AE Properties," Proceedings of the 41st Annual Conference ofJSCE, 5, pp. 117-118 (in Japanese).
T. Uomoto (1986), "Nondestractive Inspection Technique by AE," Concrete Journal, 24(3), 56-58 (inJapanese).

596

Automated Determination of First P-Wave Arrival andAcoustic Emission Source Location
E. Landis, C. Ouyang and S. P. Shah
The authors are affiliated with NSF Center for Science and Technology of Advanced Cement-Based Materials,
Northwestern University, Evanston, Illinois, USA

Abstract
An attempt was made to improve the accuracy of AE source location analysis by improving the accuracy of the arrival time routines. The algorithm developed was coded and implemented on a microcomputerfor fast data analysis. The motivation being that as less human interpretation is required for data analysis, agreater volume of data can be used and therefore hopefully more useful information can be extracted from an
AE test While it is recognized that many factors other than arrival time influence the accuracy of AE source
locations, (such as P-wave velocity and isotropy, signal attenuation and reflections, and transducer size andconfiguration), a reduction in arrival time error leads to an immediate and substantial improvement in theprocessing of AE data.

1. Introduction

Measurement of acoustic emissions (AE) is becoming an increasingly popular technique to evaluate

damage in quasi-brittle materials such as concrete. Recent developments in data analysis allow a muchclearer picture as to how AE signals relate to different damage mechanisms. Deconvolution and moment
tensor analysis can give an accurate description of source-time histories (Scruby et aI., 1985; Micheals eta!., 1981; Ohtsu, 1989). The location of AE sources can be related to areas of greatest damage, and could
potentially trace the movement of the fracture process zone.
Obviously the usefulness of an AE source location is increased when uncertainty in its accuracy is reduced. Not only is an accurate source location in itself of interest, but quantitative measurements on crack
size and orientation depend on an accurate source location. Several very good methods for determining thesource location have been established and are widely being used (Budavari, 1971; Salamon and Wiebols,
1974; Ohtsu and Ono, 1988). These routines require a series of arrival time differences to deduce the source
location. The location is generally found by minimizing an error term in a series of linear or nonlinear equations. An improvement in arrival time accuracy will lead to a faster convergence rate and a better estimate.
Recent advances in AE data acquisition systems have made it possible to collect very large amounts of
AE data. A typical test where 500 events are detected will produce 4000 separate time histories for an eightchannel system. It is quickly realized that for meaningful interpretation of data, the postprocessing of AE
signals must be automated. The motivation for this study was to assemble suitable routines for the automatic analysis of AE source locations, and to serve as a platform for more advanced source characterization
routines. It should be noted here that all of the following work was based on postprocessing of digitized
data. The digitizing rate of the data acquisition system was 16 MHz with 8 bit resolution.
2. Time of Arrival
2.1 Determination ofArrival TimeAn important but not very well documented task is the determination of the first P-wave arrival time.
Estimates of AE source locations are not meaningful unless a consistent algorithm for arrival times is in597

place. Visual inspection of AE signals is appropriate only for a small number of well defmed events. Thismethod, while useful as a check, is too sensitive 10 the subjectivity of the inspector, and is not adequate forprocessing large amounts of data. For both consistency and volume capacity, a computer aided method must
be used.
Maji and Shah (1988) used a statistical analysis of noisy signals to estimate time of arrival. An arrivalis detected when the signal exceeds a prescribed limit based on the standard deviation of the initial (noiseonly) part of the signal. An AE signal is not detected unless it exceeds a minimum of plus or minus threestandard deviations of the mean. While the routine works quite well for signals of sufficient amplitude, problems occur when the signal does not exceed the prescribed cutoff. Also, signals with a slow rise time may
not cross the threshold until well into the AE signal.
Claxton (1990) used a linear prediction (LP) filter to determine arrival times. An LP filter can be written as:N

Yn= rAYn-j+ XI
j. I

In this equation the value yo is a predicted value based on the previous Yn-j data values. The coefficients dj
are determined by power spectrum estimation (Press et al., 1986) of an initial segment of the signal. Oncethese coefficients are established, they can be used to predict future values of a series based on N previous
values. The Xn term represents the error between the predicted value and the actual measured value. When an
AE signal arrives, the filter is no longer able to predict the future values, and the error term rises. Arrival
time can then be defined as the point at which the error passes above a prescribed threshold.
While this method works quite well for determining arrival times of some types of signals, it had difficulty with signals having a slow rise time. If the signal changes gradually, the filter can respond to thesechanges and actually predict continued increases or decreases in signal amplitude. It was concluded that thismethod was not robust enough for the wide variety of signals normally encountered in AE measurements.
2.2 Proposed Filters;
For a robust arrival time algorithm to work well for a wide variety of signal types it must be able toaccomplish two things: reduce the effect of noise, and enhance the signal so as to produce a well defined
point which indicates the start of an AE evenL An adaptive moving average filter was found 10 be quite effective at removing random noise, and a Laplacian filter was found to enhance a given signal for easy detection.
An analysis of the electrical noise showed not surprisingly that it is essentially a random process. The
data acquisition system was triggered to record data records which contained no signal (only noise). A typical autocorrelation of one of these signals is shown in Fig. 1. The autocorrelation, C(l, 't) of any function,
f(t), is a measure of the influence or correlation between f(t) and f(t + 't) or f(t - 't). For a stationary process

-1000

o

1000

Fig. 1 Autocorrelation function for a signal with noise only.
598

(one in which statistical properties are invariant to a shift of the time origin), such as electrical noise, autocorrelation depends only on t, and therefore can be written C('t). An ideal random process has an autocorrelation which approaches a Dirac delta function, Sex). The Dirac delta function is defined as infmity at x = 0
and zero for all other x. The autocorrelation shown in Fig. 1 is sufficiently close to a delta function to conclude that the electrical noise is an essentially random (white noise) process.
Since the noise is a random process, simple data smoothing methods can be utilized. One such method
is a moving average fIlter. For each poim in the signal a new value is assigned based on the average of thesurrounding points. A moving average filter can be represented as:f\Yet) = _1(t - 1) d1
2T -T
where 2T is the width of the filter window centered at L For the discrete time case the moving average canbe written as:
1T
Y (t~ = - - 2, x (ti.)

2T + 1 j ••T

This ftlter effectively removes random noise by 'averaging' it out over a span of 2T.
A fundamental problem with a moving average ftlter is the smearing effect it can have on the actual signal.
A moving average is a form of a low-pass frequency filter. If the width of the averaging window is set toolarge, the useful part of the signal can be distorted. In order to minimize this smearing effect, an adaptive
moving average ftlter was applied. In an adaptive moving average, the width of the averaging window varies
according to characteristics of the signal in the neighborhood of time, L For this study, the window width ateach time point was determined by:I
T(t~ = ----:::2

a + bcrwhere cil is the variance of a window of fixed width centered at tie The constants a and b were determined by
setting a maximum T of 16 points (1 ~ ) for a variance of zero, and a minimum T of 3 points (0.1875 ~)for a variance of two or greater. These maximum and minimum values for T were established for their desirable effect on the signal. The effect of this adaptive moving average filter is illustrated in Fig. 2.
The second stage in locating a precise arrival time is to enhance the signal so that the possible times ofarrival can be reduced to a few data points. An effective method for this purpose was taken from an image
analysis routine designed for edge detection in 2-dimensional video images. In this routine a new signal, Yet)is generated from the old signal, x(t) such that:yet) ::: x(t) - V2 x(t).
The effect of this operation is illustrated in Fig. 3. As can be seen in the figure, the leading edge of the
wave front becomes sharper, and the amplitude becomes larger than the original signal. This effect allows
the signal to be more easily spotted by a computer algorithm. The arrival time algorithm was coded so thatthe arrival time was defined as the minimum (or maximum) point before the first wave front.
The effect of this procedure was to reduce the possible arrival time to a few data points. All signals
possessed the small characteristic trough (or crest) before the wave front. The width of this trough is generally small so that the arrival time can be fairly precisely defined. This operation is illustrated in Fig. 4 foran actual AE event.
The method used in this study to estimate the time of arrival can be summarized as follows.
1. The signal is "smoothed" using an adaptive moving average filter.
2. The second derivative of the smoothed signal is calculated using a second order finite difference
formula. The second derivative is subtracted from the original (smoothed) signal to get the"filtered" signal.
3 . The leading edge of the flI'St peak in the filtered signal (point B in Fig. 3) is located by estab699

lishing a "trigger" level for that signal, and by noting the point where the filtered signal exceedsthat level.
4. The arrival time was defmed as the point where the filtered signal was a minimum (or maximum) just before the peak found in 3 (point A in Fig. 3).

100

60

amp\.

ImV)

OrigInal Digitized Signal
Smoothed Signal

o

·20

·18

·14

·16

·12

time I J1S1

Fig.2 Effect of adaptive moving average fllter.

Vf

f(x)

--x

1---------x-f----'---"tr-........

f.-If

Vf

__x

1----:O"'--~-t---~r-----x

polntA(anival time}

Fig. 3 Effect of a LapJacial operator applied to a smooth I-D function.
3. Verification
3.1 Arrival Time Verification

In order to verify the accuracy of this approach, an array of AE sensors was mounted on a steel plate as
shown in Fig. S. Since the locations of the source and the receiver were known, and the velocity of a p.
wave for the specimen was known. any error in measured arrival time can be attributed to error in the arrival
time routine. Two different sources were used; fracture of a 0.5 mm pencil lead. and a 5 nun diameter steel
ball dropped from a height of approximately SO mm. The Iheoretical difference in arrival times can be computed by taking the difference in ray path lengths between the source and the receiver. These differences intheoretical arrival times were compared with the measured values to evaluate the accuracy in the method.
The pencil lead break generated avery sharp wavefront, and the difference between the theoretical andmeasured arrival time differences was generally zero, but in almost all cases was Jess than ±O.l2S I..I.S (2 data
points). The steel ball impact was not quite as sharp as the pencil fracture, but the error was still generally
less than ±O.187S I..I.S (3 data points).

S100

arrival rime

200

amp\. 100(mV)!--........:=::::=:::=-----=::::3:J

---

o~---

·100

smoothed Signal. y(t)y"(t)y(t) - y"(t)

·20

·18

·16

·14

Ume (JJS)

Fig.4 Laplacial filter and arrival time.
SourceSteel Plate

cc 594Om/s

Transducer 1
tl = arrival Umetransducer 1

Transducer 2
t2 = arrival Umetransducer 2

TheoreUcal difference In arrival Umes: At '" t I • t2" !l..:...!2.
c
Average error between theoreUcal
and measured arrival Ume differences: 0 Jl8: lead penctl fracture
0.1875 Jl8: steel ball
impactFig.5 Verification of arrival time routine.

3.2 SOUTce Location Verification
Since the ultimate goal of this study was not to determine arrival times but rather to improve on AE
source location estimates. a test was run to measure the accuracy of the source location routine using the
newly developed arrival time routine.
The method used to locate the source is an iterative technique which solves the following equation:(lrj - rl -Ir·· rO
AtijJcwhere Atij is the arrival time difference between the i-th andj-th transducers. r. rio and rj are the locations of
the source. i-tho and j-th transducers, respectively. and c is the P-wave velocity in the material (assumed
isotropic). A minimum of four transducers is required to solve this equation in 3-dimensions. If more thanfour transducers are available. a minimum error approach can be used to solve the equation.
A normal strength concrete cylinder was fitted with an array of five AE transducers as shown in Fig. 6.
The same artificial sources used on the steel plate were applied to the cylinder at the locations shown in the
figure. The source location was then estimated using the above procedure. The error between the measured
and actual source locations averaged less than ±1.8 mm for the lead pencil break. and less than ±3.0 mm forthe steel ball impact.
C

S101

Sources

-

o

x

III
5 Transducers
Sources:- .Smm lend
pcncll fl-aclurc• Steel ball Impact

o

o
III

III

I.

.1

Location found by minimizing: l\tg =~rl - If ~ II) -~l
Average error bctwcen aClualand measured source locaUon: 1.8 nlln lead pcncil fraclurc
3.0 mm steel ball hnpacl

Fig. 6 Artificial AE source locations in a concrete cylinder.

. t··.

• . \;ti,.

··

+
+
+

• .+ +"t

+

•

+ +

+ •

Fig. 7 2-D source locations for center and off center notched mortar beam specimens.
4. Application to Actual AE Signals

The new arrival time/source location method was used to analyze data from a previously conducted mortar beam tesL A center-notched and off-center-notehed standard mortar beam was subjected to four point load
8102

ing as shown in Fig. 7. Due to equipment constraints, the test was set up for 2-dimensional AE analysis.
The data was analyzed to determine the AE source locations. These locations determined are ploUed in Fig.
7. This data had been analyzed earlier for source locations using the time of arrival technique of Maji and
Shah (1988). While differences in individual AE source locations were noticed, the major influence of the
new routine was the usefulness of more data. In the original analysis roughly 25% of all detected events had
to be discarded due to either ambiguous arrival times or for failure of the source location routine to converge. For the new routine this number was reduced to about 15%.
Acknowledgements
The research reported herein was supported by a grant from the U.S. Air Force Office of Scientific
Research under a program managed by Dr. Spencer T. Wu.
References
C.B. Scruby, G.R. Baldwin and K.A. Stacey (1985). "Characterisation of fatigue crack extention by quantitative acoustic emission". International J. Fracture, 28. 201-222 .
J.E. Micheals, T.E. Micheals and W. Schase (1981), "Application of Deconvolution to Acoustic Emission
Signal Analysis", Materials Evaluation, 39, 1032-1036.
M. Ohtsu (1989). "Source kinematics of acoustic emission based on a moment tensor", NOT International,
22(1). 14-20.
S. Budavari (1971), "A Mathematical Method of Determining the Anisotropic Velocity of P-Wave
Propagation and for the Siesmic Location of Rockbursts". Geomechanics Journal. 1. 15-17.
M.D.G. Salamon and G.A. Wiebols (1974), "Digital Location of Seismic Events by an Underground Network of Seismometers Using the Arrival Times of Compressional Waves". Rock Mechanics, 6,141-166.
M. Ohtsu and K. Ono (1988), "AE source location and orientation determination of tensile cracks from surface observation". NDT International, 21(3). 143-150.
A. Maji and S. P. Shah (1988). "Process Zone and Acoustic Emission Measurements in Concrete",
Experimental Mechanics. 28. 27-33.
R. Claxton (1990). "Characterization of Fracture in Charcol Granite Through Advanced AE Instrumentation
and Deconvolution Techniques", Ph.D. Dissertation, Northwestern University. Evanston, m.
W.H. Press. B.P. Flannery, S.A. Teukolsky and W.T. Vetterling (1986). Numerical Recipes, Cambridge
University Press, Cambridge.
A. Papoulis (1977). Signal Analysis, McGraw-Hili Book Co.• New York.

W. Nieblack (1986). An Introduction to Digital Image Processing. Prentice Hall International. London.
H. Stang, B. Mobasher. and S.P. Shah (1990), "Quantitative Damage Characterization in Polypropylene
Fiber Reinforced Concrete", Cement and Concrete Research, 20. 540-558.

5103

Application of Acoustic Emission Techniques in the
Evaluation of Frost Damage in Mortar
Hisatoshi Shimada and Koji Sakai
The authors are affiliated with Materials Sec., Civil Engineering Research Institute, Hokkaido Development
Bureau, 1-3 Higashi, Sapporo, Hokkaido 062, Japan.
Abstract
To characterize the deterioration of concrete due to freezing and thawing, acoustic emissions (AE) of
mortar were measured and the locations of AE sources were determined. AE during ice formation and melting were examined to establish the test conditions. In addition, effects of physical and geometric factors on
the accuracy of source locations were discussed in order to establish the experimental method. Results showed that AE generation due to ice formation and melting took place dwing both freezing and thawing cycles.
Most AE events from mortar occurred during only freezing cycle and the number of AE events did not increase with the number of freezing-thawing cycles.
The result also showed that the arrival time of AE should be determined from reading wavefonns andthat error in the arrival time is dependent on the threshold level of the system. The physical and geometricfactors were discussed to improve the accuracy of source location.
1. Introduction

In cold regions, concrete strUctures must be frost-resistant. Commonly, resistance to freezing and thawing is evaluated by accelerated tests based on ASTM C666. However, the relationship between the durability factor obtained from these tests and frost damage in the natural environment has not been clarified yet.
Recently, acoustic emission (AE) techniques have been found useful to detect cracking and deteriorationof concrete. The purpose of this study is to describe the deterioration behavior of concrete under freezing andthawing by the AE techniques. Events, arrival time, and amplitude were measured and analyzed on plain
mortar.
2. Details of Experiments
Normal portland cement and Toyoura standard sand were used. The water-cement ratio of the mortar was
0.65 and the sand-cement ratio was 2.0. Test specimens were cured in water for 7 days. AS mm-high frameof silicone was placed on the top edges of specimens for holding water.
Figure 1 shows a diagram of the AE measuring system. Two or four sensors (PZT type of 140 kHz
nominal resonance frequency) were attached to the specimen with thermoplastic wax and silicone. Each sensor was connected to a 40 dB preamplifier with high pass filter of 50 kHz. The output from the preamplifier
was fed into an AE acquisition system and a wave memory. In the AE aquisition system, the threshold wasset at 55 dB and AE parameters, such as events, maximum amplitude, arrival time, and so on, wererecorded. In the wave memory, one waveform was digitally recorded at 200 ns/point with 1024 points. In
this paper, data obtained through the AE aquisition system and the wave memory are defined as "event dataand "wave data," respectively. All data were transfered to a computer system and stored on floppy disks.
The length of each freeze-thaw cycle was 6 or 24 hrs. Figure 2 shows these temperature histories. The
range of temperatures was from 12°C to -25°C. Noise exclusion is an important consideration in AE measurements. In this study, two or four sensors were used for the source location. It was found that the noise
was inconsequential when we selected only AE events which could be detected at all sensors.

8104

I

I

AE Sensor
Il.OkHzl

I
Preamplifier.OdS

oH.P.F. 50kHzI
I

I

AE Aqulsillon

Wave Memory

TI"..... oId 55dB

Slmpllng Po,lod~.P.F. 1000kHz

56mm

200nl

I

Fig. 3 AB measurement with water under freezing
and thawing.

I
I

I

Ym~mm
aJarr

Computer System

I
Floppy Disk

I

e...
~

Q)

II

I
I

-e 12
:::J

Printer

II

I

Q)
Q.

ProtlerE~

Fig. 1 Acoustic emission measurement system.

p '~[ 6 hCXJ~Cle
--25

...

cu
:J

V

1'\

\.TV V

11~~<oJrs/CYd.

-zt=:
o

1'\

6

6CCO

~

\OJ

6hoors/cycl.
a
1\

C\

V

V

Total AE event
12692
17724

I

L

,7 ,
12

-2~

t

18

20000

24

4CXXX)

6aXXl

Time (s)Time (hourI

Fig. 4 Acoustic emission at ice formation andmelting.

Fig.2 Temperature history.

_ 1200I.J

51~. . . . 800

Total AE.event: 486
408
169
Freezing process
Freezing processi

Thawing process

~

e 400
o

a

)
zam

I

~

«XXXl
eo::x:oTime (5)aa:::rolca:x:x:>Fig. S Acoustic emission at ice formation and melting (24 brsIcycle).
8105

water supply

3. AE Events due to Frost Damage in the
Mortarsil iconFig. 6 Rectangular prism specimen.

-p~center of specimen
_ 12
I
~
cu

6houlS/cycle

0 f->I:~-----:f-r~:---f-."e::.>~--ht

E-25 '--

~

--J

12

TOlol AE event

57

26

20

without surface waler

8

N

4

o
o

~

A~
2<XXX>

4<XXX>h~

6CCOJTime (s)Fig. 7 Acoustic emission in monarozt/I 10 x 20cm
1 (5.0.5.0. i.OJ

7.0

2 (0.0.10.0.0.0)
J (-5.0.5.0. i.OJ
4 (0.0.0.0. O.OJ

7.0

x

y

Fig. 8 Cylinder specimen.

Prior to AE tests of mortar, AE events due toice formation and melting were measured with 30
and 60 cm3 of water in a plastic vessel as shown in
Fig. 3. Two sensors were attached to the two sidesof the vessel with silicone.
Figure 4 shows the AE events due to ice formation and melting during the six-hour freeze-thawcycle. AE events occur during thawing as well as
freezing. The number of AE events tends to increase
with the increase of water volume from 30 to 60
cm3 . AE due to ice formation is not observed after
the temperature becomes constant at -25°C, but AE
due to ice melting occurs successively. This result
suggests that the ice does not melt completely during this process. Figure 5 shows a result of the 24
hr freeze-thaw cycle. Under this process, AE events
do not occur after ice formation or melting is complete. .
Figure 6 shows a rectangular prism specimen.
In this experiment, water was supplied to the top
surface of the specimen. Thus, the effect of surface
water on AE events was taken into account.
Figure.7 shows AE events in mortar with and without surface water. AE events in monar with surface
water appears similar to that of ice formation andmelting shown in Fig. 4. In contrast, AE events do
not occur above O°C with no surface water. AE
events are observed with surface water. The numberof AE events in the mortar with surface water islarger than that without surface water. This meansthat AE events are affected by surface water. The ice
formed by surface water is not directly related to
frost damage in the mortar. Consequently, the surface water should be removed for AE measurements
due to frost damage.
As the specimen is subjected to freezing andthawing in air, water content in the specimen decreases with cycling. Therefore, as shown in Fig. 7,
the number of AE tends to decrease as the numberof freeze-thaw cycles increases. Although the consideration of surface water indicated that it should be
removed, water supply is necessary for the frost
damage in monaro Therefore, water was supplied forabout five hrs prior to AE measurements of every
three freeze-thaw cycles.

5106

As the specimen is subjected to freezing and thawing in air, water content in the specimen decreases
with cycling. Therefore, as shown in Fig. 7, the number of AE tends to decrease as the number of freeze·
thaw cycles increases. Although the consideration of surface water indicated that it should be removed. watersupply is necessary for the frost damage in mortar. Therefore, water was supplied for about five hrs prior to
AE measurements ofevery three freeze-thaw cycles.
The AE measurements were continued until the looth cycles. However, there was no increase in thenumber of AE. This may be associated with a phenomenon like the Kaiser Effect and also with changes inpropagation properties due to microcracks.

4. Source Location in Mortar
In order to clarify the deterioration process, a three·dimensional source location analysis was carried out

The shape of the rectangular prism specimen shown in Fig. 6 was found to be unsuitable for the determina·tion of three·dimensional source location because arrival time differences were too large between the axial
and lateral direction. Therefore, a cylinder specimen shown in Fig. 8 was used.

~y

X Sensor 3 n": ~'."'"

,'.

,,/

"

/, Sensor 2

Sensor 1

..

. -:

:

.,'

e•

.

Sensor 1

Sensor 4.,...

'. ~ ~.

,

Sensor 2

z

z

4Y

X~
Fig. 9 AE source location.

5107

Fig. 10 Effect of geometric factors on source location.
In the analysis, two arrival times, Te and Tw, were used. Te is obtained from event data and is the nominal arrival time when the amplitude crosses the SS dB threshold level for the fmt time. Tw is the real arrival time obtained from wave data. Based on the arrival time obtained from wave data (Tw), source locations of 64 events could be determined. Whereas based on the arrival time obtained from event data (Te),source locations of only seven events could be determined. These results indicate that it is difficult to determine the source locations of AE due to frost damage in mortar only from the arrival time of event data (Te).
Figure 9 shows results of the three-dimensional source location by Tw obtained from wave data. It is
obvious that most of AE events occur in the upper portion of the specimen. The deterioration in the upper
portion was accumulated more than that of the lower portion, because water was supplied from the top surface of the specimen.

s. Discussion of Factors to Improve the Accuracy of Source Location
In general. AE sources are located from the arrival time difference and a wave velocity. It is known thatthe accuracy of source location depends on such physical factors as arrival time, wave velocity, attenuationand geometric factors as sensor position and relative position of sensor and source (Rindorf, 1984). From

S108

the present analysis. it is found that the error in arrival time can'be minimized by the use of Tw • However.
it seems that other factors need to be examined in order to improve the accuracy on the source location.
The wave velocity of the specimen changes depending on the freeze-thaw cycles and was measured after
each three freeze-thaw cycle to evaluate the frost damage in the mortar. However. it is realized that the effectof error in the wave velocity on the accuracy of the source location is smaller than that of the arrival timedifference.
The amplitude of AE decreases as the distance from the source to the sensor increases due to attenuation. AE events which occur at locations far from the sensor may not be detected. The attenuation may
cause a difficulty in the determination of the arrival time as discussed above. Therefore. the distance ispreferably determined by taking the effect of attenuation into consideration.
Effect of error on the arrival time was investigated. Figure 10 shows the effect of geometric factors on
the two-dimensional source location on the rectangle plane of dimension 10 x 20 em. It is assumed that thewave velocity is 4000 mls and the error in arrival time at sensors A and C is absent and that of sensor B is
2.5 JlS. When sensors A and C are set at the comers. the effect of the error on the arrival time is small withrespect to located sources. The error vector is ploued in the figure. However. in the case of the bottom figure. it is obvious that the error of located sources is very large.

6. Conclusions
1. AE is observed in ice during melting as well as freezing. The number of AE events tends to increase
with the increase of water volume. AE events do not occur after ice formation or melting is complete.
2. The surface water should be removed for AE measurements due to frost damage, because AE events areaffected by the surface water.

3. There are few AE events from mortar during thawing, and also there is no increase in the number of
AE when the number of freeze-thaw cycles increases.
4. It becomes difficult to determine the source location with the decrease of wave amplitude. The source
location could not be performed from the arrival time of event data, the maximum amplitude of which
was less than 78 dB. The arrival time was best determined from wave data.
5. The relative position of the sensor and the source is quite important for the source location.
Reference
HJ. Rindorf (1984), "Location of Microseismic Activity," Proceedings of the Third Conference on
Acoustic EmissionlMicroseismic Activity in Geologic Structures and Materials. Trans. Tech. Publications.
Clausthal. Germany.

8109

An Apology from the Publisher
For the past two years. I was working in Tokyo for the University of California's
Education Abroad Program; overseeing the academic programs of about 100 UC students
studying in Japan and helping a similar number of exchange students coming to california. My
initial plan was to continue work for this Journal from Tokyo. However. the workload there
was higher than anticipated since all experienced staff departed before my arrival and I had tofind an assistant and train her from scratch. Thus. despite the intention. we have managed to
publish only two issues through the end of volume 9 during this period. I must also apologize
for not responding to many inquiries regarding papers submitted, subscriptions. publication
schedules. etc. Now that rm back in California. we'll attempt to clear the backlogs of papers
and return to more regular publication schedules. Since the research environment in the US andEurope for acoustic emission work has deteriorated substantially in the last few years. we must
redouble our efforts to maintain the quality of articles to be published.
This issue contains papers from The International Joint Meeting. 1st Workshop on Acoustic
Emission in Civil Engineering and 2nd Workshop on AE and Rock Fracture Mechanics. held in
Kumamoto. Japan, October 29 - 31. 1990. Professor Masayasu Ohtsu of Kumamoto University was
the Topical Editor for this issue. He also contributed.an introduction. "Current Research and Future
Trend of AE Applications to Civil Engineering and Geological Technology."

Kanji Ono, Publisher

1992 MEETINGS

1991/92 SUBSCRIPIlONRAlE rorVohme 10
S 96.00
Base rate for one volume (four issues)
Add Postage of
U.S. - Book rate
S 8.00All others • Book rateS ) 1.00
U.S.• Canada - First class
S 17.00$ 25.00
Western Europe - Air mail rate
All others - Air mail rate
S 30.00
Add S5 without payment upon order. Payment must be in U.S.
dollars drawn on a U.S. bank. Bank transfer accepted to the
A.E. Group account (Acc:ount No. 080.03416470) at
Sumitomo Bank of California
San Fernando Valley Office15250 Ventura Blvd
Sherman Oaks. CA 91403-3262
Notify us of such a transfer as the bank cannot sometimes giveus the name of the payer. One volume of four issues per year.
Index included in the Oct.-Dec. issue: Back issues available at
550 per volume for Vols. I - 3.590 per volume for Vols. 4 - 9.
US surface postages 58 per volume. For Canada and overseas
surface delivery. add Sll and for air mail. add S30 per volume(adjusted for multi-volume order). All orders should be sent to
Acoustic: Fmlsslon Group308 Westwood Blvd, Box 364
I.Ds Angeles, CA 90024·1647

4th International Symposium on AE
from Composite Materials
The 4th International Symposium on AE from Composite
Materials. sponsored by the CARP and ASNT. will be held on
July 27 - 31. 1992 at Holiday Inn Crown Plaza in Seattle, USA.
Organiz.ing Committee Chair is Dr. Burke Dykes (Boeing
Commercial Airplanes) and the Program and Papers Comminee
is chaired by Dr. D. R. Smith. Ir. (Boeing Commercial
Airplanes: FAX No. 206.393-7457). Pre-symposium seminar
will be held on July 27 and the Symposium will be Iuly 28 to 31(3.1/2 days). Proceedings will be published by ASNT.

11th International AE Symposium
The 11th IAES. sponsored by the JSNDI. will be held on Oct. 26. 29. 1992 at Sun Palace (c:onvention center) in Fukuoka, Japan.
Fukuoka is located on the island of Kyushu and can be reached inless than 2 hrs from Tokyo by air or in 6 hrs by trains. Paperand Program Committee Chair is Prof. K. Takahashi (Kyushu
Univ.) and the Organizing Comminee is chaired by Prof. T.
Kishi (Tokyo Univ.) while the Secretarial Commiuee by Prof.
M. Ohtsu (Kumamoto Univ.). For details. contact Prof. K.
Takahashi. Research Institute for Applied Mechanics. Kyushu
University. Kasuga. Fukuoka 816 Japan. FAX No. 81-92-5824201 The Proceedings will be published by JSNDI.

Editor-Publisher
ScndFAX to
Editorial AssistantCover Design
Production

Kanji Ono

(310) 825-5233(818) 368-8309
Barbara Brooks
Robin Weisz.
UCLA Publication Services

Publication Dale of This Issue: 2 August 1992.
S110

Notes for Contributors
1.

GeneralThe Journal will publish contributions from all parts of theworld and manuscripts for publication should be submitted to
the Editor. Send to:Professor Kanji Ono, Editor - JAB
6532 Boelter Hall, MSE Dept.
University of California
Los Angeles, California 90024-1595 USA
European authors may submit manuscripts directly to:

Dr. Roger Hill, Associate Editor - Europe
Department of Physical Sciences
Trent Polytechnic
Clifton Lane
Nottingham NG11 8NS England United Kingdom
Authors of any AE related publications are encouraged tosend a copy for inclusion in the AE Uterature section to:
Mr. T.F. Drouillard, Associate Editor - JAB
Rockwell International
Energy Systems Group
P.O. Box 464
Golden, Colorado 80401
All the manuscripts will be reviewed upon submission to the
Editor. Only papers not previously published will be
accepted. However, special consideration will be given topapers published in conference proceedings of limited
circulation; consult the editors.
Authors (unless stipulated otherwise by governments) must
agree to transfer the copyright to the Journal and not topublish elsewhere, a paper submitted to and accepted by the
Journal.

The title should be brief. Except for short communications,descriptive heading should be used to divide the paper into
itscomponent parts. Use the International System of Units(SI).
References to published literature should be quoted in the text
citing authors and the year of publication. These are to begrouped together at the end of the paper in alphabetical andchronological order.
Journal references should be arranged as below.

H.L. Dunegan, D.O. Harris, and C.A. Tatro (1968), Eng.
Fract. Mech., I. 105-122
Y. Krampfner, A. Kawamoto, K. Ono. and A.T. Green (1975),..Acoustic Emission Characteristics of Cu Alloys under LowCycle Fatigue Conditions" NASA CR-I34766. University ofCalifornia, Los Angeles and Acoustic Emission Tech. Corp..
Sacramento, April.
A.E. Lord. Jr. (1975), Physical Acoustics: Principles andMethods. Vol. II, eds. W. P. Mason and R. N. Thurston,Academic Press, New York, pp. 289-353.
Abbreviations of journal titles should follow those used in
the ASM Metals Abstracts. In every case, authors' initials,
appropriate volume and page numbers should be included.
Illustrations and tables should be planned to fit a single
column width (85 mm or 3.3") or a double width (180 mm or7"). For the revieWing processes, these need not be of highquality. but submit glossy prints with the final manuscript.
Unes and letters should be legible (smallest character should
be larger than 1 mm).

4.
A paper is acceptable if it is a revision of a governmental ororganizational report, or if it is based on a paper publishedin a conference proceedings volume of limited distribution.
An abstract not exceeding 200 words is needed for Researchand Applications articles, while it should be shorter than
100 words for other articles.

All manuscripts will be judged by qualified reviewers. Eachpaper is reviewed by one of the editors and typically sent forreview by members of the Editorial Board. The Board
member may seek another independent review. In case of
disputes. the author may request other reviewers.

5.
The language of the Journal is English. AU papers should be
written concisely and clearly.

2.

Page Charges

No page charge is levied. Fifty copies of off-prints will be
supplied to the authors free of charge.

3.

Electronic MediaIn order to reduce any chance of typographical error, theauthors are encouraged to submit a floppy disk copy of the
text. We can read Macintosh and IBM PC formats. Those
who can connect to computer networks. such as BITNET,ARPANET. ASIANET, EARN, NETWORTH. uucp. CSNEr.
JANET, Oz. etc. can send 10 "ono@ seas.uclaedu". Note thechange in this address.

Manuscript for Review
6.

/

Review

Manuscripts for review need only to be typed legibly;
preferably, double-spaced on only one side of the page withwide margins and submitted in duplicate.

Color PhotographWe can print color photographs needed to enhance thetechnical content of an article. Because of the cost, theauthor is asked to pay $350 per pase.

Contents
Special Issue on The International Joint Meeting, 1st Workshop
on Acoustic Emission In Civil Engineering and 2nd Workshop
on AE and Rock Fracture Mechanics, Kumamoto, Japan,
October 29 • 31, 1990.
Masayasu Ohtsu, Topical Editor
Current Research and Future Trend of AE Applications to Civil Engineering and Geological Technology~~uOh~uiVariety of Acoustic Emission Wavefonns Produced by Discrete Crack Growth in Rock
Steven D. Glaser and Priscilla P. Nelson
51·512Estimation of Maximum Suess in Old Railway RivelCd I-Girder Bridges using Acoustic Emission Signals
Hisanori Otsuka, Hiroshi Hikosaka. Hiroyuki Miyatake and Syouzou Nakamura
S13-517
Another Look at Booming Sand
Marcel F. Leach and GouIricd A. Rubin
S18-521
Expected Acoustic Emission from Around a Shaft in Intact Rock522-528
a.J.s. Wilkins and G.L. Rigby
Acoustic Emission Monitoring during Microseismic Activity Caused by Mine Subsidence
Brian R. A. Wood and Raben W. Harris529-534

Acoustic Emission/Microseismic Activity Monitoring of Salt Crystallization for SlOne Conservation
M. MontolO, R.M. Esben, L.M. suarez del Rfo, V.G. Ruiz de Argandoi'la and C.M. Grossi
535·541
Acoustic Emission Monitoring during In-situ HcaterTest of Granite
T. Ishida, K. Kimno. N. Kinoshim and N. Wakabayashi
542·548
Using Acoustic Emission Testing in Seepage lr'lVcsligations
Andrew R. Blystra
549·554
Acoustic Emission of Penetration Experiments to Judge Soil Condition
S. Nacmura, M. Tanaka, S. Nishikawa. M. Nakamura. K. Jo and T. Kishishim
555·558
A LaboralOry Investigation of AE from Coal
R. W. Harris. B. R. A. Wood and T. Flynn
559·562
Detennination of the Initial SlJ'esses on Rock Mass using Acoustic Emission Method
K. Michihiro, K. Hata, H. Yoshioka and T. Fujiwara
563·576
U.S. Bureau of Mines Research on the Kaiser Effect for DeLCnnining SIJ'eSS in Rock
S77·S89
Michael J. Friedel and Richard E. Thill
Evaluation of Joint Properties of Anti-washout Underwmcr Concrete by Acoustic Emission Measurement
Kazuya Miyano, Tntsuo Kita. Yuji Murakami and Takako Inaba
590-S96
Automated Detcnnination of First P-Wave Arrival and Acoustic Emission Source Location
E, Landis. C. Ouyang and S. P. Shah
597·5103
Application of Acoustic Emission Techniques in the Evaluation of Frost Damage in Mortar
Hisatoshi Shimada and Koji Sakai
5104·5109
An Apology from the Publisher
5110
5110
Meetings in 1992

Dr. Raymond W. B. Stephens (1902-1990)

JOURNAL OF ACOUSTIC EMISSION
Editor: Kanji Ono
Associate Editors: A. G. Beattie, T. F. Drouillard, R. Hill and S. L. McBride
1. Alms and Scope of the Journal
Journal of Acoustic Emission is an intemational joumal designed 10 beof broad interesl and use to both researcher and practitioner of acoustic
emission. It will publish original conlributions of all aspects of research andsignificanl engineering advances in Ihe sciences and applicalions of
acoustic emission. The joumal will also publish reviews, Ihe abslraCII of
papers presented al meetings, technical notes, communications andsummaries of repons. Currenl news of inlerest to Ihe acoustic emission
communities, announcements of future conferences and working groupmeetings and new products will also be included.
Joumal of Acoustic Emission includes the following classes of subjeci
mallers;
A. Research Anicles: Manuscripts should represenl compleledoriginal work embodying the results of eXlensive invesligation. These will
be judged for scientific and technical meril.
B. Engineering Applications: Anicles musl presenl significanl
advances in the engineering applications of acoustic em ission. Malerial will
be subject 10 reviews for adequate description of procedures, substanlial
dalabase and objective interpretation.
Technical NOleS: This class provides publicalions of works ofcurrent inleresl, and new or improved cxperimenlal lechniques andprocedures.
D. Communications: Shon ilems of currenl inleresl, discussions ofpublished anicles and relevant applications.
E. AE Literalure: This section will collecllhe tilles and abslracts ofpapers published elsewhere and Ihose presented al meelings andconferences. Repons of conferences and symposia will also be presenled,
IOgether with meeting schedules.
F.
Reviews, TUlorial Anicles and Special Conlribulions: This classof anicles will cover Ihe subjects of general inlerest.
Nontechnical Section: This pan will cover book reviews, significant
personal and technical accomplishmenls, current news and new producls.

e.

2. Endorsement
Acoustic Emission Working Group (AEWG), European Working
Group on Acoustic Emission (EWGAE), Commillee on Acoustic Emission
from Reinforced Composiles (CARP), and Acoustic Emission Working
Group of India have endorsed the publicalion of Journal of Acouslic
Emission. This Journal gratefully acknowledges financial conlributions fromEWGAE and AEWG.

3. Governing Body
The Edilor and Associale Editors will implemenl Ihe editorial policies
described above. The Edilorial Board will advise Ihe editors on any majorchange. The Edilor, Professor Kanji Ono, has Ihe general responsibility for
all the mailers. Associate Edilors, Dr. S.L. McBride and Dr. A.G. Reallie,
assist the review processes as lead reviewers and Associale F.ditor, Dr. R.
Hill coordinales the regional review processes for Europe. Mr. T.F.
Drouillard is responsible for the AE Literature section.
The members of the Editorial Board wiD advise and assisl the edilors
on the publication policies and OIher aspects. They wiU promptly review themanuscripl assigned and will COntribule actively to the Journal. The Board
prosently includes the following members:

John Baron
F. BeallL. Bolin
F.R. Breckenridge
S.H. Carpenter
H. Croslack
D.M. Egle
P. Fleischmann
TJ. Fowler
L. Golaski
M.A. Hamslad
II.R. Hardy, Jr.
T. Holroyd
I.V. Ivanov
P. JaxT. Kishi
A.E. Lord, Jr.
K.MagiW. Murgner
C.R.I~ Murlhy
A. Nielsen
M.OhISU
A.A. PoUock

J. RogelI. RomanC. ScrubyC. Thaulow
E. Waschkies
K. Yamaguchi(Canada)(USA)(Sweden)
(USA)
(USA)
(FRG)
(USA)
(France)(USA)(Poland)
(USA)
(USA)
(UK)
(Russia)(FRG)(Japan)(USA)(Japan)(DDR)(India)(Denmark)(Japan)(USA)(France)(Israel)(UK)(Norway)(FRG)(Japan)4. Publication
Joumal oC Acouslic Emission is published quanerly in March, June,
September and December by Acoustic Emission Group, 308 Weslwood
Blvd.. Bo;\ 364, Los Angeles, CA 90024·1647. It may also be reached al5731 Boeller HaD, UniversilY of California, Los Angeles, California 90024·
1595 (USA) lelephone 310·825·5233. FAX 818-368-8309.
5, Subscription
Subscription should be senl 10 Acoustic Emission Group. Annual ralefor 1992 and 1993 is US 596.00. For surface delivery, add S6 in the U.S.
and 58 for Canada and elsewhere. For air mail delivery Lo US and Canada,
add SI5, South America and Wcslern Europe. add 523 and add $28
elsewhere. Add 55.00 for orders nOI prepaid and requiring invoices.
Overseas orders mUSI be paid in US currencies wilh a check drawn on a US
bank. Inquire for individual and bookseller discounls.

6. Ad\'utlsemenl
No advcnisemenl will be accepted, bUI announcemenls for books,
lraining courses and CUlure meetings on AE wiD be included withoul charge.

ISSN 0730-0050 Copyright © 1992 Acoustic Emission Group

Coden: JACEDO

Printed in U.S.A.

Acoustic Emission of the 45HNMFA Structural Steelduring Low-Cycle Fatigue
J. Siedlaczek, S. Pilecki and F. Dusek
In this situation, a complicated system of protections from

Abstract

possible consequences of a breakdown of the high-pressurechamber must be used. These systems. however. do not protect
the materials and equipments inside the chamber or in its close
proximity. This practice does allow rational use of the material
for high-pressure chambers, either.

This paper presents the results of the measurements of
Acoustic Emission (AE) count rate during low-cycle fatigue ofthe variously heat treated 45HNMFA SlIUCtural steel. This steel
is used for the production of high-pressure chambers. The experimental technique and results are described. The most important result of this investigation is the occurrence of threestages of the AE activity during the low-cycle fatigue. These
dependencies can be applied in monitoring Ihe service life (safeoperation period) of high-pressure chambers and/or otherstudied objects.

That is why an attempt has been made to include theacoustic emission (AE) melhod in the research of low-cycle
fatigue of 45HNMFA steel used for the production of highpressure chambers. It was expected that the AE activity, as wellas the area of hysteresis loop would be proportional 10 lhe
energy dissipated by the material during one loading cycle.
Besides. it was supposed that the AE activity in samples would
be similar to that in working pressure chambers made of thesame material. This would give a possibility to use the AE as a
crack detection method for parts serving in the region of lowcycle fatigue; in our case. a possibility of the estimation ofsafe-service life of the high-pressure chambers.

1. Introduction
When investigating the properties of materials in theregion of low-cycle (high slress amplitude) fatigue. they aresupported by the records of hysteresis loops arisen due toelastic-plastic deformations during particular loading cycles.
The area enclosed by the loop is proportional to the energy
dissipated by the material during this loading cycle (Szczepinski. 1984). From the practical point of view. the purpose of
the investigation at low-cycle fatigue is as follows:a) determining the service life of a specimen or a machinepart at the given loading level;b) estimating the allowable load for a given number ofcycles to failure.

In the present paper. the results of AE measurements on
the samples of variously heat treated 45HNMFA steel duringlow-cycle fatigue loading are presented.
2.

The experimental study of AE during the low-cycle fatigue
was conducted on samples of a 45HNMFA steel produced in
Poland. Its chemical composition is given in Table 1.

The problems in question are the accuracy in defining
these and Ihe certainty of how we can put Ihe obtained results
into practice. It is difficult to answer these questions while designing a component or SlrUCture for services at the low numberof cycles to failure. Either by the influence of scattering or by
an inaccurate estimation of the limiting deformation (or SlresS)
value. a possible failure of the SlrUcture can appear.

Table 1 Chemical composition of 45HNMFA steel (in WL%)C
0.45

Such a case occurs in high-pressure chamber (Unipress.
1986) subjected to conditions of low-cycle fatigue loading.
The fatigue computations (Siedlaczek. 1983) based on thepresumptions of Lame do not give satisfactory results, because
the problem is being solved in the region of elastic deformations (strains) and Ihe calculated allowed loading is then (atthe safety factor. n I) up to 4 times lower than that used fordesigning the high-pressure chamber for laboratory practice.

MnSi
0.50 0.30

Cr
1.00

Ni
Mo
1.70 0.25

VS
P
0.15 0.027 0.026

From such a material twelve specimens were made according to Fig. 1. They were heated to 860'C for I hr and oil
quenched. Then the specimens were randomly divided into threesets which were for 60 min. tempered at 600" 500' and 3OO'C
and furnace cooled to obtain three different levels of Rockwell
C hardness: HRC 30. 40 and 50. respectively. The sets weremarked 3, 4 and 5. Thus, each specimen number has double
digit. e.g.• 33, 51 etc.; the flfSt digit is the set number, indicating its hardness. and the second one is lhe specimen number
among this set. Each set had four samples; one of them wasused for static mechanical test and the others three for low.
cycle fatigue experiments. Mechanical properties obtained
from static testing are presented in Table 2.

=

Received 3 September 1988, in revised fonn, 18 July 1991. J. Sied1ac:zek
is affiliated with High Pressure Research Center. Polish Academy of
Sciences, Sokolowska 29, 01-142 Warsaw, Poland, S. Pilecki is with
Institute of Fundamental Technological Research, Polish Academy of
Sciences, Swietoknyska 21. 00-049, Warsaw, Poland, and F. Dusek iswith Institute of Physical Metallurgy, Czechoslovak Academy of
Sciences, 7lzkova 22. 616 62 Smo, Czechoslovakia.

Joumal of Acoustic Emission
Volume 10, Number 3/4

Experimental Details

The loading of the specimens in the low-cycle fatigue
region was performed with a Zwick 20T testing machine. Each10730-OOS0192l1 0001 ~ll
Copyrightel992 Acoustic Emission GroupTable 2 Mechanical properties of 45HNMFA steel

Aattened Part for Transducer

RockwellHardness
HRC

a ys

au

MPa

MPa

30
40
50

975
1375
1500

1050
1585
1875

Elongation Necking
%
%

17
12
10

44
34
32

The AE activity was measured by the AE 10-UFM CSAV
analyzer (Fig. 2). developed at the Institute of Physical Metallurgy of the Czechoslovak Academy of Sciences in Bmo(Dusek et al.• 1984). This analyzer has been constructed as aten-channel device with fIXed amplitude levels. The frequency
range is 30 kHz to 2 MHz. The dynamic range of this amplitude
analyzer is 40 dB and the separation of individual channel is 4
dB. The selected parameters of the AE analyzer were: amplification, 70 dB; bandpass. 0.1 - 0.3 MHz. It can record and analyze
AE count rates and cumulative counts.
During system setup per Fig. 3. possible spurious signals
caused by Zwick testing machine were examined by the AE
analyzer OEMA-I0 RMS. developed at the Experimental
Department TECHPAN attached to the Institute of Foodamental
Technological Research of the Polish Academy of Sciences.
Warsaw. It was fOood that no extraneous signals resulted. To
measure AE signals. a suitable sensor was chosen from the setof products of the Polish fum. UNIPAN. Warsaw. The measure-Fig. 1 Dimensions and shapes of test specimens.
specimen was loaded by cyclic force with triangular wavefonn
from zero up to the constant maximum amplitude being thevalue of yield stress of this specimen (Table 3). The crosshead
speed of the testing machine was chosen to be 2 mm/min.

r-,

ITBI
L_.J

COMP.

Fig. 2 Block diagram of the AE 10 Analyzer: NZ - low-noise input amplifier. VF - electronic filters. Z - linear amplifier. PA •threshold analyzer, AA - 10-channel amplitude analyzer, DC -COOO'ter. 00 - control and pilot circuits. MP - multiplexer control. VK channel selection modules, OA - O/A converter. M • acoustic and optical monitor, SZ - power supply Wlit.
2

Table 3 Summary of fatigue resullS of all specimens tested
SpecimenMax
initialNo.

(yield)stress
(J

£pin

£pc

£pl

Ne

Ns

Ne

866
945
794
1355
1410
1320
1853
1710
1780

0.31
0.80
1.35
0.58
0.42
0.65
0.57
0.35
0.41

0.41
0.28
0.29
0.77
0.98
1.13
0.11
0.27
0.20

0.72
1.08
1.64
1.35
1.40
1.78
0.68
0.62
0.61

9
10
7
8
6
7
21
22
9

142
83
253
121
97
157
25
53
49

293
220
862
206
221
337
86
153
107

31
32
33
41
42
43
51
52
53
• Ne

Plastic strain £p [%]cyclictotal

Cycle number •=No. of cycles to AE signal extinction. Ns =No. of cycles to 2nd AE activity appearance. Nr =No. of cycles to specimen failme.
then cyclically loaded up to failure. Stresses. strains and cycle
numbers are given in Table 3.
Plastically deforming a sample during the fast cycle ofloading was to simulate real service conditions for overloadedhigh pressure chambers and to examine their subsequent ability
to emit warning AE signals in-service monitoring teSts. It wasquite obvious that exceeding the yield stress at the very onsetof specimen loading cycles can reduce the AE signals via Kaiser
effect till before crack appearance.

M

AE10
y

x

In Fig. Sa. collected resullS of low-cycle fatigue of thespecimens Nos. 31. 32 and 33 with hardness of 30 HRC arepresented in the form of dependencies of cumulative total
plastic deformation on the number of cycles. Similar graphs
were obtained also for all the specimens of different hardnesses. Typical Stress-strain hysteresis loops of all specimens
were obtained by Zwick testing machine. Two of them areshown in Fig. Sb. where it is visible that stress amplitude waspractically constant; as usually is in stress controlled fatigue
tesls. The amounlS of strain in subsequent cycles were quite
different: individually chosen maximum strain. £pin in the fustcycle. and then gradually decreasing values.

xvx
Fig. 3 Diagram of arrangement of the measuring set of AE
signals during low-cycle fatigue teslS. A) test specimen, B) AE
transducer. C) sensor of specimen elongation. D) acoustic
transducer pick-up of test machine vibration. LNA) low-noise
input amplifier 40 dB. AE 10) Czechoslovak AE analyzer. M)
monitor. DEMA) Polish AE analyzer. XY) XY recorder.

To compare the average resullS. the curves of cumulative
strain against the number of cycles (like these shown in Fig.
Sa) were normalized using the respective values of the initialstatic plastic strain. £pin' given in Table 3. These normalized
curves of strain vs. the number of cycles are shown in Fig. 6.
One can see a big difference between normalized strain values
Ep/£pjn in the specimens with HRC 40 and both HRC 30 and 50.

menlS of frequency characteristics of the sensor by means of a
spark calibrator were carried out at the Institute of Physical
Metallurgy of the Czechoslovak Academy of Sciences (Dusek etal.• 1990). The frequency characteristic is given in Fig. 4.

3. Experimental Results

Figure 7 shows the macroscopic fracture appearances of all
the specimen groups. In Figs. 8. 9 and 10, higher magnification fractographs obtained by the scanning electron microscopy are presented. The fatigue fractures of specimens withdifferent hardness exhibit many differences in surface patterns.
which indicate differences in the development of low-cycle
fatigue process.

3.J Structure and Mechanical Properties
All samples of the 45HNMFA steel in the first cycle were
loaded with individually chosen initial static plastic deformation, £pin' The deformation was achieved. as the yield stress ofa particular specimen was exceeded. The chosen values of £pinwere not depended on yield stress values; we tried to apply threequite different £pin for each hardness set. The specimens were

3

1

muv

dl

r ~~
-10

-20

-30

~

1 .0
~
0

~~

i!
100

A

::P-

.

-40

o

2

UNIPAN
q2LR22B I2l3I

~o

200

11.5
A

400

SOO
600
700
--f(kHz]-

1.0

<a>

~:S4SM"aFig. 4 Frequency characterization of the acoustic emission
transducer UNIPAN-O.2LR22B, No. 213.

A

The 30 HRC specimen (left on Fig. 7) shows a typical cup
and cone fracture, and a large necking is evidenL Reduction inarea, Z. was 49%. The fracture initiation area was in the middleof the specimen. The concentric fracture surface marks (see Fig.
8a) are characteristic of crack propagation in this specimen.
After the crack had reached the critical dimension, a sudden
failure of remaining cross section occurred, thus producing a
cup and cone fracture. In Fig. 8a and b, the fatigue zone can beseen with larger magnifications. The centrically displaced
faullS are the evidence of the cyclic character of the process offracture swface formed.

I

O.3UI1I

45HNMFA

I
I

o 3
o 50

30HRC
100

150

200

250

--Nlcycles]-Fig. 5 Plastic defonnation £p vs. the number of cycles N in the low-cycle fatigue affected by the different degrees of initial static
deformation. a) £p vs. N for Nos. 31, 32 and 33. b) (J vs. £ for Nos. 33 and 51.
At 40 HRC (center on Fig. 7) a semi-brittle nat fracturewith radial markings is seen. The fracture initiation region isshown at the center left of Fig. 9a. along with a very diversified
fracture surface pattern. Upon fraCDlre (Nf =206), a relatively
large necking (Z =40%) was observed. It is difficult to distin-

guish the fatigue and the residual zones. The characteristicfeatures are the numerous secondary cracks displaced radially.
The circumferential secondary cracks separate the originationof fracture surfaces in subsequent load cycles.
4

\2.2
45HNMFA.§;2.0

40HRC

0-

w

'0.1.8

w

11.6

(a)

30HRe

50HRe

1.0

~

_ _--'--_ _o----L.

50

'---'

100
150
--N[cycles)--Fig. 6 Normalized changes of plastic deformation F.pI£pin for
all three streng!h sets of !he 45HNMFA material during low-

cycle fatigue.

(b)

30HRC

40HRC

50HRC

Fig. 7 Macrostructure of fractures of the 45HNMFA steel test
specimens with hardness of 30. 40 and 50 HRC after low-cycle
fatigue. Magnification 0.76X.
At 50 HRC. it is evident from macrophotograph (right on
Fig. 7) that the fracture initiated from the surface. The initial
crack then propagated over the whole cross section in thebriule fashion in very low cycles to failure (Nf = 86). The
plastic deformation of the specimen was very small (Z = 6%).
There are no clear Iraces of cyclic fracturing of the specimen
according to Fig. 10.

Fig. 8 Fracture surface of the specimen with hardness of 30
HRC.
the loading cycle. AER(N). to see if an estimation of the service life of high-pressure chambers could be made. The ampli.
tude dislribution of AE signals was also evaluated in particularloading cycles.

3.2. Acoustic Emission
During the measurement of the mechanical properties of
the 45HNMFA steel in low-cycle fatigue. AE signals wererecorded. The data was Ireated in a form of the AE count rate vs.

5

<a)

<a>

..
-~_
.
,',

w.

.

-,:...~.'<:
V._.

19Quml<b)(b)Fig. 9 Fracture surface of the specimen with hll.ldness of 40
HRC.

Fig. 10 Fracture surface of the specimen with hardness of SO
HRC.

Such a differentiated and diversified way of fatigue fracturesurface formation of the specimens with 30, 40 and SO HRC
should manifest itself as differences in the results of AE tests.
Indeed, the differences concerning the AE activity itself havebeen found; the greatest activity was exhibited by the specimens with SO HRC, the smallest - by the ones with 30 HRC.

However, the character of the AE activity of the specimens was
similar: all of them showed maximum activity during the firstcycle, decrease of activity for a few folJowing load cycles,
eventual lack of AE activity beyond several cycles to about ahalf of the fatigue life and then systematically increasing AE
activity in the remaining fatigue life, up to failure.

6

a:
w

In Fig. 11. a typical example of the AE count rate.
AER(N), for samples with 40 HRC is shown. The data is plottedagainst cycle number for selected cycles up to failure; for the
specimen No. 41. it occurred during the 206m cycle. The
results with similar character. but quantitatively different, wereobtained on other samples in all the hardness sets (Table 3). It
concerns especially the AE activity for the specimens with
HRC SO. For example, specimen No. 51 has very shon silent
period: decrease of AE activity endured till 21st cycle and from25th cycle there appeared secondary AE count rate increase. up
to the specimen failure.

45HNMFA 40HRC

4.

a:

~.
~
~

The cumulative amplitude distribution (LAB) of AE signals
after the fust loading cycle is shown in Fig. 12a. By a dashedline. the amplitude distribution of the AE signals after 136
cycles is given here. In Fig. 12b amplitude distribution for theentire 206 cycles is presented.
Cumulative amplitude distribution for all the specimens
during particular loading cycles is given in Table 4. One cansee that in all the specimens. amplitude dislribution drifts togreater values by increasing cycle number. Besides. average AE
count rate is am biggest for the specimens with 50 HRC and am
smallest for those with 30 HRC. These fmdings seem to beconsistent with crack behavior differences described in Sec.
3.1; brittle cracking which occurred in the specimens withhigher hardness appears to be connected with higher AE
energy.

N(cycl.es]

Fig. 11 Activity of the AE signals in the low-cycle fatigue
process of the 45HNMFA material. hardness of 40 HRC.

Q)

b)

1

2

--NUMBER OF CHANNEL3 4 5 6 7 8 9 10--NUMBER OF Q-fANNEL..
2 3 4 5 6 7 8 9 10

1

--,I
I
I

l After 136 cycles

V
I

I

L._,
I

I
I

I
I

L_,
I
I
I
I
I
I

L_-.
I

I
I

L_,
I

4

8

12 16 20 24 28 32 36 40--A [dB]-

48

12 16 20 24 28 32 36 40--A[dB]-

Fig. 12 Amplitude distribution of the AE signals during cyclic loading of the specimen with hardness of 40 HRC.

7

a::

460

UJ

«

463

465

466

0.-

0

.45HNMFA 30HRC
480

482

491

492

---N[cycles]•

Fig. 13 AE activity in dependence on various modifications of loading schedule in the specimen with hardness of 30 HRC.

In Fig. 13. examples of AE count rate measurements inseveral cycles with different loading schemes for the specimen
No. 33 (30 HRC) are shown. In the second half of specimen
fatigue life, each increase of loading is accompanied by anincrease in AE activity. This phenomenon is quite similar thatshown in Fig. 11.

of cyclic plastic deformation of specimens with both SO HRC
and 30 HRC. The causes of such a conformity between the HRC
30 and SO curves will be studied in the future.
The different mechanisms of AE occurring at the beginning of sample cycling and in its advlUlced !>lage seem to be as
follows:
The changes in AE activities are the well-known results of
dislocation density increase during the fatigue process of ametal. At the high deformation level, almost complete dislocation structure forms in the fmt few loading cycles (GrossIcreutz. 1971; Gro!>slcreutz and Mughrabi, 1975). The high AE
activity corresponds to several possible processes (James andClIIpCtlter, 1971; Heiple et aI., 1981). The fundamen1al cause of
AE is the dynamical effect of spontaneous dislocation movement, multiplication, and annihilation of paraDel segments ofloops, occurring especially during the high activity of FrankRead sources (James and Carpenter. 1971; Imanaka et aI.,
1973; Pawelek et a1., 1985). as well as sudden dislocation
acceleration andlor deceleration (Pilecki. 1986). Many dislocations and Frank-Read sources take part in these processes inentire volume of the sample and AE intensity is also high. In
the macroscopic elastic range, the segment length of mobile
dislocation increases with increasing external stress (lmlUlakaet aI., 1973). At small strains, the loop length and AE countrate are highesL With gradual development of plastic deformation, the length of dislocation loop gets shorter, and their
number increases. Below a certain level of length of free
dislocation loops (corresponding to the sufficiently highdislocation density) the detectable AE does not occur, because
most of dislocation lines are immobile.

By means of the relationships found experimentally. three
AE activity regions can be identified during low-cycle fatigue:a) high AE activity in the fust cycle (with after-effects andslight showers of AE signals in the few following cycles); b)
extinction of AE signals beyond several initial cycles up to
about half of the number of cycles to the failure: c) rise of asecond AE activity starting from the end of extinction region(b) and enduring up to the specimen failure. Now, during the few
loading cycles (having triangle form - see Fig. 11) the first AE
signals occur by maximum loading only. In the following
cycles, AE signals appear to start at lower load levels, down to
the minimwn level in the last several cycles before the failure.

4.

Discussion

The value of fmt-cycle stalic loading of specimens influenced the curves of strain vs. number of cycles. Ep(N). The specimens with greater initial strain showed lower increase in£pc(N) during cyclic loading. The changes are more evident
with the increasing number of cycles. For the sake of comparison, parallel lines (A) going through the points corresponding
to the 30th loading cycle were ploned in Fig. Sa.
The mentioned differences in the dependence of the amountof cyclic plastic deformation on the number of cycles are notgreat in comparison with the differences of cyclic plastic
deformations occurring in specimens with different hardnes!>.
From Fig. 6. it is clear that the cyclic plastic deformation of
specimens with hardness of 40 HRC greatly exceeds the values

According to the Kaiser effect. no AE signals ought tooccur at all during the following cycles. In our experiments. in
the first few cycles, some AE signals were noted, certainly as a8

Table 4 AE amplitude dislribution in particular cycles
Ampliblde Level Channel

Specimen
No.

Cycle
No.

2

3

4

5

6

7

8

9

10

31

1
182
287

3638
2803
7007

1198
459
1149

432
1300
4325

100
923
1807

30
406
1027

8
209
526

0
98
245

0
47
117

0
65
164

32

1
86
127
219

3535
6230
9831
12714

1294
1392
2334
5469

495
348
547
1770

233
100
166
549

163
64
76
184

70
20
66

43
0
1
26

19
0
0
14

9
0
0
20

1
322
526
862

6727
4435
3564
2877
2383

1869
1275
2111
9784
7025

629
391
583
2786
2582

127
110
190
967
1711

86
30
27
406
1409

48
25
46
296
598

10
10
62
257
342

13
15
88
255
447

7
20
147
355

41"

1
136
206

3706
9999·
9999·

1360
7845
9999-

558
3690
9999·

225
1040
4216

90
545
1133

61
220
321

30
135
310

9
50
407

11

15
6374

42

1
100

14571
4155

4611
1030

1686
322

622
140

289
53

106
14

63

23
8

11

56
129
237
337

4520
7540
8578
2356

1103
302
323
3396

283
98
104
1834

77
32
28
749

17

8

0

13

17

14
735

24
30
814

28
736

1
11
20
744

4
22
32
2923

51

1
10
21
60
269

1411
2062
1623
1492
2512

480
594
2947
5269
8579

169
140
878
1522
6853

63
49
334
476
1539

27
38
141
181
657

10
29
59
86
311

2
13
6
25
215

8

10
0
16
195

95
13
0
11
1490

52

1
12
96
121
140

3286
3273
2339
1375
4376

687
720
3922
3467
6720

426
227
2651
2179
6343

121
92
343
871
2582

63
51
175
348
987

20
23
65
203
428

6
9
28
183
368

10
1
17
193

112
3
3
223
1127

1
5
53
78

4159
718
689
587
3794

1272
526
775
1067
10532

515
121
168
263
6241

224
28
40
43
2735

23
1

44
0

11

6
8

17
0
2
9
456

33

'2.61

43

53

105

16
1419

11

880

11

326

5
0
7
6

243

940

9

50
0
9
10
1290

• off scale •• for specimen No. 41. cumulative counrs };AE are given.
result of some dislocation rearrangements only near the
maximum loading.

cyclic displacemenrs occur, giving some contribution to cumulative cyclic plastic SD'ain growth.

During many following load cycles. no AE signals appeared because of ground dislocation SD'ucture has been formed in
the course of the f'lfSt cycle. that resulted in a large plastic
SD'ain (see Figs. 11. 13). In the subsequent cycles. dislocation
density approaches its saturation state which prevent freemovement of dislocations and their significant multiplications. Though in subsequent cycles some number of dislocalions may make a flip-flop movement between cell boundaries,
causing some total cyclic sD'ain increment (GrosskreulZ,1971). There is no dislocation muhinlicalinn nrocess, but

After the dislocation density saturation state is reached(this is usually complete for most metals after 20 to SO % of
their life to failure), inhomogeneous plastic strains in the formof slip bands occur (Lukas and Klesnil. 1971). These bands do
not begin their development until after saturation is reached(OrosslcreulZ, 1971). Dislocation arrangement is determined by
the plastic strain ampliblde which is certainly greater in theslip bands than in surrounding matrix. Because dislocation
dipoles are the dominant feature of this state (GrosskreulZ,1971), it is suggested that the dislocation density saturation
state could be accommodated by the equilibrium between gene9

ration process of the dislocations and the annihilation ofexisting ones. Smce the generation of new dislocation is pro.
portional to the mobile dislocation density and the annihi·
lation, to the density squared, such an equilibrium stateestablishes only when dislocation density is sufficiently high(Pilecki, 1969, 1977; Bammann and Aifantis, 1982).

creasing amplitude value of AE signals (Fig. 12, Table 4). This
Ulformation allows use of the AE method to estimate the safeservice life of devices working in the fatigue conditions. With
regard to the AB signals. it is evidently possible (HoussnyEmam et al., 1983) to describe the output from a monitored
object which consists of three stages:
1. Hardening COJDlected with the high AE activity;
2. Dislocation density saturation characterized byextinction of AE activity;
3. Nucleation and propagation of a crack wilh a corresponding increase in AE activity with increasing
amplitude of the AE signals up to failure.

The dislocations move mainly under the action of externalloadUlg. By cyclically varying loading, dislocation velocity
and displacement direction is correspondmgly changed, butthere occurs a net drift velocity and flux of dislocations. This ismost possible m a thin surface layer from which dislocations
escape to the free surface. Some dislocations, especially thosein near-surface regions, can easily move to the surface by theadditional influence of mirror (image) forces atttacting dislocations to the surface (Hirth and Lothe, 1968; Pilecki, 1977,1986). Thus, as a result of dislocation moving from theinterior, the slip band is characterized at the surface, thenaccompanied by growth of roughness and fmally by notchpeak geometty (I'etelman and McEvily, 1967). In the vicinityof a notch tip, the dislocation density is especially high(Yokobori et al., 1970). As a result of dislocations escaping to
the notch surface. notch tip displaces, which is equivalent tocrack growth. It is weD known that the initiation of crack
propagation causes the high AB signals. Modem understandUlgof this phenomenon ought to connect the AE effects with crack
growth as a result of reaching the crack surface by great amountof the dislocations, moving with high velocity (Pawelek et al.,
1985; Pilecki. 1986).

References

OJ. Bammann and B.C. Aifantis (1982). "On a Proposal for aContinuum with Microstructure". Acta Mech.• 4S. 91-121.
F. Dusek, M. Kotoul and C. Seidl (1984), The Acoustic
Emission Analyzer AE 10, Czechoslovakia Patent No. 231236,
Prague.

F. Dusek. C. Seidl. J. Siedlaczek and S. Pilecki (1990). 'The
Acoustic Emission Transducer Calibration Using Spark Technique'" Arch. ACOUSL. IS [3-4],
J.C. Grosskreutz (1971). 'The Mechanisms of Metal Fatigue".
phys. staL sol. (b). 47, 11-31. 359-396.
J.C. Grosskreutz and H. Mughrabi (1975). "Description of Ihe
Work Hardened Structure at Low Temperature in Cyclic Defor·
mation", Conslilurive EqlUllions in Pltzsticily, ed. A.S. Argon.
MIT Press. Cambridge. pp. 251-326.

It appears that AE observed in the second half of fatigue
life is caused by such escaping of these dislocations to lhe freesurface in the vicinity of notch tip. In Fig. 11 we see that for N> 120 these AE signals appear fIrStly at the maximum loading
only and then the beginning of the signal appearance shifts tolower and lower loads. down to zero just before failure. Thismeans that crack growth makes the escape of dislocations more
and more plentiful and thus the acceleration of crack growthmust take place. When the number of applied cycles is sufficiently high. it is seen (Fig. 13) that each (even small) loadincrease causes a generation of new AE signals.

C.R. Heiple, S.H. Carpenter and M.J. Carr (1981), "Acoustic
Emission from Dislocation Motion in Precipitation-Sttength.
ened Alloys". Metal ScL, IS, 587.

J.P. Hirth and J. Lothe (1968), Theory of Dislocations. McGraw-Hill, New York.
M. Houssny-Emam and M.N. Bassim (1983). "Study of theEffect of Heat Treatment on Low Cycle Fatigue in AlSI 4340
Steel by Acoustic Emission". Mater. Sci. Engr.• 61, 79-88.

Second appearance of AE signals can be identified withcrack initiation and gradual AE amplitude growth with crack
propagation. That is, during crack growth. the AE amplitude
drifts from small to large values (see Table 4). It means thatfatigue cracking generates AE signals of much higher energy
than plastic deformation does.

T. !manaka, K. Sano and M. Shimizu (1973). "Dislocation
Attenuation and Acoustic Emission during Defonnation in
Copper Smgle Crystal", CrysL Lattice DeC., 4. 57-64.
D.R. James and S.H. Carpenter (1971), "Relationship between
Acoustic Emission and Dislocation Kinetics in Crystalline
Solids". J. Appl. Phys., 41, 4685-4697.

It is evident that, by means of AE signals, it is possible todescribe the process of fatigue fracture nucleation, as well as
holh Ihe stable and unstable crack propagation in Ihe fatigue
processes.
S.

P. Lukas and M. KIemil (1971). "Dislocation Structures inFatigued Single Crystals of Cu-Zn System", phys. stat. sol. (a).
5. 247·258.

Conc:lusions

The data on mechanical and Sb'Ucture characteristics of the
45HNMFA steel in the low-cycle ratigue region was obtained.
It can be applied in the construction oC service·safe high pres·sure chambers. The most important result of these experiments
is a repeated occurrence of increasing AE activity in the second
half of life period of the material (Figs. 11 and 13) with in-

A. Pawelek, W. Stryjewski. W. Bochniak and M. Dybiec(1985), "Mobile Dislocation Density Variation during Strain
Rate Change Evidenced by Acoustic Emission", phys. staL sol.
(a). 90. 531-536.

10

A. Pawelek., W. Bochniak. H. Dybiec and W. Stryjewski(1988), "Application of the Technique of Acoustic Emission
for Testing Mechanical Properties of Metals and Alloys U",
Arch. Metallurgy, 33, 645-661.
S. Pilecki (1969), "Proliferation, Diffusion and Vanishing of
Dislocations in the Course of Process of Metal Fatigue", Bull.
L'Acad. Polon. des ScL, Sel. Sci. Techn., 17, 489-496.
S. Pilecki (1977), "Dislocation-Diffusion Mechanism of
Fatigue Crack Fonnation", Proc. Fourth lllli. Conf.on Fracture,
Waterloo, Canada, vol. 2, pp. 687-692.
S. Pilecki (1986), "Acoustic Emission Application for Testing
Mechanical Properties and Toughness of Solids (in Polish)",Arch. Akustyki, 21(1), 109-134.
R.T. Sadgwick (1968), "Acoustic Emission from Single
Crystals of LiF and KCI", J. Appl. Phys., 39[3), 1728-1740.

J. Siedlaczek (1983), "Acoustic Emission in the High Pressure
Vessel Testing (in Polish)", XII KKBN NOT-Conference,
Rydzyna.
W. Szc:zepinski (ed.) (1984), Experimelllal Methods in Solid
State Mechanics. (in Polish), PWN, Warsaw.
A.S. Tetelman and AJ. McEvily. Jr. (1967), Fracture of Structural Materials, Wiley, New York. Chapter 8.
UNIPRESS (1986), "Prospect of the LC Liquid Cells", Information Leaflet of High Pressure Research Center, Warsaw
T. Yokobori. K. Sato and Y. Yamaguchi (1970), "X-Ray Microbeam Studies on Plastic Zone al the Tip of Fatigue Crack", Rep.
Res. InsL Strength and Fracture of Materials, Tokohu Univ.,
6[21. 49-67.

11Progress in Acoustic Emission VI, Proceedings of The 11th
International Acoustic Emission Symposium
Held at Fukuoka, Japan, October 26-29, 1992; Edited by T. Kishi, K. Takahashi and M. Ohtsu;
Published by The Japanese Society for Non-Destructive Inspection, Tokyo. 604 p.

Contents
Special Lecture
Application of Acoustic Emission Techniques in Manufacturing
D.DORNFELD

Prediction of Chaotic AE Signals by a Neural Network
I. GRABEC

Applications of Acoustic Emission Techniques for Diagnosis of Machinery in Plants and Mass Produced Goods inFactories
I. SATO

Composites 1
Determination of the Elastic Properties of Composite Materials Using Simulated AE Signals
W. SACHSE. M. VElDT and L. NIU

The Fracture Behavior of a Model Fiber Reinforced Plastic Composite Utilizing a Displacement-Sensitive AE
Monitoring System
H. SUZUKI. M. TAKEMOTO and K. ONO

Soutee Characterization of Acoustic Emission (AE) Wave during Tensile Test of CFRP by Analyzing Frequency
Spectrum
J.-S. LEE. J.-O. LEE and S.-K. OH

Acoustic Emission Monitoring ofDamage Progression of CFRP Laminates under Repeated Tensile Loading
I. KlMPARA. K. KAGEYAMA. T. SUZUKI and I. OHSAWA

Manufacturing 1
Acoustic Emission during Unlubricated Sliding Wear of Silicon Nitrides
O.-Y. KWON. D.-J. YOON. S.-K. PARK and D.-S. UM

Origin of Acoustic Emission Produced during WIre Drawing
Y. MORI
Acoustic Emission Quality Control for Wooden Panel Production
K. SATO. K SUGIMOTO. F. KAMIYA. T. HIRATA. K YAMAGUCHI. N. ANDO and M. FUSHITANI

Acoustic Emission Monitoring of Incipient Failure in Journal Bearings
D.-J. YOON. O.-Y. KWON and M.-H. JUNG

Composites 2
Transient Elastic Waves Generated by Point Source in Carbon Fiber Reinforced Plastics Plate
J. K. LEE. Y. H. KIM and H. C. KIM

Monitoring of Creep Damage in Woven E-Glass FiberlEpoxy Laminated Composites during Short Term Creep Test
through Acoustic Emission
T. UENOYA(continued to page 18)
12

Parameter Estimation in Acoustic Emission Signalsc. E. D' Attellis, L. V. Perez, D. Rubio and J. E. Ruzzante
bursts are separated by an interval less than the decay time. so
Ihat bursts overlap and continuous AE is obtained.

Abstract
An acoustic emission signal is represented by a series of
decaying bursts of the same fonn, random amplirudes and varying times of occurrence. The estimation of the time of occurrence and amplitude for each burst is given using Kalman filter
leChniques. The method is easily implementable on-line. Somesimulated and experimental results are presented.

The aim of this paper is the estimation of the time ofoccurrence and amplilUde for each burst in the AE signal given
by equation (I). We solve the problem using a Kalman filter as
estimator (Iazwinski, 1970; Gelb. 1974). This approach provides an efficient estimator, which is easily implemenaable on·line,

1. Introduction
2. Model for RepresentiDg aD AE signal
By monilOring the output of a piezoeleclric ttansducer on aoscilloscope during an acoustic emission (AE) experiment,
decaying bursts stochastically spaced and with random amplitudes can be observed. The AE bursts often have similar form,
so Ihat information about material changes is largely hidden in
the rate and amplitude of bursts.

In order to apply the Kalman filter theory (Jazwinslci.
1970; Gelb, 1974; Me Gany. 1974; Wong. 1971; Sage andMelsa. 1971). we ought to modellhe AE signal (1) using a system of differential equations (see Appendix). In the fonowing.
we will show the consttUction of such a system of differential
equations step by step.

Following Mitrakovic et al. (1985) we can describe an AE
signal by a series of wavettaim of the same form, but of different amplirudes and varying time of occurrence, by the expression
s(t)= I. a; h (t - Ii)

2.1 Differential EquazionfoT a Single Burst
The function (2) allows us to obtain a second order differential equation corresponding to a single burst. In fact, theLaplace ttansform of (2) is(1)

iwhere Ii is amplirude for rhe i·th burst, Ij is time of occurrence
for the i-th burst and h(t) is the assumed waveform of a single
burst, which is a damped sinusoidal oscillation which can be
expressed by the function
h(t)=(

exp(-t/'t)sin2nf"tifl~O

H( s)=-r-'t
where s is a complex variable.
The corresponding differential equation is

(2)

o
ift<O
where fa is the resonant frequency of the ttansducer, and 't is thecharacteristic decay time. The time interval between adjacentbursts can vary by several orders of magnitude during an experiment.

i( t )t)x( t ) ::: ( 2ttfo ) v( t )=O. i(O) =O.

Defming

we obtain the following system of differential equations:~=~(
X2 =-[~ + 4r

Received 12 November 1990. in revised form, 27. May 1992. C.E.
D'AueI1is, LV. perez and D. Rubio are affilialed with Comision Nacionalde Energia Atomica, Centro de Calculo Cientifico (CAc>, Avda. delLibenador 8250. 1429 Buenos Aires, Argenlina. J.E. Ruzzante is with
Depanamento Materiales, Comisicm Nacional de Energia AtomiC&, andLV. perez is also with Consejo Nacional de Investigaciones Cienlificas y
TeaJicas. Supported by IAEA Research Ccmtrael S987/RB.
Volume 10, Number 3/4+~ i( + [~+ 4~ e]with initial conditions x(O)As Mittakovic et aI. (1985) have established, the rate andamplitude of the bursts depend on the state and rate of defonnations. bUI for sufficiently shott intervals the AE phenomenon
can be tteated as a Poisson impulse process for a given averagerate. If the rate of bursts is very low, then the bursts are separated by more than the characteristic decay time and we gel discrete acoustic emission. On the conttary, for high rates the

Jnumal or ACOUStic Emission

2ttfo
S2 +1.+.1.+ (2nfa)2

f;]

Xl -

~

~ X2 + ( 2ttfo ) v( t ).

The veclor x is called swe of the dynamical system (3). If
the input v(t) is a Dirac impulse applied in la.
v(t) = ~t - 19),
the solution of (3) is073CUlOSOI92lI0013.Q17
13

Copyrightel992 Acoustic Emission Grmp

Xl ( t ) == I ( t- to ) ex~ - t ~ to ] sine 2nfo )( t - to ).

where
X (t)=

where I(t) == 1 if t ~ 0 and let) == 0 if t < O.
Thus. a single burst wilh to as time of occurrence can beconsider as the solution of equation (3) when the input fimctionvet) is an impulse at t == to.

xo (t)][Xl (t)X2 (t)

is lhe vector state.

2.2 DijferemiaI EqlUJlionfor the Time Occurrence ofa BurstF=

A Kalman filter estimates lhe states of a dynamical system(see Appendix). As stated in Sec. 1. our problem is lhe estimation of lhe time occUlTence for each burst in the AE signal. In
order 10 use a Kalman filter as estimator we have 10 change lhetime-occurrence estimation problem in a state estimation problem. The following rust order differential equation make it possible:x.,(t)+CXo(t)::::u(t)
(4)

-c

o

o

o

o

1

_11:

H=[O I 0]

andu(t)=Lad>(t-t;)iwith (li) random points in time.

wilh Xu(O) = 0 and u{t) == 110 ~(t-to).

2.4 Input Analysis

The solution of equation (4) is
Xu(t) = llo exp[-c(t - to)) I(t - to).

According 10 Mitrakovic et aI. (1985). lhe input (6) is asequence of random impulses Poisson-distributed and with random amplitudes 8;.. which are independent of lhe lj. It can beshown (Papoulis. 1960) that this type of slOchastic process
has the following characteristics:

If lhe constant c is large enough. !he exponential fimction
is practically equivalent to lhe input u(t). and lhen lhe estimation of lhe state Xu in equation (4) is practically equivalent 10
the estimation of lhe time occurrence 10 and the amplitude llo.

E[u(t») =mI..

2.3 System of Dijferemial EqlUJlions as Model ofAE Signal

(9)

and
E[u(t)u(t')]

According 10 the previous discussion. connecting in cascade the dynamical systems (3) and (4) we obtain a system ofdifferential equations for AE signals:='M.a.1- + mZ)~(t. t') + mZlZ,(10)

where E[ail = m. E[lI?] = 0.2 and Ais the Poisson constant.
Taking the stochastic processx.. (t)= - au( t)+u (t)U(t)=L aiS(t-li)-m8(t-li)(5)ilhe expressions (9) and (10) become
E[u(t)]=O
E[u(t)u(t')] = A(ob8(t - t'). a = lIi - m.

Using as input u(t) a sequence of impulses
u( t)=

L ai~(t-li)

These are the characteristics of lhe white noise, as Kalman filter algorilhm required (see Appendix).

(6)iwilh {lj} random points in time. the AE signal given by equation (1) can be considered as the output yet) = XI(t) of the dynamical system (5). We suppose a noisy measurement so II
white noise wet) with zero mean and covarianceE(w(t)w{1:») :::: RS( t - 1:)

3, Computer Simulation
Figure 1 summarizes in II block diagram the model
described in lhe previous section and the estimation algorilhm.
Figure 2 shows the AE signal corresponding to the following
parameters: i = I, 2 (i.e.• two bursts), tl = 350 ~, tz :::: 366 ~,u(tt) = 1 mY. u(t2) 35 mY. fo = 0.135 MHz, A. =0.0075, R
2.02 x 10-3• c 10,000, 't 172.1 ~s.

(R>O)

is added to the output fimction: yet) = xI(t) + wet).

=

=

=

=

Summing up. the AE signal is given by the following sysFigure 3 shows the estimation io(t) of the rust state variable xo(t) given by the Kabnan filter algorilhm. A good estimation of time occurrence and amplitude is obtained.

tem of differential equations:x(t)=Fx(t)+Gu(t)y(t)=Hx(t)+w(t)

(7)

(8)

14

l.J

a

i .. Fx+Gu.

PoiS30nS!jsrem
model

impulses~1/

H

J<o.lmQ.n tUter-X"

l'1eo..slJremen t
modeL

Estima.tor

l1a.them4tiad 1'I000ei 'or AE'Si9n4u
Fig. I Mathematical model.

8

1.00

~ 6
e
~

.-III

t>l

w

0.80

4

2

0.60

<I

0
0.40

-2
-4

0.20

-6
O.OO..J-......,.--T'""'"-...,..--r----r--~--.,...._-_.,

-8
0

100

200

300

400

500

600

700

5

800 900 1000
t i /1'111 (;IS )

10

15

20

25

30

35
time

40
(1'-5)

Fig. 4 Filler gain.

Fig. 2 CompUler simulation of an AE signal composed by two
bursts localized in tl 350 and 12 = 366.

=

4
4

.

>e

'1S

. 3

:J

-.:l
:J

2

-... 2..E-1-1-2+--T'""'"--,r--~---r-_r_-"T'"""-,.......
.........--.--...,o-2+--r---,--...,....-r--'-T-.....-r----r--.----.

o

100

200

JOO

400

500

600

700

800

100

200

JOO

400

500

900 1000

time

(J&5)

600

700

800 900 1000
t imll (f5)Fig. 5 Estimations given by the Kalman ftlter using the steadyvalues of the gain.

Fig. 3 Estimation of the time occurrence and amplitude givenby the Kalman filter.

In our case this gain is a vector
As can be seen in the Appendix (see equation AI). theestimation given by the Kalman filter consists in two parts:
one of them corresponds to the new information, which isweighted by a time dependent matrix K(t) called "gain of the filter".

and is shown in Fig. 4. As can be seen, the three entries of thematrix gain reach the steady state in a short time interVal. This
fact allows an easy implementation of the method, since, using
15

ptllse
width

t2

t
I

I

'lime

I
I
I

il'\
'/

I

\::)."'. 0\ 0I/ " \9-I

I

V\ /\ /"\ _\:TVV ·

Steel Plate
Fig. 6 Laboratory configuration for AE signal generation.

-~

Fig. 7 Burst generator.

... 1.00

600

-

400

III

200

->e•

0.50

~

4

...

...

::J

I:

Co

E O.

<I

III

w

0
-0.50
-200

-1.00
-400

-1.501-1----...---.--,,---,..-.----r-.----r-.-...-,.--,-6001+----.-....----,.--r---r-T"---.--.,.---.--r-.,...---,o100 200 300 400osao 600 700 800 900 1000 1100 1200t imQ (J-l5l

Fig. 8 Experimental AE signal.

100 200 300 400 500 800 700 800 900 1000 1100 1200
time (,..5l

Fig. 9 Estimations given by the Kalman filter.

the constant steady state values in the filter algorithm. theestimations obtained are as good as the ones obtained using thetime variable gain K(t). The estimation io(t) corresponding to
the Kalman algorithm with constant gain is shown in Fig. 5.

same amplitude, approximately located in 320·350 J1S and 500550 J1S. This fact is not clear from Fig. 8; i.e., from the original AE signal, where there are similar amplitudes in 350, 400,
600 and 650 J1S.

4. Experimental Results

S.

Figme 6 shows the laboratory selUp for the AE generation.
The AE signal was obtained using a piezoelectrical crystal fed
by a square wave coming from a pulse generator. Each wave
front generates a burst in the steel plate, shown in Fig. 7. The
pulse can vary from 1 J1S to 15 ms. The signal detection wasmade using a piezoelectric sensor (AEDOS-ClSE) of approximately 150 kHz. of resonance frequency. The signal was amplified 60 dB and digitized using an AID converter ETUDE-TM (2S
MHz. 8 bits). The analysis of the digital signal was made in a
PC system.

A technique for estimating the time occurrence and amplitude of bursts in AE signals was presented. This technique isbased on the model given by Mitrakovic el aI. (1985) and thetheory of Kalman filtering. We have shown the effectiveness ofthe proposed method using computer generated data and experimental data.

CODc:lusioD

A possible extension of this work would be the consideration of several frequencies in the model and the analysis of thereflections of the AE signal from the boundaries of the speci-men.
The AE signal obtained can be seen in Fig. 8. It is composed by two bursts of the same amplitude localized in 11 350
J1S and t2 500 J1S.

==

Figure 9 shows the estimation obtained with the algorithm
described in Sec. 2. We can distinguish two pulses with the16Appendix

References

Summary of continuous Kalman niter equations

A. Gelb (1974), Applied optimal estimation, MIT Press.
Cambridge.

=

System model: dx(t)ldt F x(t) + G u(t)where x is a column n-vecror, u is a column m·vecror, F
and G are n x n and m x m matrices, respectively, and u(1)is white noise wilhE[u(t)] =0,E(u(t) u'('t)] Q 8(t - 't)(u' transpose)=

A.H. Jazwinski (1970), Stochastic processes and filJering theory. Academic Press, New York.

T.P. McGarty (1974), StocJuutic systems and state estimation.
Wiley, New York.

==

Measurement model: y(t) H x(1) + w(t)where y is a column p-vecror, H is n x p mattix and w(t) is
white noise with
E [w (t)] 0,
E[w(t) w'('t)] R 8(t - 't)==

D, Mitrakovic, I. Grabec and S. Sedmak (1985), Ultrasonics,

13, 227-232.
A. Papoulis (1965), ProbDbilily random variables and stochastic processes. McGraw-Hill, New York.

(R: nonsingular)

Stare estirnale:di(t)/dt = F x(t) + K(t)(y(t) - H x(t)]where K(t) is a n x p mattix, the gain of the filter.
Initial condition i(O) = xo(O).

A.P. Sage and I.L. MeIsa (1971), estimation theory withapplications 10 communications and control, McGraw-Hill,
New York.

(AI)

E. Wong (1971), StocJuutic processes in information anddynamical systems, McGraw-Hill, New York.

Error Covariance Propagation:dP(t)/dt = F P(t) + P(t) F + GQG' • K(t) R K'(t)P(O)=Po
Gain Mattix: K(t) :;: P(t) HR'I
when E[w(t)v'('t)] O.

=

17

Progress in Acoustic Emission VI
Relation Between AE Characteristics and InterlaminarFractureofQuasi-Isotropic CarbonIEpoxy Laminates
T. TANIMOTO and T. MORII
Characteristics ofAcoustic Emission in Fracture Process in Static Load Tests for Notched FRP Plates
H. HYAKUTAKE and T. YAMAMOTO
Characteristics of Acoustic Emission in Notched Short Fiber Reinforced Tbennoplastic
N. S. CHOI. K. TAKAHASHI andK. HOSHINO
Microfailure Mechanism and Improvement ofMecbanical Properties for SheetMolding Compound
N. SATO and T. KURAUCHI
Subsurface 1
Recent Kaiser Effect Studies on RockH. R. HARDY, Jr. and W. SHEN
The Estimation ofPre-Sttess from AE in Cyclic Loading ofPre-Stressed RockM. SETO. M. UTAGAWA and K. KATSUYAMAIn Sibl Stress Measurements Obtained Using Overcoring and the Kaiser Effect of Acoustic Emissions within the
Cammenellis Granite, Cornwall, UK.
A. J. JUPE. S. P. BARR and R. J. PINE
A Sbldy of the Time Dependency of the Kaiser Effect in the Carnmene11is Granite, Cornwall, UK.
S. P. BARR. R. J. PINE and A. J. JUPE

Systems and Analysis
The Reverberation of AE Signal in Small Specimen during Corrosion Process
B. Q. 7.JlANG and Y. H. UU
A Hit Processing Procedure for Locating Acoustic Emission Events
M.-H. YANG
A SiGMA Analysis of the 2-Dimensional PMMA Model
M. SHIGEISHI and M. OHTSU
A Method of One Dimensional Source Location by Using AE Waveform Parameter
T. KISHISHITA. A. CHICHIBU and T. KIKUCHI
Acoustic Emission Waves in Heterogeneous Media and Its Application to the NOE Problems
T.-T. WU and J.-H. GONG
AE Source Wavefonn Analysis by Using a Neural Network
H. YUKI and K. HOMMA
Structures 1
Acoustic Emission Testing - A Mature Technology for Chemical Industry Safety
T.J. FOWLER
AE Monitoring and Studying of Signal Analysis Method for Large Spherical Tanks with Insulating Layer
G. DAI. Y. T. XU. B. Q. 7.JlANG and W. X. HAN
AE Monitoring and grain Measurement for Testing Pressure Vessel with Artificial Prefabricated Flaw
G. DAI. Y. T. XU and B. Q. 7.JlANG(continued to page 24)18

Acoustic Emission Technique at Proof Tests of Nuclear
Pressure Vessels in Hungary
Peter Pellionisz and Janos Gerebwall thickness: 140 mm; forged belt~plates welded togemer andinner side covered with stainless cladding of 9 mm; material:
reactor steel 15Ch2MFA (C = 0.16%, Mo = 0.6%, Mn 0.54%.
Cr 2.7%. V 0.028%); number of water loops: 6: operatingpressure: 12.3 MPa; operating temperature: 290·C.

Abstract
One of me most sua:essful applications of acoustic emission (AE) teelmiques is monitoring pressure tests. This surveillance method has been introduced in Pales Nuclear Power Plant.
Hunguy. where four. 440 MW pressurized-water nuclear reactors are in operation. The first application was made in 1987 atme four-year in-service inspection and proof test of me rust
reactor unit and it has been followed by yearly procedures ofinspecting me second. third and fourth reactor units, between
1988·90. The AE investigations performed at Pales Nuclear
Power Plant are presented regarding Ihe measurement. datatransmission and processing memods. me instrumentation andorganization teclmique.

=

=

=

According to the actual regulation in Hungary. proof pressure value must be 50 percent higher than the operational one.
This high·value overload can cause damages in the pressure
vessel material: tests can enhance the risk instead of reducing
it. Acoustic emission monitoring has been introduced for thisreason. for checking the most dangerous areas during thehydrotests. Communication lines have been established anddata processing took place simultaneously with the measurement in order to alert control personnel and enabling them tointerrUpt the test if needed.

1. Introduction
Safe operation of nuclear power plants is of primuy
importance. One of the key components determining me overall safety of pressurized water reactors is the pressure vessel
containing me highly radioactive nuclear core. In consequenceof me embrittlement process of the vessel material caused by
the irradiation, Ihe safety margin decreases continuously.
Periodical proof tests with enhanced pressure verify system
integrity and reliability. the overloads, however. represent
additional sources of potential danger. In addition to other NOT
methods, acoustic emission (AE) teelmique is frequendy used inorder to detect anomalies such as mictOCrack formation. leakage. etc. during the hydrotests. Charactmstic features of the AE
technique represent benefits at these applications: the wholeprocess can be monitored and evaluated in a relatively simple
and inexpensive way. This is the reason that monitoring acoustic emissions at hydrotests of pressure retaining structures isused as an important test worldwide (Dunegan. 1989; Fowler etaI.• 1989; Scruby et aI.• 1988).

The hydrotests were applied to reactor pressure vessels,
primary circuit and the water cleaning system. They were carried out before re-starting, after complete NOT testing, wim
reactor completely assembled and core fuel installed. The pressure was raised from 2 MPa to 19.2 MPa, with pressure hold andpressure down periods. with a total duration of several hours.
The maximum rate of pressure increase was 0.6 MPa/minute andthe wall temperature was at 120·C.

3. Acoustic EmIssion Monitoring
During the pressure tests AE sensors were applied at three
regions of the structure as Fig. 1 shows. Number. applicationmode, and type of the AE sensors placed at regions A. B and C
varied from one proof test to me other as Table 1 shows. At
Unit 1 only two small. experimental AE systetns were applied.
This son of surveillance proved to be a valuable memod and by
this reason AE monitoring has been used regularly at each of
the consecutive proof tests in the Pales Nuclear Power Plant(Pellionisz et aI., 1989).

2. Proor Tests or Nuclear Reactors In Hungary

Table 1 AE sensors and instrumentation for proof testing

A high percentage of the electricity demand of Hungary is
supplied by me country's four pressurized.water-reaclOr units of
440 MW each. The WWER-440 type reactors of Paks Nuclear
Power Plant have been delivered by me Soviet Union and put
into operation between 1983-87. The units are operating withvery good reliability: their continuous operation is. however,interrupted every four years in order that in·service inspection
and hydrotest of the pressure vessel be performed.

(- =High temperature sensor; -- = Sensor with waveguide)
I

2

3

4

1987

IClll8

1989

1990

---

12

12

09203

09203
09210·

Roac:tor un It• Veer of test
• Nlltaber of lI.O. sensors- In nozzlo roglon (A)- on ducts (8)- lit weldln!i 5/6 (C)• Type C!' sonsors- lit ~~~. (C)- at 8ne-s• NuJaIler Of
lind Exaandor units• HelDOTO chock of sensors

The main characteristics of the V.213-type reactor pres·sure vessel are as follows: diameter: 4.27 m; height: 11.8 m:

--

12

15

4

4

8

\2

6
\2

,

~

-

I

09203
09203-,

2
Exporili.

09203

09203

~

systllal

TClStlll"

Received 20 December 1990. The authors are affiliated with Cenual
Research InstiQ1te for Physics. H·lS25 Budapest, POD 49. HlDIglJ)'.

Iournal of Acoustic Emission
Volume 10, Number 3/4

0730-00S0192l10019.Q23
19

Copyrightel992 Acoustic Emissim Group

09210 Ml) or standard sensors with acoustic waveguide wereused.

.. :;'..:'!
.'

I'~ .. ~

,w ._".'. " .. "...
.

....

.

~,

.-

. .. .. ...
f.

c

Two local AE measuring systems (Defectophone plus
Expander) at places D and E were applied each one communicating with a remote computer through RS-232C serial data transmission links. Before the proof tests, sensors. instruments.
communication lines, computers were checked and calibration
measurements performed. By continuous surveillance of themeasured AE data, on the display of the computers. acoustically
active regions and periods could be identified. Since pressure
was followed generally by pauses or depressurization. when
acoustic activity was not detected, there was available time during the measurement for detailed analysis (plotting correlations between different parameters, making source location
maps. etc.) The real-time analysis was followed by more detailed post-processing after the tests.

/.)<::!

-: ....

...:.....: : -"."'.

-... .. .
.oo....~~~c·~·······
I ' ......

.

..

10

r~ : :........ ' ; . . '.
~~.

The AE investigations were carried out by AE researchersof KFKI (Central Research Institute for Physics. Budapest) andby those of EROKAR (Service Company for Power Plants) incooperation with the NOT-staff of Paks Nuclear Station.

~l::d:

4. Instrumentation
Fig. 1 WWER-440 reactor pressure vessel. Regions indicated:
A = nozzle region with water inlet. outlet ducts. B = primarycircuit piping. C =welding at the height of the reactor core. D =AE measuring devices for regions A and B. E = AE measuring
devices for region C.

The AE measurements were performed by the AE instrumentation developed and put in commercial production by theCentral Research Institute for Physics. Budapest. The applied
equipment was based on the multichannel AE analyzer system
Defectophone NEZ-220 which is a complete family of the nec·
essary AE instrumentation items. comprising amplifiers. frequency filters. testers, etc. An important part of the system is a
powerful software package for IBM-PC and compatible computers enabling them to program and to control the measuring
units as well as to receive and to analyze the AE data arriving
through the RS-232-C standard asynchronous communication
line. The applied systems became larger from 12 channels in1987 to 24 channels in 1990.
AE sensors attached to the reactor wall were provided withsmall. impedance matching transformers, and connected bycoaxial cables to logarithmic amplifiers put at an accessible
place together with the programmable Defectophone System
Tester. 'This assembly was connected to the Defectophone plus
Expander units each having 16 AE plus 16 parameter input
capacity and processing 4 arrays of 4 AE sensors on the fusthit principle. Defectophone plus Expander systems are 16
channel front-end processors enabling identification and digitalization of selected AE parameters of the IlTst hit signals inany of the four-sensor arrays (elapsed time. ring-down count,peak amplitude, absolute average, rise time, event duration.
parameter value), two at the same time, as well as measuring
differences in arrival times at all channels in the active array.
The device disposes microprocessor-based intelligence, 128
kB memory cassette for storing measured data, programmable
operation modes. display, plotter. and alarm facilities. variable
trigger thresholds, selectable data filtering possibilities aswell as a bidirectional computer interface to IBM-PC/AT orcompatible computers.

Fig. 2 Magnetic sensor holder.

At region A the AE sensors were attached manually to thevessel wall with the aid of standard magnetic holders. Access tothis region from space B is by the heavy doors around theducts. Attachment of the sensors to the pipes was made bymetal straps since the pipes are of non-magnetic steel material.
For placing transducers in the welding zone C inaccessible forhuman beings, special magnetic holders were constructed (Fig.
2) which could be taken. positioned. attached and left on thewall by the pneumatic arm of the remote controlled positioning
device for ultrasonic testing.
Generally, 100-400 kHz AE sensors (Dunegan D 9203 A)
were applied. After some of the tests, the sensors on the water
pipes were left in place for measurements during normal operation: in these cases either high-temperature sensors (Dunegan

At the proof tests, each Defectophone was connected to a
dedicated computer in the remote diagnostic control room of20

_1UOO

DEl·

3

.

1

CRCXJPi~----''':'-~

I
.,.

._$.-

a.~lot3

t

1~

16

i

CH N"
(OEF1)-

"

3

4

LOOP

6

2

5

Fig. 3 Sensor arrangement at the proof test of Unit 4.
the power planL Both insDUmem control and data reception as
well as operating the system tester for verifying correct operation of the system were made from this conaol room. The system teSter is controlled by digital commands from the computer
through the Defectophone and stimulates the selected AE sensors by pulses up to 80 V, injecting the needed high energy forthe enormous metal structure to get proper response from thesensors placed far from the stimulus.

S. Checking System Performance

After placing the sensors in their position. coneet functioning of the measuring chains were verified by mechanicalstimulation and watching Defectophone display. Bidirectional
communication between computers and Defectophone devices
was also tested. Measuring sensitivity, background noiselevel, signal attenuation, optimal threshold and wave propagation velocity were made by several tests: pencil-breaking,mechanical shocks, electric pulser driven directly by the
Defectophone devices and by operating the system tester unit.

Distances between adjacent sensors were selected generally between 0.8 m and 2.2 m on the wall. Additional sensors
placed on the water pipes made possible cross-c:hecking of themeasured activity by comparing the signals coming from thewall and from the pipes. The sensor configuration applied at
the proof test of Unit 4 (see Fig. 3) comprised two detectors at
the pipes: the order of signal arrivals showed the direction of
the sound waves; i.e.. identified the region of sound origin.

It must be mentioned that Defectophone source location
software is provided with a semi-automatic velocity measurement mode which instructs the user at what points should be
teSt pulses applied. accepts or rejects the answers and calculates
velocity averages in different directions. The found velocity,geometrical coordinates of the sensors, the selected location
algorithm and drawing forms are summarized in location configuration tables in the computer (Table 2).

Table 2 Summarized data for location calculations.

Source location ability of the AE measuring system wastested by artificial test pulses at known positions. The heatingprocess of the reactor up to 120"C by operating the main water
pumps can also be regarded as an introductory phase of theproof tesL During this process, acoustic activity in the water
inlet.-outlet tubes was also tested: background noise originatedfrom the pumps or showing leakage was measured and recorded.

DEFIlE umnllllDfl_lIl11

Il,IJ ~ gnqs,.Mll:

3

Prula9aUan velOtilg 1115: 2m

IdentkaVdiffennt: diffennt

6. Data Evaluation
Both burst data (acoustic events) and sampled values ofcontinuous signals (acoustic averages and values of parametersignals) were displayed on the computer screen real-time. Correlation between pressure variations and burst data would havehelped the AE experts to detect any anomaly, if had occuned. Hchange in instrument parameters was needed. one could replace
the data display on the computer screen with the front panelpresentation of the Defectophone, by a single keystroke and

L0 eAII." G CAL CUl ATI 0 HID RA~ ~l~i~ eTERI SII e~t~~~tian:~ IQI~: : ~ingle
list III!ISIlI'UI!I sechan:lISt"-"ifylJl!:
hi~

rrawd

21

,t=~1 vim [as)generating new data files which exclude data satisfying userselectable criteria (e.g.• shon and/or small pulses, events fromcertain area, etc.)

Ills) MalIii 111'1.'

By way of illustration, some typical plots are shown.
Figure 4 shows a correlation plot made by the Defectophone
software between event duration and ring-down counts: events
not alike can be easily separated. Figure 5 shows a series oflocation maps at a certain period of the proof test of Unit 2.

i~t~w~F'hllr
" ,
,

,

'

/~:l

11,1
III I
,

"I."

As a base of interpreting AE data. the corresponding

ASTM standard was taken: AE sources were classified in the following groups: critically active, active and inactive, An important diagram for the overall estimation of the safety of thepressure vessel is shown in Fig. 6 from the proof test of Unit 2
in 1988. One can see that AE activity stopped during depressurization or at constant pressure, showing that no critically
active AE source (see ASTM Standard, 1985) was present in theinspected zone. Another presentation mode is shown in Fig. 7.
where burst amplitudes are displayed by dots together with therecord of pressure and cumulated events (proof test of Unit 4).

Fig. 4 Correlation plot between event duration and ring-down
count

+

+

..

+

+

~

+

......

Pressure Ibarl SUIl of »Jents+-

288 i888

J •

+

.+

+

+

+ '.:

+

+

+

+

+

+

+

+

,+

+

+

+

+
TIlle lsI

1ft

u~--r---r----r--r--r-..-r-,-,........-+

18886

+

+

+

+

+

+

+

+

+

+

+

+

Fig. 6 Cumulated number of acoustic events and pressure during
the hydrotest of Unit 2.
Vl£dSl Point
Y2CbarJ
100

90

.m!

f::

110

o

80
70

Fig. 5 Location map in the nozzle area of the reactor vessel
without filtering (a), after filtering simultaneous arrivals (b),further filtering events with counts less than 1500 (c).

.

S

"

~

4- •
"
n'
y~

130
110

·It
-0,

60

90

. ..
i
:~~

,,"'. r....r,.\.

~

50,Sum of E..re~L8-Number ,;~ , : .....
'0" ,. : .
,....A;" .

30".z:r..~~

..
'..

"

,.0

J

40

the required command could be sent to the measuring device,
then displaying data could be continued.

150

,

U.".

.',

.'
•

'"

~

,

50

30

'

00'0

20

Data display - real-time listing of AE parameters - offers ageneral overview on the acoustic activity, but details, correlations between different AE parameters. location maps. etc, arealso needed to have a complete and detailed analysis. The
applied Defectophone data processing software offers many
options to facilitate it A very important one is filtering. i.e.

70

10

10
I

17

23 45 66 88 109 131 152 174 195 Z!7
Elapsed Time [min)Fig. 7 AE peak amplitudes. event number and pressure in function of time at the proof test of Unit 4.

22

7.

Conclusions

Acoustic emission monitoring proved to be an efficientmelhod to increase safety at proof tests of nuclear reactors andto detect regions for further inspections by other NDT methods.
During the proof leSts of four reactor units, measuring and evaluation methodology has been perfected, instrumentationacquired, a team of experts trained: everything is prepared forroutine applications. Experience gained during the proof tests
has led to many improvements both in measuring hardware andsoftware. These additions and alterations are applied at the pr0duction of the Defectophone systems from 1990.
AcknOWledgment
Applied methods. measuring hardware and software arecommon results of a coordinated research and development
work. The most important contribution was given by Messrs.
G. Lafranco, T. Mar6thy. A. Peter, B. Iuh4sz. Gy. Szekely. The
AB monitoring measurements were performed with the participation of engineers and teclmicians of the KFKI as well as
those of EROKAR (project leader: Mr. P. SZOCs). The work wasstimulated and highly facilitated from the side of the nuclear
power plant by Mr. P. Trampus. Thanks are due to all contributors.
References

ASTM (1985), ASTM Standard. E569·85: Slandard practice foracoustic emission monitoring of sttuctures during controlled
stimulation, Philadelphia.
H.L. Dunegan (1989), "AB - for when you absolutely cannot
afford a failure", 1. Acoustic Emission. 8(1/2), S71·S74.
TJ. Fowler, I.A. Blessing, PJ. Conlisk and T.L. Swanson(1989), "The MONPAC system". 1. Acoustic Emission, 8(3).
1-8.
P. Pellionisz. P. SzUcs and P. Trampus (1989), "Acoustic emission test of WWER-440 pressure vessel", Proc. 12th World
Conf. Ofl NOfl-DestrllCtive Testing, Amsterdam, April.
C.B. Scruby and K.A. Stacey (1988), "AB measurements on
PWR weld material with inserted defects", I. Acoustic Emis·sian. 7(2), 81-93.

23

Progress in Acoustic Emission VI
Sensors
Method for Measurement of Sensitivity Degradation of Acoustic Emission Transducer
Y. HIGO and H. INABA
Calibration of Conical Transducers in Seismic Model Tests
P.-L. UU. T.-H. SONG and T.-T. WU
Characterization ofAE-Transducers Using Davies' Bar and Laser Inted'erometry
A. UMEDA and K. UEDA

Data Reproducibility Improvement at Changing Contact Conditions for the Electrodynamic Sensor by Means ofUsing Electroaeoustic Effect
YA. I. BULBIK, V. V. BULAVKIN, N. V. VASILENKO and A. G. KOZLOV

MedicalVibration Arthrography in Degenerative Knee Joint Disorders
C.-C. JIANG, T.-T. WU, Y.-R. UU and C.-S. UU

The Effect of Haversian Remodeling on AE Response of Compact Bone under Quasi-Static Tension
S. TANAKA, T. HARA, T. SHIBUYA and T. KOIZUMI

Non-Destructive Evaluation of Yielding Stress of Bone during Fracture Treatment Using Acoustic Emission
TeclmiqueN. INOUE, Y. HIRASAWA, Y. WATANABE, Y. ARAI, H. rSUNEOKA and Y. HATANAKA
Nonlinear Behavior with AE of Cortical Bone Treated with Acid lDlder Tensile Loading
T. HIRAI, T. KATAYAMA, H. YAMAMOTO, M. INADA. N. INOUE, Y. WATANABE and Y. ARAI

Structures 2
Enhanced Fatigue Crack Detection in Aging Aircraft Using Continuous Acoustic Emission Monitoring
S. L. McBRIDE. Y. HONG and M. D. POllARD

Development and Calibration ofDownhole Triaxial AE Detectors for Subsmface and Civil Engineering Ali
Measurements
S. NAGASHIMA. H. MORIYA and H. NIITSUMA
Characteristics of Acoustic Emission Waveform during Slope Model Test
A. CHICH/BU. T. KISHISHnA and T. KIKUCHI

Subsurface 3
A Theoretical Study of AE Traveling through a Fluid-Filled Crack with Application to Characterization of a
Geothermal Reservoir Crack
K. HAYASHI and K. SArO

Subsurface AE Source Location by Multi Zone Modeling for Uncertain Velocity Structure
S. OKADA and H. NIITSUMA

Applicability ofAcoustic Tomography Techniques to Evaluate the Instability of Stressed RockK. ITAKURA and K. SATO

Virtual Calibration Method for Neural Sowce Location in Subsurface AE Measurement
A. MIYAZAKI and H. NIITSUMA(continued to page 30)24

Effects of Wave Velocity Change on MagnetomechanicalAE in Sintered Iron
Noboru Shinke and Yoshitugu Ohigashi
Abstract

Magnetizer

Magnetomechanical acoustic emission (MAE) behavior
shows a sensitive dependence on the change of the SlrUClUre in
ferromagnetic materials and can be applied for the nondestructive measurement of fenomagnetic materials. Wave propagation properties also reflect the change of the structure in them81eriaL The purpose of this paper is to experimentally investigate MAE behavior as a function of the wave velocity in sintered iron. A correlation exists between MAE and the wave
velocity in sintered iron regardless of the change of the lcind,average diameter and fonning condition of iron powder. Thatis, the intensity of MAE increases in inverse proportion to thewave velocity. Thus, this MAE phenomenon can be applied to
the nondestructive evaluation of sintered iron.

D

_

0

~Magnetiz1ng' - generator

1. introductionoMachine parts made of sintered iron are used widely,because they can be manufactured economically in large quantities. As the sintered iron has a unique structure with many
pores, the mechanical and other properties change greatly
according to the porosity, size, fonn and distribution of thepores. There have been many reports on how the mechanicalcharacteristics depend on the porosity (Sarac et al.• 1974; Bardand Williams. 1984). The wave velocity is generally used as IUl
imponant characteristic value to evaluate some materials.
because it is sensitive to the change of the metallurgical structure and is easy to measure nondestructively. From such a viewpoint. the method of measuring the porosity of sintered materials nondestructively relied on an adequate correlation between
the porosity and the velocity of ultrasonic waves (pohl, 1969;
Mihara et al., 1988). The discontinuous magnetization in theprocess of magnetizing ferromagnetic materials works to generate magnetomechanicaJ acoustic emission (MAE) (Lord,
1975). It was theoretically pointed out that MAE are generated
in the process of discontinuous shifts of 90° magnetic domain
walls and rotation of magnetizing vector. and that the intensityof MAE increases in inverse proportion to the wave velocity inferromagnetic materials (Ono, 1986; Shibata and Sasaki,1986). The MAE intensity depends on the change of the metallurgical structure in ferromagnetic materials. To utilize MAE asa new method of nondestructive evaluation of sintered m81erials, effects of wave velocity change on MAE have been experimentally investigated in this report.

0

---'

PersonalcomputerFig. 1 Schematic diagram of experimental equipment.

1. Experimental Apparatus and Method
2.1 Analyzing ApparQlIlS 01 MAE Waves

One way to estimate MAE intensity is to measure AE
COlDlts during the basic period of the magnetizing frequency,
but it is also important to know the amplitude distribution ofMAE. In this research, the AE colDlting method was adopted.
The automatic MAE analyzer used to measure MAE waves produced by test-pieces is shown in Fig. 1 as previously reponed(Shinke et al., 1985). The analyzer is designed to automatically
set the discriminating level in the input section of the COlDlting
circuit instructed by the computer. Therefore. it operales fullyautomatically after starting. and all the measurement data arerecorded on the memory. These data are indicated in the fonn ofgraphs or of numerical data in hard copies. The detector used for
detection of MAE signals is a PZT transducer which has a resonance frequency of 250 kHz. The transducer oUlpUt is amplified
by 80 dB using a pre-amplifier and main-amplifier.

2.2 MeasUTemIllll of Wave Velocity
Received 21 JlUIuary 1991, in reviJed fonn, 25 JlUIuary 1992. NoboN
Sbinke is affiliated with Faculty of Engineering, KlUIsai University,
Yamau: 3 - 3 -35. Suita, Osaka 564.1apan and Yoshitugu Ohigashi iswilb Osaka Tilanium Co., Ltd., Higashihama I. Amasasaki, Hyogo 660,

The ultrasonic flaw detector used for measuring the wave
velocity of given test-pieces is made by Sonic Instruments Inc.
IUld its transducer has a resonance frequency of 5 MHz and outside diameter of 6.35 mm, coupled to the test-piece with vacuum grease.

Japan.
JoumaJ of Acoustic Emission
Volume 10. Number 3/4

250730-0050J92/1 0025429
Copyrigbtel992 Acoustic EmissiClll Group

2.3 Test·P~ce and Experimental Method

100

The test-pieces are composed of atomized iron powder or
mill-scale reduced iron powder. The distribution of diameters isshown in Fig. 2. The test-pieces were formed into a rectangularshape with several pressures at room temperature, and then sintered at 1423K in an atmosphere of the decomposed ammonia
gas. Test-pieces were machined to a shape with the dimensions
150(L) x 9(W) x 9(H) mm. The wave velocity and density fortest-pieces used in this experiment are shown in Table 1. To
magnetize the test-pieces, we used a function generator, power
amplifier and magnetizer with a compact ferrite core aroundwhich a coil is wound as shown in Fig. 3. Since the magnetic
field produced by the magnetizer is not fixed but changes in theform of ttiangular waves, the intensity of the magnetic field isshown by the peak value of the current field strength applied tothe magnetizing coil.

-Mill-scale
reduced Iron powder- - - AlOmlzed
Iron powder

-~

a 50

o

o

50

100

150

C,(lJm)Fig. 2 Relation between the cumulative distribution percentage Q and the sieve opening C for iron powders.

Table 1 Test-Pieces
Mill-scale reduced iron powder

Atomized iron powder

Test-pieceNo.

Diameterd(Jun)

Velocity
S(mls)

DensityD(g/cm3)

Test-pieceNo.

Diameter

VelocitydUun)

S(mls)

DensityD(g/cm3)

1
2
3
4
5
6
7
8
9
10
11
12

76
76
76
76
51
51
51
51
37
37
37
37

4140
4398
4730
5080
3992
4380
4800
5168
4080
4458
4835
5140

6.09
6.33
6.61
6.95
5.94
6.25
6.66
6.98
5.94
6.26
6.67
6.96

13
14
15
16
17
18
19
20
21
22
23
24

63
63
63
63
43
43
43
43
32
32
32
32

4157
4620
4952
5248
4190
4613
4944
5280
4366
4645
5057
5338

6.31
6.70
6.97
7.23
6.24
6.63
6.89
7.21
6.41
6.62
6.95
7.20

1 - - - - - 30 - - - - t
1088"1'_ _-;-_ _--;-_ _--;-_ _......_ _-:-_ _-,

,.

II

I

18E

I

C

11

•

0

•

•

0

d=43J.1rn!

I=300mA

f=60Hz

I'

I.
I
••

I

I

·-----I--·-l-.~~-_.-"'+'1"---1
I i · II 0 0 I·. ~ I
I
j

C

0

I
j

i

I
!.I

Y

eI

I Iron powder

I°of.. tI·.
Too··.
!.

uI: Atomized

I·

C
o

~

I
I
I

..:

•

~

•

_ j

.:t4l

0 I
~ 01

• ~

!
1 ~ -I ~ to••,... .
·_··_·-_·-t--···-·I-~ ~-f--"- ~---r"~ -Fig. 3 Details of magnetizer.

Figure 4 shows examples of the total counts of MAE signals produced by magnetizing the sintered iron with differencesof wave velocity. The magnetizing conditions were; frequency f

II

i

I

I

• p~

180e

3. Experimental Results and Discussion

I

188S!
•• ,~..!

T

Ferrite C~~(7x15mm£)I!

C011. (o=0.2mm)
n=2000T

!.

1
I

!

!

i

I!
Z80

SOB

I

II
e80

'6..
'5"_

~.

'

I.

UBO

0

~o
.~

I.
I

l~O!

iI

OJ

1400

noo

ThreehoId Vol te98 (aN)

~

••

•
ZBOO

Fig. 4 Examples of the total AE counts for test-pieces withdifferent wave velocities.

xur
3

5

x11f

4

-;::-2

-

cj

o

-...

cj 3
o

U

,g
1

~2
~

o

o

200

100

300

1

400

I (rnA)o

0.6 - - - - - - - - - , . - - - , - - - . . ,Atomized iron powder
d::43~m5>'4'! /~_;5280

0.3 t -

I
Atomized i ron powder
1.0 _d=43J.1rn
I=300rnA

(m/s)

-

i-r/°

;?
.?
0.2 -~r
.1"

0.1

o

Z·$i/"/---' .

0.8

C'l

. -.---.-.---...
8=5900 (ml s) -.,•.. . --

C 0.6

I

IQ)

/

200

300

400

I (rnA)
Fig. 5 AE coWl1S per one cycle T and mean event energy e as afunction of magnetizing current 1 for test-pieces with different
wave velocities.

~

l/~~.1~4~~~-

·
/

/. 0
•
0/

100

I

0

l
.
_0--._.-

0.4

Y

/

$

O-O----"'!O

I I

200

1.2

./
0.4 t---/ - - 4 - - - + - - - - - t
•

100

50

f (Hz)

0.5 t---;----=.~- - - - - i. / 8=4190 (ml s)

t 'I

20

7

f::60Hz

--

10

I

4

./ ~Cb'"/0.2 I----;.·~o.....,.., - . /.~~

-j-,

o
5

10

20

'pOJ

~

50

100

200

f (Hz)= 60 Hz and magnetizing current I = 300 mAo In Fig. 4. T repre

Fig. 6 Relation between T..}2nf and e vs. magnetizing frequency f for test-pieces with different wave velocities.

sents AE counts per one cycle of the magnetizing currenL The
test-piece with the wave velocity S of 5900 mls is the pure iron(more dian 99.9 % purity) which hardly contains the pores. The
test·pieces of smaller wave velocity showed higher AE counts.
The amplicude of MAE waves were also higher. indicating thatMAE changes according to the difference of the wave velocity
in sintered iron. In this paper. in order to estimate the intensityof MAE signals generated from the test-pieces. we used T (AEcounts per one cycle) and i!' (the mean event energy).

First. we examined how the measuring conditions such as
magnetic field intensity and magnetizing frequency affected
MAE signals generated from test-pieces. Figure 5 shows therelation between the intensity of MAE signals generated during
the magnetization of the test-pieces at 60 Hz and various magnetizing current up to 400 rnA. The results showed that T and etend to saturate at the magnetizing clUTent of 300 mAo This

2.4.........

1.00

h .....

2.3

••

2.2

0.95 i-

~.

0\

~2.0

•o

-

•

"!.

V=500jV

J-..

1.7

,.

0.9

0.8

0.75
0.6

. '.

0.

o

1.6

. '....
•••

reduced Iron powdl!1'-0- AtomIzed
I-Iron powder

0.6'?:.. .. •'.

MlII-sl:8le~•••... 0....

'.0f=60HzI--t..

1:300mj

1.4

0.8

0.9

1.0

The mean event energy e based on amplitude of MAE increases
with a rise of magnetizing frequency.

0 .....

-

0.7

Hz. It was observed Ihat MAE with Ihe larger amplitude is gen-

"'0

1.8

•,{l

erated according to the increase of the magnetizing frequency .

'.

leu
"leu

It·
•

Fig. 8 Normalized density DID* vs. the normalized wave
velocity S/S·.

0'.........

• 2.0.;Sis·...
',..'.

2.2

0.80

SIS-

...

2A

l

oY-

)..0
0, I--

0.7

I

l

0

0 0.85~..

f=60Hz
I=300mA

0.6

•.
.
,'.
.

il

0.90

~....

Mill-scale- - reduced Iron powder
000- Atomized
1.9 IIron powder

1.8

Mill-scale- - reduced Iron powder-0- Atomized
Iron powder

•J
l•

l

'"

~ 2.1

1

I

0.7

0.8

0.9

SIS·
Fig.7 Relation between the normalized AE counts Trr*. mean
event energy ire* and wave velocity S/S* under magnetizing
condition of 60 Hz and 300 mAobehavior is commonly observed for the test-pieces having different wave velocities. When a test-piece is magnetized under
the alternating fields. the eddy current is produced in the outerlayer of the test-piece. The magnetizing frequency affects theskin depth of the magnetic flux and Ihus the source volume ofMAE. Figure 6 shows the relation between T..J2nf and e vS.
magnetizing frequency up to 150 Hz. T..J2nf is Ihe product of
Ihe square root of magnetizing frequency and AE counts. Thisparameter allows a comparison in terms of AE counts given by
Ihe outer layer of Ihe test-pieces per unit Ihickness even at dif-ferent magnetizing frequencies. When a sufficiently strong
magnetic field was applied.lhe values ofT..J27tf increase with arise of magnetizing frequency. and tend to saturate at about 100

The effects of wave velocity up to 5338 mls in sinteredirons were examined. In order to evaluate the effects of wave
velocity change on MAE. AE counts. mean event energy andwave velocity in sintered iron were normalized by their respective values for pure iron. Figure 7 shows the relation between
the normalized AE counts Trr*. mean event energy ere* andwave velocity S/S* using a reference magnetizing condition of
60 Hz and 300 rnA. In Fig. 7. T* is AE counts. e* is mean event
energy and S* is wave velocity for pure iron. The normalized
AE counts decreases in proportion to the increase of normalized
wave velocity. The normalized mean event energy decreases
lineally wilh the increase of Ihe normalized wave velocity. It
can be seen in Fig. 7 that the effects of wave velocity change
on MAE are not influenced much by Ihe change of the kind andaverage diameter of the iron powder within the range of wave
velocity in this experiment. The relation between the normalized density DID* and the normalized wave velocity is shownin Fig. 8. Here. D* is the density for pure iron and takes thevalue of 7.86 g/cm3• The normalized density tends to increaselinearly wilh the increase of Ihe normalized wave velocity. The
skin depth due to the eddy current effect is inversely proportional to the square root of the product of the magnetizing frequency. conductivity and permeability of a sintered iron.
Sintered iron has a characteristic that the e:onductivity and permeability become smaller with the decrease of the density(JPMA, 1987). Therefore, the source volume of MAE becomes
greater wilh the decrease of the density of Ihe sintered iron. The

K.S. Bard and J.D. Williams (1984). "Relationships Between

sintered iron with the smaller density has a slIUcture which contains many internal pores. It appears that the pores are imporlant in consideration of wave velocity change. The pore is oneof the primary factors detennining the pinning source of magnetic domain walls. This increase of pinning source implies ahigher MAE intensity. The experimental results of wave velocity change on MAE generated by sintered iron can be explained
from the standpoint of the source volume of MAE and the pinning source of magnetic domain walls.

Process Variables and Density of Explosively Compacted Iron
Powder", International J. Powder Metallurgy and Powder
Technology, 10(1). 23-32.
D. Pobl (1969). "Ermittlung der Zugfestigkeit von Sinter·
metallen mit Ultraschallmessungen, Arch. Eisenhiltt, 40,647.
650.
T. Mihara. M. Obata. T. Watanabe and H. Shimada (1988),"Nondestructive Evaluation of Porosity in Copper Powder
Sintered Products by Ultrasonic Velocity Measurement", J.
JSNDI, 37(5), 376-380 (in Japanese).

4. Conclusion

The dependency of wave velocity on MAE was experimentally investigated by using sintered iron with different wave
velocities. The results indicated that the intensity of MAE signals varies inversely with the increase of the wave velocity insintered iron. This suggests that MAE phenomenon can beapplied to the nondestructive evaluation of sintered iron.

A.E. Lord, Jr. (1975), "Acoustic Emission", PhysicalAcoustics, vol. XI, eds. W.P. Mason and R.N. Thurston.
Academic Press, New York. pp. 290-353.
K. Ono (1986). "Magnetomechanical Acoustic Emission - A
Review", Progress in Acoustic Emission Ill, JSNDI. Tokyo,
pp. 200-212.

Acknowledgments
The authors are grateful to Japan Powder Metallurgy Co.,
Ltd. for providing the test-pieces and to Eishin Kagaku Co.,
Ltd. for measuring the wave velocity.

M. Shibata and H. Sasaki (1986). "AEM and BN Signals in
Steel", Progress in Acoustic Emission Ill. JSNDI, Tokyo, pp.
213·221.

References
N. Shinke, H. Maeda and Y. Usuki (1985). "Development andApplication of Automatic Analyzer for AE during Magnetization", J. JSNDI, 34(8), 506-512 (in Japanese).

A. Sarac, V. Miskovic, E. Dudrova and E. Rudnayova (1974),"The Dependence of Mechanical Properties of Sintered Iron
Compacts upon Porosity", Powder Metallurgy International.
6(3), 128-132.

Japan Powder Metallurgy Association (1987), "Syoketu Kikai
Buhin", Gijitusyoin. Tokyo, pp. 108-114 (in Japanese).

29Progress in Acoustic Emission VI
Concrete
Relation Between Acoustic Emission and Absorbed Energy ofReinforced Concrete and Fiber Reinforced Plastic
T. UOMOTO, T. YAJIMA and T. NISHIMURA
Relation Between Fracture Touglmess and Widths ofFracture Process Zones in Mortar and Concrete Analyzed by
Improved AE Source Location
S. NOSEKI. H. M/HASHI and N. NOMURAAE Properties of Concrete with CoIlOded Reinforcement
Y. MURAKAMI. H. YAMASHrrA and M. OHTSU
Rate Process Analysis ofAE Activity in Core Test of Deteriorated Concrete
K. MAT.S'UYAMA, A. ISHIBASHI and M. OHTSU
Fundamental Study of Acoustic Emission Generated in Comer Part ofReinforced Conaete Rigid Frame under Cyclic
Loading
S. YUYAMA. R. TOMITA, T. OKAMOTO, S. KAlIO. M. OHTSU and M. SHIGEISHI

Metal
Evaluation of Stress-induced Martensitic Transfonnation Velocity in Cu-Al-Ni Shape Memory Alloy Single
Crystals by Acoustic Emission Method
K. YOSHIDA. A. TAKAHASHI. K SAKAMAKI and H. TAKAGIThe Effect ofPrecipitate Forming Elements in Al4.5% Mg Alloys on the Measured Acoustic Emission and on

U1ders BandDefonnation
D. L. ARMENl'ROUT and S. H. CARPENTER

Study of the Fracture Mechanism of Hydrogen-Attaeked Steel by Acoustic Emission Technique
J. TAKATSUBO and S. YAMAMOTO

Acoustic Emission from Crack Growth in 7050 Aluminum and 7f115 Aluminum as a Function of Temperature andHeat Treatment
S. L. McBRIDE and Y. HONG
Analysis of Microfracture Mechanism in Al203 by Acoustic Emission
M. ENOKI and T. KISHI
Acoustic Emission Behavior during Heating/Cooling of Pd·Cu Alloys
M. YAMANAKA, K. ASAOKA. K. YOSHIDA and K. SAKAMAKI

Ceramics
Evaluation of Grade in the Grinding Wheel by Acoustic Emission
N. SHINKE and Y. ISHIDAAE Studies on the Microfracwre Process in Silicon Nitride
S. WAKAYAMA. M. IWATA and M. KAWAHARA
Nondestructive Evaluation of Ceramic Coating by Acoustic Emission and Ultrasonic Testing
M. SHIWA. H. MORI. T. KISHI, M. OHTSUKA, A. rrOH and Y. SUGrrA

Composites 3
Fiber Break Characterization by Acoustic Emission in a Single SiC Fiber Aluminum Matrix Model Composite
N. CHAMlOT-CLERC. P. FLEISCHMANN and R. FOUGERES(continued to page 34)
30

Origin of Acoustic Emission in Naturally Aged AluminumLithium Alloys
F. Zeides and I. Roman
Abstract

Table 1 Chemical composition of principal alloying
elements of the Al-Li alloys studied.

The present study identifies the origin of acoustic emission (AE) operating during plastic deformation of 2091 andLockalite Al-Li alloys in the'T3 heal treatment condition. AE
intensity characterization techniques. i.e.. AE nns voltage andpeak amplitude distribution. were employed to compare the AE
generated during tensile and compressive loading. It has beenconcluded that in both alloys in the naturally aged condition.
AE results from plastic deformation for both uniform and jerky
Oow. The second potent source of AE. particles failure. is either
non-operative or negligible.

Alloy

Li
Lockalite
2091

Lockheed
Pechiney

2.1
2.0

Element
Mg
2.2
1.2
2.4
1. 7

Cu

a
0.12
0.10

mm gage section. Specimens machined from the 2091 alloy
were of the sheet thickness. In the case of the Lockalite alloy
specimens were of 2 or 5 mm thick and were taken in the rolling direction with the gage surface perpendicular to the rolling
plane. Tensile tests were conducted utilizing a J.1. Lloyds
machine. All samples were polished prior to testing. Compression tests were conducted in plane strain loading mode to
identify AE source during plastic deformation. Acoustic emission characteristics were evaluated during mechanical testing
utilizing a micro-computer based system (Model 5000. AET
Corp.) comprised of 175 kHz resonant transducer (MACI75.
AET). preamplifier (160. AET) with 125 to 250 kHz bandpass
filter and a rms voltage processor (201. AET).

1. Introduction
Aluminum-Lithium (Al-Li) alloys are under extensive
investigation due to their lower density and higher elastic
modulus in comparison with other aluminum base alloys usedin advanced structural applications (Divecha and Karmarkar.
1986; Lavernia and Grant, 1987). However. Al-Li alloys areprone to the strain localization that impairs their deformationand fracture behavior. Better understanding of deformation andfracture mechanisms will assist in materializating of the highpotential of Al-Li alloys.

3. Results and Discussion

It has been noted (Roman et al.• 1987) that abundant
acoustic emission (AE) was produced during tensile test of a
Lockalite Al-Li alloy in both naturally and artificially aged
heal treaIment conditions. Thus. the use of AE can contribute ro
the characterization of Al-Li alloys. provided that AE origins
are properly undersrood.

Significant acoustic emission was detected during tensile
deformation both in the elastic and plastic regions as shown in
Fig. 1. The upper portion of Fig. 1 contains a characteristic AE
nns voltage vs. extension curve and shows that. initially. theAE is of a continuous nature with a maximum at the end of theelastic region. followed by a change to apparently burst-type
AE activity in the plastic ponion.

The present repott gives an identification of AE sources
during plastic deformation of AI-Li alloys and deals with alloys
in the T3 heat treatment condition.

The peak in the rms voltage curve of the continuous AE
happens shortly before general yielding. The apparently bursttype AE observed in the plastic region coincided with thePortevin-Le Chatelier serrations in the load-extension diagram.
AE obtained in the tensile tests has been analyzed with a microcomputer based 5000 AET system. Cumulative distributions ofAE events by peak amplitude for both continuous and apparently burst-type AE are shown in Fig. 2. The shapes of the two
distributions corresponding to the two types of AE are very
similar. and thus it has been concluded, that both types of AE
had common AE origin. This is in accord with waveform observations using a transient recorder as well as the monitoring on
an oscilloscope during testing.

2. Experimental Procedures
Two Al-Li alloys were investigated in this study. One is a
Lockheed developed alloy. Lockalite, received as a 25 mm
thick plate, with a partially recrystalized pancake-grain structure elongated in the rolling direction. The other. a Pechiney
2091 Al-Li alloy. has been received in the form of a 2.5 mm
thick sheet with equiaxed fully recrystalized grain structure.
Chemical composition of both alloys is given in Table 1.

n.

Tensile test employed samples in the as-received.
condition. Longitudinal Oat tensile samples had a 6.25 x 32

Compression tests were employed in order to assist in theidentification of AE sources active during the deformation of
the two investigated alloys. The plane-strain mode of compression was employed. Details of the test procedure havebeen reported elsewhere (Zeides and Roman, 1989). Intense

Received 31 May 1991. The authors are affiliated wilh Graduate Schoolof Applied Science &. Technology. the Hebrew Univenity or ICfUsalcm
91904. IsraeL
Iournal of Acoustic Emission
Volume 10. Number 3/4

Manufacturer

310730..ooS0192110031..Q33
Copyrigbt@l992 Acoustic Emisam GroupTENSION

1aBae

1
I~--­

1e~6 i11-------

186

10

COMPRESSION
25 28 31 34 37 49 43 46 49 52 55 58 61 64 bl

LOG OF CUM. OIST. 1 OF EYENTS 8Y PEAK AMPLITUDE <De>
Fig. 3. Cumulative distributions of AE events by peak
amplitude produced in tension and compression, the upper andlower charts correspondingly. The linear approximation is
given by doned lines.
acoustic emission was produced during compressive loading inboth alloys. Load and AE nns voltage vs. cross head displacement charts are given in the lower portion of Fig. 1. Bothcontinuous and apparently burst type AE were observed.

CROSS HEAD DISPLACEMENT
Fig. 1. Load and AE nns voltage vs. Cross head displacement
obtained in mechanical tests in 2091 alloy. The tensile test has
been conducted at 10-4 sec-I initial strain rate.

19

1
LO~

~" " " "'. l. -. .I. . - -&. .-\.-:. . ='L=----.1--L~

25 28 31 34 37 49 43 46 49 52 55 58 61 64 67

OF CUH. DIST. 1 OF EVENTS BY PEAK RHPLITUDE (DB)Fig. 2. Cwnulative distribution of AE events by peak amplitude
obtained during different stages of deformation. Doned andsolid lines relate to the continuous and apparently burst AE
types correspondingly.

The shape of cumulative peak amplitude distribution of AE
events produced in compression was similar to that of tension.
as can be seen in Fig. 3. In both cases. the major portion of the
cumulative distributions can be approximated by linear dependence with the similar slope. It follows, then, that the major AE
source in plastic deformation of both alloys in T3 heat treatment condition stems from plastic deformation process anddoes not relate directly to the constituent particles failure.
As has been mentioned earlier, jerky plastic flow resulted
in spikes of AE rms voltages. At the beginning of loading, one
or two bands were produced on the surface of a sample while theload level remained constanL Striations nucleated at one of thespecimen ends and propagated through the entire gage length,
so that each new jump formed in front of the previous one.
When the entire gage length was swept by the striations, a new
Ponevin·Le Chatelier band originated at that end propagated in
the opposite direction. Details of AE accompanying Ponevin·
Le Chatelier flow will be published elsewhere. It has beenfound, that heal treatment affects AE in two ways. First, thepeak height of continuous AE varies with an artificial aging
going through a maximum. Second, constituent particles failure begins to contribute to the AE and finally becomes the
predominant AE source.

4. Summary
Acoustic emission accompanying deformation process inthe two Al·Li alloys in T3 heat treatment condition originatesfrom plastic deformation during both uniform and jerky flow.

The second potent source of AE in common AI-base alloys.
constiruent particles failure, is either nonoperative or negligible.

Acknowledgement
The .authors wish to acknowlege useCul discussion with
ProCessor K. Ono of University of California. Los Angeles.
who also provided the Lockalite plate used in this study.

References
A.P. Divecha and S.D. Karmukar (1986), Advanced Materials& Process. 130(4), October. 74-79.

EJ. Lavemia and N.J. Grant (1987). J. Material Science. 22.
1521·1529.

I. Roman. K. Ono and C.H. Johnson (1987), J. Acoustic Emission, 4(2/3), 111-115.
F. Zeides and I. Roman (1989), J. Acoustic Emission. 8(3), 4749.

33

Progress In Acoustic Emission VI

Acoustic Emission Characterization of Failure Behavior of SiC FiberlAI Composite
Z. G. JIN. X. H. PAN. C. ZHU and G. G. SONG
Acoustic Emission Behavior during Tensile Fracture of Resin-Impregnated Sttands of Pirch-Based Carbon Fiber
A. NANJYO. M. MOHRI and K. ONO
Adhesion ofTitanium Coated Films on Various Substrates Measured by the Scratch Tester with an Acoustic
Emission Sensor
I. TAKANO. S. ISOBE. K MATSUI. T. TAIRA and N. INOUE
Quantitative Evaluation of Bending Strength and Fracture Dynamics of Laser Fusion Produced Titanium Aluminide
Coatings by AE Wave Characterization
M. TAKEMOTO and M. SHINYA

11 th International AE Symposium
The 11th lABS, sponsored by the JSNDI, was held on OCL 26 - 29, 1992 at Sun Palace Convention Center inFukuoka. Japan. The Symposium attracted about ISO participants from 10 countties. Paper and Program Committee
Chair was Prof. K. Takahashi (Kyushu Univ.) and the Organizing Committee was chaired by Prof. T. Kishi (Tokyo
Univ.) while the Secretarial Committee by Prof. M. Ohtsu (Kumamoto Univ.).

r~\~r:·= ~.L-_..,;~"":""""

During the Symposium, Prof. K. Yamaguch~ who retired from Tokyo Univ. in April 1992, received Achievement Awards
from AEWG and CARP. which were conveyed by Prof. S. Carpenter and Dr. T. FOWler, respectively.

Progress in Acoustic Emission VI
Proceedings of the Eleventh International Acoustic Emission Symposium,
eds. T. Kishi, K. Takahashi and M. Ohtsu, Oct. 25-29, 1992, Fukuoka,
Japan. Publ. by JSNDI, Tokyo; xx, 604 pages.
Cost: $150 plus shipping of$8 In the US and $10 elsewhereSend prepaid order to Acoustic Fmtssion Group308 Westwood Blvd, Box 364
Los Angeles, CA 90024-1647 USA
FAX 1-818·368-8309
34

Analysis of Artificial Acoustic Emission Waveforms Usinga Neural Network
Hironobu Yuki and Kyoji Homma
the field of nondestructive evaluation of materials, an identification of AE source using an associative memory (Orabec andSachse. 1989), an eddy current defect charaecerization using aperceptron (Udpa and Udpa, 1990), and others, have been
investigated. The neural network is effective in problems where
equations of the inpurIoutput relation are difficult to obtain andprovides learning capability 10 suggest the unknown inpuri
output relation.

Abstract
This paper deals with a method of Acoustic Emission (AE)source analysis using a neural network. Artificial AE source
signals caused by pencil lead breaks were used 10 confinn abe
validity of the method. Derected wavefonn dll1a were processedby the two.layer network that constiwted as a software on the
personal computer system. The memory of the network, corre·
sponding to the inpurioutput relation of the system, wasformed by learning. Even though there were noises in detected
waveforms, low noise source waveforms could be obtained by
the network. In case of a small number of learning patterns,
however, incorrect source waveforms were obtained by the network. It was demonstrated that when additional restrictions
were imposed on the memory, appropriate waveforms could beobtained using the memory learned with a few pattern. Anothertype of network that could estimate response functions of thesystem from the information of the source location was also
investigated.

This sWdy applied the neural network to the AE source
analysis by utilizing its learning capability, and investigatedthe validity of the method by means of an artificial AE source
and the two·layer network.
2.

Formulation

2.1 1nplll1oUlpUl ReloJions of AE System
The detected waveform u(t) as a time function can be
expressed by the equation of a convolution integral using thesource waveform set) and the response function ret) under theassumption of the linear system (Hsu et al., 1977, 1978).

1. Introduction
Acoustic emission (AE) source analysis is the only known
method available 10 represent qualitatively the fracture behavior at the location of the fracture source. This is done by measuring the elastic waves emitted by the energy release due to thecrack propagation of the materials (Hsu et aI., 1977, 1978;
EDoki and Kishi, 1988; Ohira and Pao. 1989: Homma andIshikawa, 1989; Kim and Sachse. 1989). The analysis belongs
to inverse problems (Kubo, 1988), which estimate the input
information from the output. because the waveform at the AE
source can be obtained from the derected waveform removing
effects of the characteristics of propagation media and AE sensors.

=u(t) f~ r(t· 't )·s('t) dt(1)

Hence, the source waveform s(t) can be obtained by a deconvolution integral:=set) f~ h(t- 't }u('t) dt(2)

where, h(t) is the inverse function of ret). Equation (2) could be
rewriuen as a discrete formula:
Si

= jaG
thioj uJ(3)

or in matrix form.

By assuming the linear system, when either the source
waveform or the response function is known and the detected
waveform is provided. the other can be obtained by means of adeconvolution integral. Diverging solutions, however, could
frequently result from the numerical calculations except for
approximate solution (Homma and Ishikawa, 1989), e.g. leastsquare method (Robinson and Silvia, 1978). Appropriate calculalion rechniques must be developed.

(3a)2.2 Neural NelWorlc
Recently, information processing using the neural nel·work has been applied to problems that are difficult to solve by
analytical and/or numerical treatments (Nakano, ed., 1990). In

Neural networks are composed of a circuit of modelneurons with idealized characteristics of multi.input vs. single
output elements as shown in Fig. 1. The relation between input
and output signals can be expressed as follows:

Received IS June 1991: in revised form. 17 December 1991. The aulllon
D

are affiliated with the Depanment of Mechanical and Control Engineering, the University of Eleeuo-Communicalions, Olofu, Tokyo, 182,

Y = f(I, wit j- 9)

Japan.
Journal of Acoustic Emission
Volume 10, Number 3/4

(4)

;"1

35

0730-OOS0l92l1 0035-04 I
Copyrigblel992 Acoustic Emission GroupXI

Sell8e.

E'.Ill.lIl

A I

r I .. l •

X2
Xl)

y

XII

Fig. 1 Schematic of a model neuron.
where. Wi is a weighting function called a synaptic weight
which expresses the bonding strength between neurons, and 9
is a threshold level (Nakano, ed., 1990). The memory of the
network is replaced by each synaptic weight. A perceptron isdefmed as the network of stratum composition of model neurons, and learning is carried out to vary synaptic weights interms of teaching signals from the externals.

Fig. 2 Experimental apparaws.
The neural network was trained using the waveforms
obtained by the experiment using an artificial AE source. The
experimental apparaws is shown in fig. 2. A mechanical pencil lead (0.5 mm dia., 2H) was broken on an aluminum alloy
plate (502 x 403 x 60 mm), and epicentral displacements in the
opposite side were measured by a capacitive displacementtransducer (Ohisa and Kishi, 1981). A set of waveform data
including various amplitudes and shapes were obtained bybreaking penci11eads wilh different lenglh. The waveform datacontains 13 waveforms and were recorded by SO ns sampling
rates, and 80 points (4 IU) were used for the analysis. Using an
averaging technique, noise in the detected waveforms was
reduced. Assuming that the plate can be regarded as infmite inassociation with the wave propagation, 13 teaching source
waveforms were calculated from each of the 13 recorded waveforms by using the analytical solution (Knopoff, 1958).

In this study, we use the two.layer network whose output
signal {y} is expressed as fonows:(5)

where {x} is the input signal and [w) is the memory of the network. In AE source analysis. (x}, {y} and [w) correspond 10 thedetected waveform (u}, the source waveform (s) and the inverse
matrix of the response function matrix [h). respectively, sothat the following equation can be deduced:(6)or

3.2 Full COMection Network
A full cormection network as shown in Fig. 3 was fustinvestigated. The network of this type uses all points data ofthe detected waveform to determine one point value of thesource waveform. and the learning equation is derived fromequations (6-8) by considering the inertia term:

Each weight was changed by the gradient descent method:~hij=- t aE/ahj j (£ > 0)

(7)

by defining the error function E as
E '" 1. ~ (51<- 51<)'(t>O,O<a<l)(9)where a and £ are coefficients of learning and t indicates thelearning times. These were chosen as appropriate accounting
for learning pauerns.

(8)

2 !taO
where {s} is the source waveform derived from equation (6), (s}is the teaching source waveform corresponding to luI, and £ isa coefficient of learning.

For learning of the network. we randomly selected a pair of
input and teaching waveforms, and presented them to the network. This learning procedure was repeated 500 times. Figure 4
shows Ihe shape of the memory formed after the 500 iterationof this procedure. An 80 x 80 memory matrix is formed. Each of

3. Calculations of Waveform
3.1 Learning Waveform36

.r----__---r-.....,..---......

-~-

__-~

3
Aft ft I,. l I

r" I

/.

..o
«II
U

I

/

Neu,AIN"l ......

8Mil'r.'"------j~-----------_I

lJ.

-I

-2 ~B---:8:":.5:---~-~I• .5:---~2--2~.S:---~3--3•.5~-.J.

T I m@ (IlS)

(a)
2

I.S

Fig. 3 Schematic of a full connection network.

,..
Z......

IInel,.lleal

.j

V

. A,

~ 8.5

.

:.

-

A

-v--

-iI.S
-I

Nenr,,'

8

8.S

Nf't.ork

2
2.5
TIme (Ils)

I.S

3

3.5

4

(b)
jWe.,hl;---to ulI1n

Fig. S Source wavefonn obtained by memory formed with 13pattern waveforms. (a) artificial waveforms. (b) simulated
waveform with step function.

I

Row

Fig. 4 The shape of the memory formed after the 500 iteration
with 13-panern waveforms.
the plane axes in Fig. 4 corresponds to the row and column ofthe memory matrix. and the height corresponds to the value ofthe malrix element. It can be observed in this figure that values
on the upper right side of the memory matrix reduce to zero. andthat values of the elements along the diagonal line are constant. Equation (3a) implies that to determine one-point valueof the source waveform in arbitrary time only need the previous
time data of the detected waveform, and that the elements along
the diagonal line have identical values. In other words. todetermine the source waveform, the mattix [h) in equation (6a)can be written as a triangular matrix. The shape of memory
matrix shown in Fig. 4 is consistent with this characteristics.
It implies that learning of the convolution integral would be
successful.
Using the memory matrix shown in Fig. 4. source waveforms can be obtained by feeding recorded wavefonns into the
network. Results are shown in Fig. S. Figure 5(a) shows thesource waveform which was calculated from the recorded wave-

form due to pencil.lead break without the averaging techniquefor noise reduction. This figure shows &hat the source waveform
obtained by using &he network coincides with the analyticalfonn. Furlher, &he noise in the source waveform can be reduced
without eliminating noise in recorded waveforms. Figure S(b)shows the source waveform calculated from the simulated
waveform at &he epicenler when the ideal step function wi&h theamplitude of I N was provided. Discrepancy in step rising
between the ideal step function waveform and the wavefonn
obtained by the network would be revealed in Fig. S(b). whichshould attribute the rising time of the waveform used forlearning. Improvements of this aspect may be expected whenhigher gradient waveforms by other artificial AE source could
be used for learning.
The number of recorded waveforms was altered to know
how many learning patternS might be required. When we used 6
recorded waveforms (6-panem) for learning. the memory shapecoincides with the I3.pattern case. and an appropriate source
waveform was also obtained. However. in case of only one pattern for learning. &he memory shape disagrees wi&h the I3-pattern case. and the source waveform was not appropriate (seeFigs. 6 and 7). Therefore. in case of full connection network.
multiple waveforms for learning are needed to obtain appropriate source waveforms. This is also obvious from &he memory

jWetll'ht

00,UmnRow

Fig. 6 Shape of the memory learned with l-pattem wavefonn.

3

.....

Z
....,

2
Nllura'

GJ
0

.
0

IJ.

Fig. 8 Schematic of a convolution form network.

4,-....--......--...--...--......-...--.......--,

9

-2

3

~AD.17tleal

-I

9

8.5

2.5
Time ells)

1.5

2

,...,
3

3.5

Z

2

4

.
CD

0

(a)

0

9

IJ.

-I
-2
An.l:rtleal

9

-e.s
-I

2

2.5

3.5

3

4

Time (us)\~-----

~ 9.5

..
(1.

I.S

8.5

9

(a)

Il

l'7,
9

Anal,.lle,,1

1.5
Ne".al9.5

Nel.o.k

1.5

2

2.5

~

,...,
3

3.5

I(

Z

4

Nel",nr"",-....../Ne" •• 'Time (us)(b)
Fig. 7 Source waveforms obtained by the memory learned with
I-pattern waveform. (a) artificial waveforms. (b) simulated
waveform with step function.

-e.S
-I

9

9.5

1.5

2

2.5

3

3.5

Time (us)shape formed by I-pattern leaming shown in Fig. 6, without
having the characteristics mentioned above.

(b)Fig. 9 Source waveforms obtained by the memory learned with
I-pattern memory in convolution form. (a) artificial waveforms. (b) simulated waveform with step function.

3.3 Convolution Network
Next, we investigated convolution fonn network as shownin Fig. 8. By combining equations 3, 7 and 8, the learning
equation is obtained as follows:38

of network types that satisfaclOry wavefonns can be obtainedin frequencies below several hundreds kHz. but at high frequencies above 1 MHz. source waveforms carmot be recovered. Thisis due to the same reason as the discrepancy in the rising phaseof the step input waveform. Hence. it is ncessary 10 improve
the frequency response in high frequency range.

22,....,

20

Convolution Form Network
x x

lD
"0
'-'

4. Estimation or Response Function

180....co4.1 Calculation of Response FlUlClion by Using a Network

Connect i on Network

0:: 16Z

A deconvolution integral is the procedure to determine pet)by providing f(t) and get) in the following equation:

(f)

get):

2

6

c1

8

10

12

Fig. 10 Relation between the number of learning pattern andthe signatlO noise (SIN) ratio.

= ._£D·I

r Ut

2

D' i·1

(i=O)
(i>O)

ro (Si+j- Si+j)uj+a~(t-l)

j ..

t .. o(£>O,O<a<l)p( t- 't )f ( 't ) d't:= p ( t )*f( t )(11)Therefore, the formation of a network memory is equivalent 10
the deconvolution integral. Representing the memory and the
response function by an n x n matrix and a veclOr. respec·tively. the response functions could be obtained by providing
the delta function data inlO the network. For the discrete formula, the fonowing function is used.

14

Number of Learni ng Pattern.6h1(t)J:(12)

By taking the source waveform as the input and the displacement waveform as the teaChing. the memory matrix could
be formed. This COJTesponds to the response function of forceto.displacement. On the other hand. when one takes the inverse
relationship between the displacement and the source. theresponse function of displacement-to-force could be obtained.
In order 10 obtain the source waveform. the latter only requires
the convolution integral. while the former needs the deconvolution integral. This deconvolution integral is liable 10 diverge. The neural network, however. allows the calculation of
the deconvolution integral. once the memory matrix is obtained.

(10)

Using the same wavefonns as in Sec. 3.2. SOO iteration
learnings were conducted. In the case of 13- and 6-pauems.
appropriate source waveforms could be obtained. We tried
whether I-pattern would be available. Figure 9 shows the source
waveform obtained by using I-pattern learning memory
matrix. These results show that appropriate source wavefonns
can be obtained iJrespective of the number of panems needed
for learning. It is expected that each weight was learned repealedly in the same waveform in termS of the restriction of thematrix mentioned above. This restriction is of great advantage
in the calculation 10 save the memory capacity of the matrix,
from n x n 10 n. Disagreements at the step rising still remained
10 resolve.

4.2 Estimation of Response F~tion
To learn the network considered above. the source location
has to be specified. In the case of AE associated with fracture,learning of the network is not possible after AE has been mea·
sured, since the geometty of the propagation media has alreadybeen changed. The source location, however. cannot be specified precisely before fracture. Therefore. network constitutions
are discussed 10 estimate the response function from the information of the source location.

3.4 Effects of Parameters on Network Calculations
Figure 10 shows the relation between the number of leam·ing pauem and the signal to noise (SIN) ratio defined as thereciprocal value of the variance of the error of the source wave·
form. It is demonslrated in this figure that in case of the fullconnection network., the SIN ratio is improved as the number oflearning pattern is increased. In case of convolution form network, however. the SIN ratio hardly changes. Therefore. it isfound that the output of the network is independent of the num·
ber of learning pattern in the convolution form network, while
the full connection network depends on the number of learning
pattern.

For learning of the network., pencil leads were broken at 8
points with an interval of 0.2H (O.OH. 0.2H, .... 1.4H; H is the
thickness of the plate) on the opposite side of the plate where
the sensor was attached. Then, response functions of displace·ment-Io-force were obtained by the deconvolution integral
using the network as mentioned above. These response func·
tions were used as teaching signals. Input signals were encoded
10 be the source location veclOr.

Effects of frequency component of the source waveform
were also investigated. We supplied a 4 J1.s long wave train
comprised of 80 points at 50 ns interval. The wave is of a sinefunction of various frequencies. Results demonstrate, regardlessFigure 11 shows shapes of memory matrices that are used
10 estimate the response function by providing the source loea-

393n" (o.nl

2n F CO.n)

Z 1.5

'-'
Q)

~ 9.5
0

lI.

9
-0.5

Wel,hth.. ""-I-1.5

np:n .... pttn ....
9

9.5

1.5

2

2.5

Fttnrt'nn

3

3.5

4

Time (Ils)

(a)

Time

(a)

3,---......-.....---_._-......- .......- ......----..--...
A I' (0.81)

2.5

AFC!!III ... "t" ..)2n F co.n)

Z 1.5
'-'
Ql

I

~ 9.5
:;,

9 k----Ir.----------

--I

-0.5
-I

-1.5 ~e-~9.':5-~:---I:"'.:-S-~2---::2"":.5:---3"'"--.3....
.. S-.....J4

We I, h t

h.. "..

Time (Ils)(b)
Fig. 13 Source waveforms obtained by using estimated response functions; (a) at the location of OoSH. (b) at the locationof O.SSH.

TIme

(b)
Fig. 11 Shapes of memory matrices that estimate the response
function. (a) without weight, (b) with the weight of ±O.IH.

A BCD
I

H

Plate

\

LJSensorFig. 12 Relative locations between the AE source and the sensor. (Response functions at points A and D are known, andthose at points B and C are unknown and to be estimated.)tion data to the network. In the figures, the column and the row
indicale the source location and the time of the response function, respectively. These memory matrices are formed by using
known source locations and response functions. When input
signals are encoded to leam, the weight represented by the nonzero width in the vector has to be considered. Figure l1(a)shows the shape of the memory matrix formed by learning
without weight, and Fig. ll(b) is the case with the weight of±O.IH. Figure 11 demonstrates that in case of no weight, nosuitable response function could be estimated, because the values at the location without learning have not been changed. On
the other hand, larger non·zero width makes iU-convergence.
so that the appropriate width must be considered.
After the learning procedure, response functions can beobtained by providing source location data to the network.
Then, source wavefonns can be obtained by the convolution integral, using above response functions. Figure 12 shows locations where response functions are known (points A and D),
and unknown (points B and C). Each point A, B. C and D is
O.4H, OoSH, O.SSH alld O.6H distances from the opposite side
where the sensor was attached. We detennined source waveforms on intermediate points B and C. Figure13(a) demonstrates that the source waveform in terms of the estimatedresponse function lies between the wavefonns by the known
response function. Figure 13(b) shows the source waveforms at
the location of O.SSH with encoding step of O.OSH. In the latter figure, the waveform in terms of the estimated function lies
in dle range between O.4H and O.6H, and that it is close 10 O.6H
line rather than intermediate. These results suggest that themethod of the network is useful for the estimation of the
response function.
5.

Acoustic Emission Source Wave Analysis" (in Japanese). 1981
National Conference on Acoustic Emission, JSNDI. Tokyo. pp.
57-62.

Conclusions
E.A. Robinson and M.T. Silvia (1978). Digital Signal
Processing and Time Series Analysis, Holden-Day Inc.• San
Francisco.

Analyses of AE source waveform using the neural network
were camed out and following conclusions were obtained;(1) Low noise source waveforms were obtained by using
the neural network from detected waveforms.
(2) In case of small number of learning patterns, it is efficient to obtain appropriate source waveforms imposing
additional restrictions on the memory matrix.
(3) By applying learning capability of the network, adeconvolution integral could be carried out without tendency of divergence.
(4) Providing AE source locations to the input. response
functions could be obtained as the output of the network.

L. Udpa and S.S. Udpa (1990), "Eddy Current Defect Characterization Using Neural Networks", Mat. Eval., 48. 342347.

References
M. Enoki and T. Kishi (1988), ''Theory and analysis of deformation moment tensor due to microcracking", Int. J. Fract.,38, 295-310.

1. Grabec and W. Sachse (1989), "Application of an intelligent
signal processing system to acoustic emission analysis", J.
Acoust. Soc. Am., 85(3). 1226-1235.
K. Homma and H. Ishikawa (1989). "AE Source Analysis of
fracture in Ceramics" (in Japanese). Trans. Jpn. Soc. Mech.
Eng., 55(515), A. 1607-1612.
N.N. Hsu. J.A. Simmons and S.C. Hardy (1977). "An Approachto Acoustic Emission Signal Analysis - Theory and Experiment", Mat. Eval.• 35. 100-106.
N.N. Hsu and S.C. Hardy (1978). "Experiments in Acoustic
Emission Waveform Analysis for Characterization of AE
Sources. Sensors and Structures". Elastic Waves and NonDestructive Testing of Materials. AMD-Vol. 29, ed. Y.H. Pao.
ASME, New York, pp. 85-106.
K.Y. Kim and W. Sachse (1989). "Study of brittle fracture byacoustic emission from indentation cracks". J. Appl. Phys.,
65(11), 4234-4244.
L. Knopoff (1958). "Surface Motions of a Thick Plate", J.
Appl. Phys., 29(4), 661-670.
S. Kubo (1988), "Inverse Problems Related to the Mechanics
and Fracture of Solids and Structures", JSME Int. J .• Ser. I,
31(2). 157-166.
K. Nakano. ed. (1990). An Introduction to Neurocomputing (inJapanese). Corona Publishing, Tokyo.
T. Qhira and V-H. Pao (1989). "Quantitative Characterizationof Microcracking in AS33B Steel by Acoustic Emission",
Metall. Trans., 20A. 1105-1114.
N. Qhisa and T. Kishi (1981), "Characterization of
Piezoelectric Transducers by Using a Capacitive Transducer for41

Papers to be presented at the 36th Meeting of

Acoustic Emission Working Group,
March 17-19, 1993, Denver, CO
CM. Fortunko, M.A. Hamstad and D.W. Fitting
Sensitivity Considerations for Wide-Band Acoustic Emission SensorlPreamplifier Subsystems
A computer model has been used to evaluate the suitability of five piezoelectric materials for use in broadband (10kHz-2 MHz) acoustic emission (AE) "point" sensors. The computer model accounts for the effects of the electricalloading of the piezoelectric material by the input impedance of the preamplifier and the mechanical loading by the
specimen and backing materials. The model predictions are compared with results of measurements, which are normalized using the NIST Standard Reference Material (SRM) "conical" transducer. The effects of selecting different

piezoelectric materials (PZT-SA, lead metaniobate, X-cut LiNb03, and PVDF) and transducer configurations("pinducer" vs. "conical") are evaluated experimentally and analytically. It is shown that materials exhibiting thehighest dielectric constant, £3,1, are best suited for high-performance AE sensor applications. In addition, it is concluded that field-effect transistors exhibiting very small noise currents, less than 1.9 fAlVHz, are required to maximize the low-frequency signal-to-noise performance ofPZT-SA sensors.
Marcel F. Leach
Method for Controlling Frequencies from Booming Sand
Acoustic emissions from booming sand, when shaken by hand in the laboratory, are characterized by a rhythmic
thrum of a few hundred Hz like the rubbing of fingers on a washboard, producing noticeable vibrations in the hand.
Typical signals of these emissions display very distinct intense beat-like patterns with great regularity. This is consistent with observations of the enigmatic phenomenon of booming in the field and agrees with the effects of someof the factors, such as grain shape, degree of sorting and coherent motion which are agents considered responsible forthe behavior of booming sand. Measurements of beat and canier periods of the beat patterns recorded in the laboratory
seem to agree with observations and results obtained in the field on AE from booming sands. The present study provides further insight into the peculiar phenomenon of booming sand and may also help to shed more light on otherodd phenomena such as the observation of very intense moonquakes recorded by lunar probes, as suggested by somespace scientists.
H. Kwun, JJ. Hanley and CM. Teller
Performance Comparison of a Noncontaet Magnetostrictive AE Sensor with a Conventional Piezoelectric Sensor on
a Steel Rod
A noncontaet sensor for detecting acoustic emission (AE) signals in ferrous materials is described. The sensor isbased on the inverse magnetostrictive or Villari effect; that is, it senses changes in magnetic induction caused bystress waves associated with AE signals. The frequency response characteristics and sensitivity of the sensor were
measured experimentally using O.5-mm pencil lead break on a 3.6-m-Iong, 6.4-mm-diameter cold-rolled steel rod andwere compared with those of a conventional piezoelectric AE sensor. The advantages and potential applications of
this magnetostrictive AE sensor are discussed.
K. Yoshida, H. Takagi and K. Sakamaki
Evaluation of Stress-Induced Martinsitic Transformation Velocity in Cu-AI-Ni Shape Memory Alloy Single Crystalsby Acoustic Emission Method
In order to clarify the dynamic behavior of the phase transformation, the acoustic emission behavior during suessinduced martensitic transformation in Cu-AI-Ni shape memory alloy single crystals has been determined in the previous report (10th IAES). It was revealed that there was the asymmetry of the acoustic emission activity in the precess of loading and unloading because of the stored strain energy due to the fOJWard and backward phase transformations, and the five types of acoustic emission waveforms were observed. In this study, the martensitic trans-

formation velocity was evaluated using the acoustic emission wavefonn analysis by Takashima and co-workers.
(Continued to page 66)
42

Source Force Waveforms: The Use of a Calibrated
Transducer in Obtaining an Accurate Waveform of a
SourceThomas M. Proctor, Jr. and Franklin R. Breckenridge
Abstract

opposite the ball impact location. The lransducer is connected
to a 100bit, 20 megasample/s recorder via a unity gain buffer
amplifier mounled close to the transducer. For calibration of
the aransducer. the ball-dropping apparatus is replaced by acapillaJy-loading-screw device similar to that used in the NIST
primary transducer calibration system (Breckenridge, 1982). In
addition. a camera, light source. and rotating mirror allow formeasurements of ball reboWld heighlS.

ExperimenlS in which a point source and a receiver areplaced opposite each other on an elastic plate of known Green's
function have been applied to the absolute calibration of both
sources and receivers. With this technique. a transducer was calibrated using a glass-break source. and then this transducer wasused to measure dynamic foree waveforms from elastic spherecollisions with the plate. Calculations. from Hertz's theory. of
predicted force waveforms agree closely with the measured
waveforms.

Optical glass was chosen as the material for the aransfer
plate because. in the experience of the authors. it is the most
homogeneous readily available material. Sound.speed measurements in aluminum, fused quartz, and other materials haveshown large variation with location within the sample(ProcIDr, 1971). Longitudinal and shear sound speeds, density.
and plate thickness were measured in the glass disk and, fromthese measuremenlS. the shear modulus was calculated. and me
Green's function was obtained (Hsu. 1985).

1. Introduction
One of the major goals of acoustic emission work is to beable to determine the force waveforms from acoustic emission
events. but presently we fall far shon of being able to do thisin general. In the typical experiment, emission evenlS taking
place in a solid elastic medium produce voltage waveforms at
the terminals of a uansducer placed on the surface of themedium. In most cases there is no known way to derive thesource waveform from the transducer data. However, it has been
demonstrated thaI. for elastic bodies of cenain particular geometries, force waveforms of source events initialed on the surface of the medium can be recovered from the output voltage
data from a transducer placed at some location on the medium
remote from the source.(Hsu and Hardy, 1978; Hsu and EilZen,
1979; Hsu and EilZen. 1982; Hsu and EilZen. submitled 1988)
In me work described in this paper. we have carried out theforce waveform recovery with somewhat more accuracy than inprevious work, have placed boWlds on the accuracy of the work.
and have demonstrated, using principles of mechanics and elasticity theory. that the results fall within the claimed accuracy
boWlds. The accuracy of the present technique depends on an in
SilU calibration of the receiving transducer. Our results indirectly corroborale the displacement measurements implicit in
the National Instirote of Standards &: Technology (NIST) primary lransducer calibration procedure (Breckenridge. 1982) andagree with other recent dynamic force measurements(Breckenridge et al.• 1990). We have shown that dynamic force
waveforms can be determined in delai1.

The source evenlS used for the experimenl were thecollisions of small ball bearings dropped upon the uppersurface of the plate from a height of 30 mm (bottom of ball ID
plate surface). Balls of ten different sizes. ranging from 1.59
mm (1/16 inch) to 9.53 mm (318 inch) were used. A special
vacuum device was used for holding the balls and releasing
them from the specified height with zero initial velocity.

EVENT
EPICENTERCONFIGURATION

Glass/Plate

D

Capillary Breakor
Steel Boll Impact~ High-Fidelity_ _..,~_--J.

Transducer

2. Experimental Arrangement
The apparatus (Fig. 1) consislS of a horizonlal circularplate of optical glass having diameler 420 mm and thickness
89 mm with a device for dropping balls onto the upper surface
and an NBS conical transducer mounted on the lower surface

Transient
Recorder
10bit
.05us

Received 24 June 1991. The authors life affiliated wilh Auromated
Produclion Technology Division, NaaiOl\llI Instiaule of Standards andTechnology. Gailhersburg. MD 20899.
Journal of Acounic Emission
Volame 10, Number 3/4

ComputerFig. 1 Experimenlal serop.

073()'()()s0I92Il 0043-04S
43

Copyrightel992 Acoustic Emission GroupThe NBS conical transducer (Proctor. 1982, 1986), used as
the receiver, was held upward against the bottom of the plate bya spring which had been adjusted to apply, at the conicaltransducer tip, a contact force equal to the normal gravity load.
A strip of 12 J11l1 thick aluminum foil coupled to the glass plale
formed the ground electrode for the transducer.

epicenter arrangement) makes the experiment possible and was
a deciding factor in its design. The impulse response of the
NBS conical transducer is sufficiently similar to the ideal delta
function that. with negligible modification. it can also be
inverted. The inverses of G and T were found by direct time·
domain deconvolution (Hsu and Eitzen, 1979).

3. Theory of the Experiment

For the purpose of finding T, or calibrating the receiving
transducer. a separate experiment was performed in which a
glass capillary was broken at the source location. and the forceof the glass-break was measured by a calibrated sensor in theloading screw. We assumed the glass-break event produced a
step force. Fe. This assumption can be justified by experimental evidence. but we defer this to a later point in the paper.
Inversion of the step function is easy. (This operation isequivalent to introducing a differentiation of anyone of thetime functions With which Fe-l is convolved.) Inversion of G ispossible, so

The physical experiment can be represented:

F*G*T=V.

(1)

where F is the dynamic force of the source, G is the transfer
function (Green's function) of the plate, T is the impulse
response of the receiving transducer, V is the voltage output of
the receiving transducer, and ..... denotes convolution, and allof the variables are functions of time. If G and T can be foundand inverted with respect to convolution, then

F =V·G-l*11•

T=Ve *G-1*Fc-l.

(2)where Vc is the captured voltage waveform of the calibration.

The possibility of recovering F depends on two conditions:(1) G must be known, either from theory (Pao et al., 1979;
Hsu. 1985) or from experiment. and it must be capable ofbeing inverted with respect to convolution;(2) T must be measurable. and must be capable of being
inverted.

Effects of error in the Green's function used in our calculations were investigated by a computer experiment. An altered
Green's function was constructed using sound speeds which
differed from the measured ones. and this altered Green's function. G1, was used to calculate a transducer response. T 1, which
was found to exhibit spurious pulses independently known tobe absent in the T function of the conical transducer (Fig. 2).
These pulses occur in the T 1 waveform at times corresponding
to the arrival of energy traveling with the shear speed (S waves)and with the longitudinal speed having been reflected fromboth surfaces (PPP waves).

The problem of inverting either G or T is not solvable ingeneral (Carasso and Hsu. 1985). As a practical mauer. however. the invertibility of a function depends on how fast thefunction breaks away from the base line. The fact that it ispossible to invert the theoretical Green's function for the platemedium with source and receiver opposite each other (the,......
Ec............

~.......,
I.IJ

(/)

20
10 -

I\A

0

1\

z

-10 -

a..

-20 -

I.IJ

c:::

I.IJ

en

....J

=:l

c..
~

c:::
w
u

:::>

c
en

z
c:::
I<:

\.....,.

--

\11-

I\.
~

S

0

en

(3)

ppp

-30 -40 -50 -60 -70 -80

o

I

20

40

TIME (jJs)
Fig. 2 Transducer impulse response function from the calculation of equation (3). The curve with the quiet tail was obtained from theGreen's function based on sound speed measurements in the glass plate; the curve with the extra pulses was obtained from a Green's
function based on slightly altered sound speeds.
44

1.5
lV

-

-

U
L..

0

I..L.
~

lV

1-

N
0

E

L..

0

Z

-

-

-

~

lV

> 0.5 0
>
c

-

0

u

-

lV

Cl

0.1 0
-0.1
0

40

20

Time

(}Js)

Fig. 3 Force waveform from a capillary break as obtained by the calculation of equation (2) and normalized by the loading screw
measurement.
Their presence in the T 1 waveform is a consequence ofamval-time enors for these waves in the 0 1 waveform, andclearly indicates that the altered sound speeds are incorrect. The
deconvolution of the 0 and T functions, therefore, serves not
only to determine T, but also to verify the conectness of O.
provided T is known to be free of late·uriving pulses.

Ius. the force waveforms for each size of ball were calculated.
Originally, our calculations used a well known half-sine approximation to the force-vs.-time function. but it became apparent that errors involved in this approximation caused disagreement with our experimental data. We therefore performed
more exact calculations of the force waveforms based on numer·
ical integration of an elliptic integral (Goldsmith. 1960). It isthese more exact calculations that were used as the basis ofcomparison for the measured force waveforms in this paper.

Although G and T were determined separately, it is theirconvolution that figures in the equation that relates F and V.
Physically. O.-r descn"bes the response of the plate-transducer
system to a force. The fmal force-measurement results would
have been no different if we had found (0.-r)·1 from the same
The properties of G
calibration event that was used to fmd
and T that make their inversion possible cUT)' over to theirconvolution, 0"-. From equation (1).

4. Experimental Results,..1.

G.,.=Vc·Fc-l.

As a check on the effectiveness of the computational systems used in this experiment 10 accurately determine the force
waveforms of sources. we used the system to determine waveforms of capillary-break events other than the calibration
events (Fig. 3). Since the assumption that such events produce
step waveforms was made at the outset. it would. of course.
have been disastrous if the resulting waveforms had not represented experimentally realized steps. The results of this test
showed that the numerical convolutions and inversions perform properly and produce a result consistent with the assumptions of the experiment.

(4)

from which it follows thatF =(Vc·I·FJ·V.

(5)

A characterization of the transfer function of the combined
plate-transducer system would have sufficed for the purposes ofdetermining the source force waveforms. In other words. the G
and T functions need not be present in the equation relating
measured voltage waveform. V. and force. F. In the computer
experiment described above, alteration of the 0 function
brought about no significant change in the calculated source
force wavefonn.

By means of the experimental and computational systemdescribed. source waveforms were obtained for one hundredball-impact events: ten each for ten sizes of ball. Force waveforms were calculated for all events. For one ball of each size.
the waveform, normalized in magnitude by division by thesquare of the ball radius and normalized in time by division by
the radius. is displayed in Fig. 4 along with the corresponding
waveform calculated from Henz's thecny. Here. the outlier is the

Elasticity theory has been developed for the force producedby the collision of elastic spheres (Hertz. 1881; Goldsmith.
1960). Using measured values for the weights of the balls. anda hllDdbook value (Metals Handbook. 1978) for Young's modu45

1

C\J

0

..........

-1

E
E

z

-2
I

0

llJ

a::

<t
~

e

Ul
Ul
~
H

-3
-4
-5
-6

0
<t

-7

..........
llJ

-8

LL

-9

a::

u
a::
a

-10
-6

-5 -4 -3 -2 -1

0

1

TIME/RADIUS AFTER PEAK

2

3

4

5

6

(}Js/mm)Fig. 4 Dynamic impact force waveforms for ten sizes of ball. Each waveform represents one event using a particular size of ball. The
waveforms were obtained from the calculation of equation (2) and normalized in magnitude by lIr2, and in time by l/r. The smooth
curve, offset by 112 diVision, is calculated from Hertz's theory.
curve for the 3.18 mm ban. and the smooth curve is the theoretical one, which has been displaced vertically for clarity. The
overlapping of ten lines of data illustrates the consistency ofthe normalized values of dynamic force. For each size of ball
except the smallest, the variation of the magnitude of the force
function from event to event was less than 2911.

Based on the values of density and Young's modulus for the
steel balls and the glass plate, and the controlled drop height of
30 mm, Hertz's theory predicts the following size-independent
physical parameters:

Fp/r2 =9.736 (9.260) N/.,

ftaIr = 5.179 (5.124) JlS/mm.
IIr3 = 49.883 (47.15) N JlS/JDJD',
where Fp is the peak force. lta is the pulse width at half the peak

The Hertz theory applies only to loss-flee elastic media,
and also does Dot take into account radiative losses. Without
the radiation of sound into the medium the experimeDt would be
impossible. It may be expected that the various loss mechanisms would prevent the ball from returning from the collision
with the same energy it had on impingement. To determine theextent of such losses, careful photographic measurements weremade to determine the successive rebound heights of the trajectories. It was found in the course of these experiments that surface pitting and contamination of the balls and the plate wereassociated with increased losses. Certain balls performed morepoorly than others, and microscopic examination revealed thatthe 3.18 mm balls had larger surface imperfections than theothers. After discarding the worst balls of each size and by carefully cleaning the balls and the plate, we were able to obtainrebound heights that were 99.09& of the drop height. A systematic: error of 0.3CA; in the values of F owing to reboundlosses has been ignored in our calculations.

force, I is the impulse (I c change in momentum), r is the ballradius. The experimentally determined values are shown inparentheses. Figure 5(a,b,c) shows the experimental values ofthese parameters as a function of ball size. In general, theexperimental enors do not significanUy depend on ball size,
although the error in ftl is greatest for the smallest ball size.
A worst case error analysis was performed based OD estimated uncertainties of the input parameters (the measured soundspeeds in the glass, the measured densities of the glass andsteel, the handbook value for the Young's modulus of the steel,and the drop height). This analysis indicates that the uncertainty in Fp, ftl, and I were 0.79&, 0.511&, and 0.611&, respectively.
The fact that the experimental values of Fp and I are low inmagnitude by 59f, and 6'Ai, respectively, is attributable maioly
to errors in calibration of our apparatus by the glass capillary46

-

10

N

E
E
........ 9.8z

THEORETICAL VALUE'" 9.735......

.

'0
1Il
0

~

0"

9.6

en
G'I

~

:g
0

0:

9.4

........
1II

..
U

0

L&..
.:i.

9.2

a

III

a..

9

3

1

7
5
Sphere Diameter (mm)9_5.24 .....-+---------------------,
E
E"';;;-

2: 5.2
THEORETICAL VALUE", 5.1789
en
~

:cCI
0:::

~5.15

E

i=

....u
....
CI

l5 5.1uN--5.06 +--........

.........

-"""T"""-"""T"""-"""T"""--r---,--"""T'"-"""T'"~--1

1.0

-;;;E
{

50 1

3.0
5.0
7.0
Sphere Diameter (mm)9.0"",=============::::::;;;::=============.
Theory = 49.88 ;;;"'"

49

:::L.

I

z
......
48

47

46---.----,r---..--....---i

45.5 +----r----,--,....--.....

3

5

Sphere Diameter

7

9

(mm)

Fig.S Measured physical parameters as a function of ball size: a) FplrZ; b) 'tJr; c) IJi3.

break technique. From previous experience, we feel that theuncertainty of the glass capilluy-break calibration scheme isabout 6 to 7 9b, which is consistent with the present results.

W. Goldsmith (1960). Impact, E. Amold Ltd., London, Chapter
4.
H. Hertz (1881). Ge.sammelte Werke, J. Reine Angew. Math 92,
reprinted in 1986 as Miscellaneous Papers, Macmillan, london. p. 156.

A careful examination of many glass capilluy-breakiDg

experiment reveals considerable variation in the details of capillary collapse. At least two different processes can be observed. About half of the glass capillary-breaks exhibit a relatively cleaD single step, but the other half exhibit collapse inmultiple steps, i.e., not all the initial force is relieved in thefirst break. This phenomenon can be seen in the dec:onvolved
force-vs.-time waveforms and also in the waveforms from theload cell of the glass c:apWuy breaking apparatus. By monitoring these waveforms, data from glass capillary-breaks having
multiple steps could be discarded during the calibration pracess.
S.

N.N. Hsu (1985). "Dynamic Green's Functions of an Infmite
Plate - A Computer Program", National Bureau of Standatds,
NBSIR 85-3234.

N.N. Hsu and D.G. Eitzen (1980), "Acoustic Emission Source
Characterization Through Direct Time-Domain Deconvolution". Proc. ARPAJAFML Rev. Qua.ntilative NDE, AFWAL-TR80-4078. pp. 81-84.

N.N. Hsu and D.G. Eitzen (1982), "The Inverse Problem of
Acoustic Emission-Explicit Determination of Acoustic Emission Source Time-Functions". Review of Progress in Quanti.
tative Notukstructive Evaluation, VoL 1. Ed by D.O. Thompson and D.E. Chimenti, Plenum Publishing Corp., New York,
pp. 405-412.

Conc:luslon

It is shown that unknown dynamic forces can be accurately
measured. Agreement between measured force waveforms andthose predicted by Hertz's theory establishes the correctness ofthe method and impliciUy corroborates the results of the NIST
primary transducer c:alibration measurements and recent force
measurement results.

N.N. Hsu and D.G. Eitzen (submitted 1988), "Experimental
Determination of Point Impact Force-Tune Function", Experimental Mechanics.

It is shown that the relation between the measuring system
output and the unknown force is descnoed by the transfer function of the entire system (plate and transducer in this case). andneed not be separated into convolutional factors representing
the individual components. Furthermore, it is shown that incorrectness of the plate Green's function manifests itself as
spurious pulses visible in the derived transducer response waveforms provided that the transducer response is ftee of late-arriving pulses.

N.N. Hsu and S.C. Hardy (1978). "Experiments in Acoustic
Emission Waveform Analysis for Characterization of AE
Sources, Sensors. and Structures", Elastic Waves and NonDestructive Testing ofMaterials, AMD - vol. 29. cd. Y. H. Paa.
ASME, New York.
Y.H. Pao. R.R. Gajewski, and A.N. Ceranoglu (1979). "Acoustic Emission and Transient Waves in and Elastic Plate". J.
AcoUSL Soc. America, 65(1), 96-105.

Acknowledgment

T.M. Proctor (1971). "Sound Speed Measurements in Solids:
Absolute Accuracy of an Improved Transient Pulse Method".
NBS J. of Research, 7SC(I), 33-39.

This work was supported by the National Institute ofStandards and Technology and the Office of Naval Research.

T.M. Proctor (1982). "An Improved Piezoelectric Acoustic
Emission Transducer". J. Acoustical Soc. America, 71(5).

References

1163-1168.

ASM (1978), Metals Handbook, Ninth Edition, vol. 1 Pro·
perties and Selections: Iron and Steel, American Soc. Metals,
Metals Park, OH. p. 680.

T.M. Proctor (1986), "More Recent Improvements on the NBS
Conical Transducer". J. Acoustic Emission, S(4), 134-142.

F.R. Bl'e':kenridge (1982). "Acoustic Emission Transducer
Calibration by Means of the Seismic Surface Pulse". J.
Acoustic Emission. 1(2), 87-94.

F.R. Breckenridge. T.M. Proctor. N.N. Hsu, S.E. Fick and D.G.
Eitzen (1990). "Transient Sources for Acoustic Emission
Work". Progress in AcoJLStic Emission V, eels. K. Yamaguchi etal.• Japanese Soc. for Nondestructive Inspection. Tokyo.
Japan. pp. 20-37.
A.S. Carasso and N.N. Hsu (1985>. "Probe Waveforms and Deconvolution in the Experimental Determination of Elastic
Green's Functions". SlAM J. of Applied Mathematics. 45(3).
369-382.

48

Acoustic Emission Monitoring of a, Fatigue Test of an
F/A-18 Bulkheadc. M. Scala, J. F. McCardle and S. J. Bowles
been placed on monitoring complex-shaped components notreadily inspected by more conventional techniques. Promising
results were obtained in early AE studies on a Macchi aircraft,&om which the feasibility of in-flight AE measurements wasestablished (Scott, 1981; Scala et al., 1989). Following theMacchi study, all significant cracks were located in the main
wing-spar of 'a Mirage jet-fighter during full-scale fatigue testing (Scala and Coyle, 1987; Scala et a1.. 1989). The Mirage
smdy was arguably one of the most difficult AE aircraft applications likely to be encountered, requiring crack identification innumerous bolt holes amid a wide range of spurious AE sources.
Hence, an imponant outcome of the Mirage study was thedevelopment of AE processing which should be useful for crack
detection in a wide range of aircraft applications. The processing involves identifying possible cracking by a comparison
between AE features obtained from a fatigue-eritical region during aircraft loading and predicted features for cracking in theregion. Feature prediction is based on (i) wave propagation
characteristics for cracking in the region obtained from Pentellead calibration in fatigue-critical regions (Scala and Coyle,
1983 and 1987), and (ii) source-related characteristics frombackground studies of AE events (Scala et al., 1989).

Abstract
This paper descn"bes Ihe application of acoustic emission(AE) to identify cracking in several fatigue-erilical regions onme port and starboard sides of an F/A-IS aircraft bulkhead
undergoing fatigue testing. AE data acquisition was carried out
using an array of three sensors on each side of Ihe bulkhead. AE
features stored by each array included relative arrival times ofAE events at Ihe three sensors. event rise time at Ihe rlfSt-hit
sensor. and Ihe load level and the position on the load cycle ofevent occurrence. AE data processing involved a comparison
between the features of those AE events stored during thefatigue testing and predicted fearures for cracking in the com·plex-shaped bulkhead. Feature prediction was based on wave
propagation characteristics obtained by Pentel-lead calibration. and the known load cycle dependence of crack-related AE
events. The AE processing was completed following failure of
the bulkhead. and gave the correct locations of all cracks,
greater than about 1 mm in deplh, present in the bulkhead during the fatigue testing. The study shows that AE associatedwith cracking can be distinguished, even when many extraneous sources are present, and demonstrates Ihat AE is a promising technique for nondestructive evaluation of a complex structure such as the F/A-18 bulkhead.

Considerable background information on source-related
features is available to distinguish between AE sources, particularly in laboratory applications where a wide range of features
has been obtained (Graham and ElsJey, 1983; Hutton, 1985;
Scala and Coyle, 1983; Scala et al., 1989) including fundamental characteristics such as the directivity patterns of different
sources (Bunle and Scruby, 1989 and 1990a). Some of the features applicable under idealized laboratory conditions are also
useful for distinguishing between crack-related and extraneous
sources detected in the far field in complex-shaped aircraftcomponents (BuUle and Scruby, 199Gb; Scala et al., 1989). For
example, electromagnetic interference is typified by a shorter
rise time than other sources detected under such conditions;also, hydraulic noise has a relatively long duration. long risetime and low frequency compared to crack-related AE (Grahamand Elsley, 1983; Hutton, 1985; Scala and Coyle, 1983; Scalaet al., 1989). The position on the load cycle of AE event occurrence is anomer useful feature on which to eliminate AE fromsome extraneous sources (Buule and Scruby, 1990b; Scala etal., 1989). In particular, various extraneous sources, such as
electromagnetic interference and hydraulic noise, can occur on
all partS of the load cycle. Also, AE from fretting of a boh in ahole has been observed on negative load gradients during cycling of bolted plates (Graham and Elsley, 1983) and at a rangeof load-cycle positions for misaligned specimens. AE due tocrack propagation in aircraft aluminum alloys occurs on a load
maximum for constant amplitude load cycling; the source also
occurs on a positive load gradient during application of an
overload (Lindley ~t al" 1978; McBride and Maclachlan. 1984;

1. Introduction
Assuring the airwonhiness of high-performance miliwy
aircraft such as the F/A-18 by nondestructive evaluation (NDE)
presents special challenges. In particular, for an P/A·18 structural component such as a wing-attachment bulkhead, accessfor nondestructive inspection is limited. Also, the large sizeand complex shape of the bulkhead lead to a reduction in thereliability of application of conventional NDE techniques.
However, in the bulkhead the need exists to detect defects
which lie on the detectability limits of conventional techniques under idealized conditions. The alternative to airworthiness based on a damage-tolerant NDE approach is to establish
not only a safe life for the component but also precision manufacturing and maintenance procedures to guard against the presence of "rogue" flaws. However, even then, the high capital
cost of the F/A-18 aircraft makes operation of the aircraft
beyond the bulkhead's safe life an attractive option. Thus,
improved NDE capabilities are required.
DSTO Aeronautical Research Laboratory (ARL) has beendeveloping acoustic emission (AE) as a possible NDE teclmiquefor in-situ monitoring of aircraft components. Emphasis has
Rca:ived IS January 1992. The authors an: affiliated wilh Airframes
and Engines Division, DSTO Aeronautical Research Laboratory, S06
Lorimer SL, fishermens Bend, 31J17 Australia.
Journal of Acoustic Emission
Volume 10, Number 3/4

0730-00S0J92/10049-060
49

Copyrigbtel992 Acoustic Emission GroupScala and Cousland, 1985). Although detailed overload typeswdies on Ihe features of AE due 10 a crack-face rubbing source
are lacking, AE due to rubbing has been observed predominantly on a positive load gradient. usually below the mean loadbut occasionally rising above this level, in several systematicstudies involving constant amplitude loading (Graham andElsley, 1983; Kim and Weerunan, 1984; Lindley et aI., 1978;
Mori and Obara. 1988). Hence, the occurrence of AE events
exclusively on a positive load gradient and load maximum isconsislent with the characteristics of a crack-related source,i.e., crack propagation or crack·face rubbing, but may not be
consistent with an extraneous source (Scala et aI., 1989;
Weatherlyet aI., 1984).

In this paper, the feasibility of using AE to monilOr possible cracking in several fatigue critical regions during a fatigue test of the aluminum alloy bulkhead of an F/A-18 aircraft
is investigated. using some of the procedures developed in theMirage AE study described above. Section 2 of the paper gives
brief details on the bulkhead fatigue test and outlines the procedures for Pentel-lead break calibration and AE data acquisition
during fatigue testing; a description of the AE data processing
is also given. Section 3 compares features of the AE obtained
during fatigue testing wilh predicted features in order 10 determine the location and source of any AE-based cracking indications in the bulkhead. Also. the AE results are compared with&actographic results obtained &om the bulkhead. Section 4
discusses Ihe significance of the AE results. particularly interms of the reliability of AE for bulkhead ~itoring, and recommends some improvements in AE data acquisition procedures for future F/A-18 bulkhead tests. Fmally. Sec. 5 summarizes the major conclusions of the present study.

up

i
t---i

10mm

'

...

..

....
(a)

1

centerline
regionI

wing-attachment
holes.....-_(ijt;~~Iedgeof mould line
flangeIII

edge
of mould line
flange

I

I

1

1-

longeron 1- - - -~1
fssrenerholesI
test·section

I

1

I

I

longeron recess

1

4-' I

---;---r---;-,lor:>'
0" .'.\:.1 :,:~,,: ::::.. ':":
I

....

~

forward

• '.
.,

'1
1
~

·':1
~

.

..,~: r ". .' .. '

I

:
-'--

I ' .. II• '..
: •. : I:'1 \ . ; ....
I ~ :'.
I '. .

I
'1

(b)
(e)

Fig. I (a) F/A-18 aircraft showing FS 488 wing-attachment bulkhead. (b) View of bulkhead wilh port fatigue test-section indicated.
(c) Bulkhead with 6-inch (l5.2-em) mold-line flange. showing as unshaded the approximate extent of the section for which AE
monitoring of crack propagation was undertaken. Fatigue-critical regions indicated are: (i) edges of mold-line flanges (hatched); (ii)longeron recess; (iii) longeron fastener holes; (iv) outer face of bulkhead below lower wing allachment towards the centerline(denoted centerline region).

Following array attachment, the next step was to set up
the DART for event detection at each array sensor for a threshold level just above the DART electrical noise level with DO
external input applied. Then, the DART was configured 10 aDowstorage of the following features of AE events detected by eacharray: (i) record number, (ii) array hit, (ill) the relative AE event
arrival-times at the three sensors in an array, (iv) event risetime at the fllSt·hit sensor. (v) event duration at the farst-hit
sensor. (vi) event amplitude at the farst-hit sensor. (vii) event
counts at the first-hit sensor, (viii) load level of event occurrence during fatigue leSting. and (ix) oc:currence either on a p0sitive load gradientIJoad maximum or negative load gradient/minimum.

2. Experimental Details and Procedure

2.1 AE Data Acquisilion
As part of a feasibility study of suitable NDE techniques lO

apply to the F/A·18 fuselage swion (FS) 488 wing-auaehment
bulkhead (Fig. la), AE monitoring of a bulkhead undergoing
fatigue testing at ARL was investigated. The ARL fatigue test
fonned part of an inremational program to establish a safe life
for the bulkhead (Anderson and Revell. 1990). The fatigue leSting caused the bulkhead (made from thick plate aluminum alloy
70S0-T7451) to be subjected to a repealed loading program.
Each loading program comprised a sequence of loads, variablein both amplitude and frequency. and simulated 300 flight hoursof an F/A-18 aircraft. The loading was designed to test sections
which were mirror images on the port and starboard sides of theaircraft (FiS. Ib). Each leSt section included several fatiguecritical regions which had been established prior to the commencement of the teSL The regions comprised the longeron
recess. the longeroD fastener holes and the curved parts of theforward and aft edges of the mold-line flanges (Figs. Ib and c).
Each port and sWboard designated test-section also included
other regions higher up towards the wing-attachment area
which U.S. tests identified as fatigue-critic:al after the ARL
fatigue testing (and AE monitoring) was well-advanced. These
additional regions comprised the centerline region below thelower wing-attachment lugs on each outer face of the bulkhead
and the edges of the mold-line flanges right up to the level ofthe centerline region (Fig. lc).

The DART system was used to acquire data in calibration
studies. using the fracture of 2H 03 mm diameter Pentel lead lO
obtain wave propagation features for crack-related AE from thebulkhead. The Pentel lead. which gives a high-amplitude.
shon·fise-time source. has been successfully used to predict a
limiting range of wave propagation features in various large,
complex-shaped aircraft components (McBride et al., 1991;
Scala et al., 1989). In such components. these wave-propagation features are usually the far-field characteristics. complicated by various wave reflections and mode conversions. In
keeping with the simplified approach adopted in the present
study. emphasis was placed on fewer Pentel·lead features thanused previously (Scala et al.• 1989). viz.. the relative event
arrival-times at the three sensors in an array and also the maximum possible rise time at the fllSt-hit sensor for AE eventsfrom a given source location. Data were obtained by breaking
Pentel leads at 10 mm intervals in the critical regions. For eachsimulated source location, data corresponding 10 five separate
Pentel-lead fracture events were recorded in order to establish alimiting range of features for the location and to ensure reproducibility of results. These calibration measurements also
allowed the likely limitations in the AE location accuracy 10 beassessed for the fatigue-critical regions more distant from thearrays. The Pentel-lead source was also applied adjacent to eacharray sensor to verify that each of the six sensors was operating at the same sensitivity.

The objective in applying AE to the bulkhead fatigue test
was to monitor known fatigue-critical regions within the des·ignated test·sections on the port and sWboard sides of thebulkhead. The bulkhead test was simpler than the Mirage-spar
AE application described in Sec. 1. involving fewer possible
spurious AE sources. Therefore, a simpler AE data acquisition
system (ahan the twenty-five sensor. waveform-based Mirage
system) was used for the bulJchead test, given the need for more
efficient storage and processing of data than in the Mirage leSt
(Scala et al., 1989). The acquisition system used was a sixchannel Dunegan DART system linked to an mM PC-AT.
Small-diameter, point-contact sensors (Valpey.Fisher pinducers) were used with the system for AE detection, as space forsensor attachment was extremely limited due to the presence ofnumerous strain gauges associated with the safe-life study ofthe bulkhead. The AE sensors were also selected because of
their wideband characteristics and their consequent suitability
for feature extraction studies (Scala et al•• 1989). The DART
system was limited to the use of up to six AE sensors so an array of three sensors was assigned to each of the port and swboard sides of the bulkhead. Each 20 array had to perfonn dual
roles in rejecting extraneous sources in a 3D structure and locating ctaek-related sources in fatigue-critical regions in the bulkhead. As location accuracy is. in principle. better for source
locations close to an array, the sensor arrays were set up in
close proximity to the regions identified as fatigue-critical at
the start of the ARL fatigue leSting. Therefore, the arrays weresome distance from the additional critical regions identified
afier resting had c:ommented (see above). Due to the space limitations. the sensor array placements were similar but not identical on the pon and starboard sides of the bulkhead.

To assist in the elimination of sources extraneous to thefatigue-critical sections, some additional calibration measurements were undertaken at locations outside the fatigue test seetions (Fig. Ie), especially at likely locations for extraneous
sources such as at the wing attachment holes. From this additional set of calibration data, a value for extraneous sources ofthe minimum relative arrival-time between fJISt-hit and last-hit
sensors (~T2) was obtained (this value being greater than thatfor events within the test-sections). During data acquisition,
events having ~T2 greater than or equal to this minimum value
were flagged by the DART as extraneous; detailed features forsuch events were not calculated to maximize the data acquisi.
tion rate.
During the entire bulkhead fatigue testing, AE data acquisition was carried out with only minor loss of data due to occasional equipment failures. Small changes in AE sensor placements were necessary at 7500 simulated flight hours, following
remachining of the edges of the bulkhead mold-line flanges
from a standard F143 configuration to a 6-inch (15.2-cm) radius51

n at a specific location within a fatigue-critical region wereconfiguration (Anderson and Revell, 1990). Other minorchanges in AE sensor placements were also necessary on a regular basis throughout the test due to external factors associatedwith the safe-life study, e.g., due to repositioning of sn-ain
gauges and temperature sensors. Recalibration was undertaken
regularly throughout the test to allow for these minor changes
in the sensor placement and test re-configuration. The recalibration was also necessary to ensure long-term reproducibilityof sensor characteristics and detection levels in the AE acquisition system and to verify the correct operation of the 4T2
arrival-time criterion.

determined as a function of loading history for comparison
with fractographic data. In particular, event rate, position on
the load cycle of event occurrence and event amplitude were all
obtained as a function of load program number.

100000(0)

10000

22 AE DOlO Processing

1000

The data processing used for this study was similar to, butless sophisticated than, that described in detail for AE monitoring of the Mirage aircraft during full-scale fatigue testing (Scala
and Coyle, 1987). The processing involved comparing measured and predicted features of crack-related AE dming the bulkhead testing, with fewer features being examined than in theMirage test. Feature prediction for crack-related AE in the aluminum alloy bullchead was based on a combination of (i) thewave propagation data obtained from the Pentel-lead calibration (see Sec. 2.1), and (ii) background information which inthis application was limited to the load cycle dependence of
crack related AE sources (see See. 1). Our previous work involved the use of background fearutes from AE from crack propagation exclusively (Scala and Coyle, 1987; Scala et al.,1989). However, in the present study, the probability of detecting any cracking in the bulkhead was maximized by combining
the load dependencies for both AE from fatigue-crack propagation and AE from crack-face rubbing in the feature prediction.
An additional reason for including AE from crack-face rubbing
was that the likely AE activity from fatigue propagation was
unknown at the start of the test. due to a lack of either microSb'Ucrutal information or laboratory AE studies on the bulkheadmaterial.

...c'"
~

100

~

10

38 39 40 41(b)

10000010000

The data processing was carried out in four successive
stages using a standard data-base program (Fox-base) operating
on an mM PC-AT:
I. AE events located within fatigue-critical regions
AE events which occurred within the fatigue-criticalregions were located on the basis of a comparison between predicted and measured event relative arrival-times and rise-times.
In particular, firstly a stored AE event was assigned a location
if the event relative arrival-times matched Pentel·lead criteria
for any of the fatigue-critical locations. Then, rise-time criteria
were applied to a located event: an event was accepted as correctly located if its rise time was in the range greater than I f.LS
(i.e., to reject AE events due to eleettical and eleen-omagnetic
interference) up to the value obtained in the Pentel-lead calibration for the assigned location.
n Crack-related AE validated
Next, load-cycle dependence criteria were applied to Validate as crack-related AE events located within a specific fatiguecritical region: Those AE events with characteristics meetingstage I were accepted as crack-related, provided that the characteristics for a specific location were observable only on positive load gradient or load maximum.
Crack-related AE compared with loading history
Finally, detailed features of those AE events meeting stage

1000

1...'"

100~

10

38 39 40 41

Fig. 2 The dependence on loading program number (fromprogram 38 up to failure) of the unprocessed AE event rate perhalf load program for AE acquired on (a) port and (b) starboard
sides of the bulkhead. (Note that, due to equipment malfunction, AE data were not stored for those half programs for which
no events are plotted.)m.

52

The AE processing was undertaken at weekly intervals.
corresponding to data from about one load program for lhe fust
7500 simulated flight hours ranging up to five loading programs per week following the radius remachining. This weekly
processing schedule meant that AE data from the fll'St fortynine load programs were processed prior to bulkhead failure in
the 54th program of testing on the port side from the forwardmold-line Dange: AE events from the remaining programs were
processed after failure.
3. Results

3.1 Limilations in SOlUce Locationfrom Calibration Data
Pentel-lead calibration measurements. undertaken following the real-time implementation of the ~T2 arrival-time criterion to flag extraneous sources (see Sec. 2.1). demonstrated thevalidity of the system for AE source location close to the AE
sensor arrays. These measurements also showed the limitationsin AE location accuracy for simulated source locations moredistant from the arrays. e.g.• some ambiguities in source location were found in the vicinity of the centerline region shownin Fig Ie. Furthermore. the Pentel-lead calibration measurements demonstrated that isolated AE events would be flagged as
extraneous if they occurred above the centerline region or
below the longeron recess level shown in Fig. Ie.

Table 1 On the basis of arrival-time criteria for the port andstarboard fatigue-critical regions in the bulkhead. the percentage of extraneous AE events is given for each half-program(from 38.5 onwards) for which data were stored.

WAD PROGRAMEXTRANEOUS SOURCES(% of total events)

38.5-39.0
39.0-39.5
39.5-40.0
40.5-41.0
41.0-41.5
44.0-44.5
44.5-45.0
46.0-46.5
46.5-47.0
47.0-47.5
47.5-48.0
49.0·49.5
49.5·50.0
51.5-52.0
52.0-52.5
52.5-53.0
53.0-53.5
53.5-54.0
54.0-failure

100.0
100.0
99.4
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
99.8
99.8
96.2
97.7
99.7
99.9
99.0
98.6

load gradient. Shortly after these AE indications were obtainedin program 39. the temperature sensor disbonded.

32 AE during FatiglU Testing
3.2.1 Overview of AE data
As the fatigue test progressed. no systematic variation wasobserved between load-program number and the unprocessed AE
event rate for either the port or starboard sensor array (Fig. 2aand b). For the port array, the event rates obtained in program
53 and the final program 54 were somewhat higher than had
been observed previously from this array during testing. andsome data were lost due to the occurrence of overflows.
However, lhe port array event rate actually decreased from thestart of program number 53 up to failure (Fig. 2a). The highest
event rate for the fatigue test was obtained on the starboard sideof the bulkhead in the fmalload program (Fig. 2b).
Application of arrival-time criteria in processing stage I
resulted in the rejection of most of the data throughout the test
as due to extraneous sources outside the test-sections (Table 1
and Fig. 2). This extraneous source activity varied from program to program. presumably associated with the changes
which occurred in the loading train. For example. a significant
increase in extraneous events early in the fatigue testing(before the remachining of the bulkhead mold-line radius) corresponded to problems with a loading strap below the longeron
region. Also. the likelihood of fretting of the pins at the wing
auachment points varied as these pins were removed andregreased each half load-program.
The arrival-time processing showed that. up to program
45. the only AE events which could be assigned a location
within the test-section corresponded to the location of a temperature sensor on the starboard section. These events passed
criterion I. having the correct rise time for the temperature sensor location. but failed criterion
occurring on a negativen.

From program 46 till bulkhead failure in program 54. asmall number of events on port and starboard met stage I inprocessing; some of these events also passed stage IT (see Secs.
3.2.2 and 3.2.3).
3.2.2 Processed port AE data from program 46
Application of arrival-time criteria resulted in 528 events
being assigned locations within the port test-section on theport flange. 14 of these events were distributed randomly
through the section and failed stage 1 in processing. having arise time of 1 J.lS (these events occurred on both positive andnegative load gradient below 509'0 of the peak applied load).
The other 514 events were assigned the location. denoted AE
cracking indication 1. shown in Fig. 3: these events also met
the range of rise-time criteria for the location and therefore
passed stage I in processing.
While the 514 events from indication 1 met the Pentellead
criteria on the forward edge of the mold-line flange at the level
shown in Fig. 3. 25% of these events also met stage I acceptance criteria for locations at the same level but 10 mm, 20 mm
and 30 mm across on the outer face towards the aft flange. Thispartial overlap in crack location resulted from an overlap in thePentel-lead criteria for sources distant from the arrays (see Sec.
3.1).
The AE events corresponding to indication 1 occurred
fairly late in the test - this indication was first detected in program 46. further activity was observed in program 52. and thenthe activity increased rapidly up to failure (fable 2 and Fig. 4).

1000

lllO

----1-----

up

10

I

L

I

I

------+------

forwardIt---4

I i i ' i i i L i s i i i ' i i i ' i ' I i i 0 i E i i i ' i 'i' ,J8 J9 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55

10mm

Program Number
1

Fig. 4 The dependence on loading program number (fromprogram 38 up to failure) of the AE event rate (per half load
program) corresponding to the pon indication 1.
100

•

80

...

.a

•

-1,.-

.:
----i----r---;I

I

60

'0

't'40

1

I

•

I

20

I

a
45

47

4&

51

53

55

Program No.

Fig. S The position of occurrence on a loading cycle (as apercent of the peak load applied in the fatigue test) of the AE
events corresponding to the pon indication 1 as a function ofloading program number from program 45 up to failure.

•

Table 2 For events passing stage I in AE processing. thenumber of AE events detected from each cracking indication are
given. together with the stage in fatigue testing at which each
AE cracking indication was fU'St detected.

AEsensors
AEcracking
indication

AECRACK

Fig. 3 2D skelCh of pon side of the bulkhead on which failure
occurred: The placement of AE sensors at bulkhead failure andthe approximate location of AE cracking indication 1 areshown.

INDICATIONThe AE events corresponding to indication 1 all occurred
on a positive load gradient and therefore also passed stage n inprocessing. From stage In. these events occurred over a small
range of relatively low loads through several loading programs(Fig. 5). and exhibited a narrow band of amplitudes separated
from the minimum detection level (Fig. 6).

NO.
EVENTSAE FIRSTDErECI'ED(pROGRAM No.• Flight)

514

46

1

2

238

51

124

3

1236

49

1

4

3

S3

124

teria for stage I in processing gave the AE cracking indications
denoted 2, 3 and 4 (fable 2). The AE indication 2 comprised
238 events with source locations in the centerline fatigue-critical region in the approximate range 10 to 30 mm across the

3.2.3 Processed starboard AE data from program 46
Starboard AE events meeting arrival-time and rise-time cri54

100

eo

..,

CD

.E

..,

----(----

up

60

Ql

I

L

a

:a.e
<

I

------t-----I

forward

20

o

I
4b

t--t
51

53

I

2

e !--

10mm

55

Prognun No.

I
I •
I

Fig. 6 The amplitude of AE events corresponding to the pon
indication 1 as a function of loading program number fromprogram 45 up to failure. (Also shown are (i) the minimum
amplitude detectable by the data acquisition system and (ii) the
amplitude of Pentel-lead fracture events on the outer face of thebulkhead about 10 mm from each sensor.)

1

I

r
I

I

I

I

I

I

I

I

-~-$I
: -$----i----r----:-•

outer face from edge of the forward side of the starboard flange
towards the aft flange (Fig. 7). The AE indication 3 comprised
1236 events at the edge of the forward flange with locations
ranging from approximately 110 to 120 mm above the star·board longeron recess fillet (Fig. 7). The 3 AE events for AE
indication 4 could have arisen from either of the two locations(48 and 4b) given in Fig. 7 (due to limitations in source location accuracy for sources distant from the array; see Sec. 3.1).
Other events meeting arrival-time criteria for the starboard section but failing stage I based on rise-time criteria were: (i) 30
events which were located by arrival times at the forward edgeof the mold-line flange at the comer of the radius (these events
all occurred on a negative load gradient). (ii) 5 events locatedby arrival times at the forward edge of the mold-line flange 80
mm above the starboard recess fiDet (these events oc:c:urred on
both positive and negative load gradients). and (iii) 10 events
distributed randomly through the section with a rise time of 1
~ (these events oc:c:urred on both positive and negative load
gradient below 50% of the peak applied load).

•All events from cracking indications 2. 3 and 4 also
passed stage n in processing. However. the events corresponding to (i). (ii) and (iii) above which failed stage I in processing
also failed stage n.

AEsensors
AEcracking
indication

Fig. 7 20 sketch of starboard side of the bulkhead showing AE
sensor placements at bulkhead failure and approximate
locations of starboard AE cracking indications numbered 2. 3
and 4 - for the small number of AE events corresponding to thecrack 4 indication. there are two possible locations (4a and 4b)
due to limitations in the data acquisition system discussed inSec. 2.1.

k for the pon side of the buUchead, all starboard AE cracking indications were obtained relatively late in the fatigue test
(Table 2 and Fig. 8). Indication 3 gave the greatest AE withconsiderable activity for several load programs; then. theactivity gradually decreased as the bulkhead neared failure. The
AE behavior for AE cracking indication 2 was similar to indica·tion 3 but with fewer cumulative AE events detected. The small
AE activity from indication 4 only commenced in the final
loading program.

63 dB). Cracking indication 4 only comprised a few events andtherefore was not amenable to stage ill processing.

Application of stage m in processing showed that the AE
events comprising indications 2 and 3 exhibited similar load
and amplitude characteristics to crack 1 (Figs. 5 and 6) but forhigher ranges of load (45-55 % of peak load) and amplitude (57.

3.3 Comparison belWeen AE Daza and Fraclogrophy
The AE cracking indication 1 on the edge of the port forward mold·line flange (Fig. 3) corresponded to the initiation
55

(0)

1000

----1-----

up

t

100

....

....l

10

I
I

------f------

forward

~

6.1mm

I---i

10mm

O.8mm
2xO.7mm_L. __

1000

(b)

I

100

-ii~

....

~l

I

I

I

I

I

I

----:-----r----:--

10~

rT.--rt • T-Y-l T --I .-1-.... - . I I I • I i I I I ' 1---' I I I • I

1I38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55
Program Number

Fig. 9 2D sketch of port side of the bulkhead on which failure
occurred from a crack originating above the cured section of theforward pon mold-line flange: Depths of those cracks greaterthan 0.5 mm measured by fractography after bulkhead failure
along the dotted line are shown.

1000
(c)

site of the crack causing the bulkhead's failure (Fig. 9) (Barter
et al.. 1991). Fractographic examination showed that thisfatigue crack was (i) about 0.5 mm deep at program 46 when the
AE processing flJ'St gave indication 1. (ii) 2 mm deep at program 52 when further AE was obtained corresponding to indication I, (iii) 3 mm deep at program 53 when the rapid increase inindication 1 AE commenced, and (iv) 6.1 mm in depth justbefore bulkhead failure.

100

-ii

~

10

~

, • , • II

I • I • I • I • I • I • I • I • IiII • I .. -,--. I • I

IOn the starboard side of the bulkhead. fractographic examination showed that the AE cracking indication 2 (Fig. 7) corresponded ro a group of centerline cracks on the starboard outerface of the bulkhead (Fig. 10). The maximum depth of thesecracks was 0.8 mm when AE indication 2 was first obtained andranged up ro a depth of 3 mm at bulkhead failure. AE indication
3 corresponded to the deepest crack in the starboard section the crack was 0.6 mm deep when fllst detected by AE and 4.4mm deep at bulkhead failure. For the few AE evenlS from cracking indication 4 (Fig. 7). a small crack of depth 0.6 mm and alarge group of cracks ro a maximum depth of 0.8 mm were preI

38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55
Program Number

Fig. 8 The dependence on loading program number (fromprogram 38 up to failure) of the starboard AE event rates (perhalf load program) corresponding to (a) indication 2. (b)indication 3 and (c) indication 4. (Note that data for the several
half-programs were not stored due to equipment malfunction see Table 1 and Fig. 2).
5&

This increase was associated with (i) increased out-of-planecrack deviation, (ii) evidence of multi-site crack reinitiation
and branching. and (iii) isolated pockets of overload-type tearing behavior. All of these conlributed to an increase in fracturesmface roughness from program 4S onwards. A further, more
substantial, increase in roughness occmred from program 51 up
to failure. corresponding to the consolidation of the overload
failure areas into discrete tear bands. Thus. from a crack depthof about 1 mm the crack advanced in large, rapid jumps involving significant ductile tearing. As with the pon crack. the starboard cracks greater than about 1 mm in depth exhibited substantial ductile tearing, together with increasing surface roughness for the remainder of the tesLI----1----

up

I

L

I

------+-----group to
max. depthIforwardgroup toIof 3 mm I~~:~~~i Ju

~

10mm

".---... -

I
I
I
I
I

4.4mm

I

O.6mm

I

Examination of the microstructure of the bulkhead aluminum alloy showed the presence of inclusions which varied up
to 100 ~ in length and 5 J1m in thickness. Inspection of crack
fracture surfaces after failme of the bulkhead showed the occurrence of some fractured inclusions only at crack depths greaterthan about 1 Mm.

4.

Discussion

I

I

_-J_

I

4: 4V
----i---r----;I

I

I
I

I

I

I

I

Fig. 10 20 sketch of starboard side of the bulkhead showing
depths of those cracks greater than 0.5 mm measured byfraclOgraphy after bulkhead failure - for each group of center·line cracks. the arrow corresponds to the coordinate in theforward direction of maximum crack depth.
sent at the two possible AE source locations 4a and 4b. respectively (Fig. 10).
Extensive visual, metallographic and nondestructive inspection indicated that no other cracks gre8leT than 1 mm indepth were present in the pon or starboard sections at buJlchead
failure (Barter et al.. 1991). However. numerous tightly-closed
cracks smaller than 1 mm in depth were present, including several gre8leT than 0.5 mm in depth which were broken open forfraclOgraphic examination, viz.. (i) two 0.7 nun deep cracks on
the pon aft edge of the failme plane. and (ii) an O. 8 mm deep
crack on the pan aft edge above the fracture plane (Fig. 9).
Examination of the fracture surfaces of the 6.1 mm deeppon crack (Baner et al., 1991) showed that a rapid increase incrack propagation rate had occurred from program 4S onwards.

The AE monitoring of the F/A·18 bulkhead was successful
in that all major cracks. including the pon crack which caused
failure of the bulkhead, were identified in the AE data processing following completion of the fatigue tesL The crack depth atwhich AE was fllSt detectable was about 1 nun for all cracks
occurring in the bulkhead. although substantial emission fromthe pon crack which caused failure was only obtained after thecrack reached a depth of 2 nun. As in previous AE monitoringof fatigue tests of aircraft components (Scala and Coyle. 1987;
Scala et al.. 1989). the AE activity from extraneous sources inthe bulkhead test was extremely high. Nevertheless. this study
provides further evidence that AE associated with cracking canbe distinguished, even when a complex structure is monitored
and many extraneous sources are present.

In the bulkhead test, the likelihood of detecting cracks by
AE was maximized by identifying crack·related AE on !he basisof the combined features of AE associated with crack propaga·tion and crack·face rubbing. Whilst an understanding of whichof these possible sources gave the crack-related AE is notessential here, consideration of the results of stage m in AE
processing and the morphology of the fracture surface should
give some insight into the AE generation mechanism. In particular, during steady fatigue crack propagation in aluminumalloys. the detectability of AE has been linked indirec:tly 10 thepresence of fractured inclusions (Buttle and Scruby. 1990a andb; McBride and Harvey, 1987; Scala el al.• 1989) - fractured
inclusions were observed on fracture smfaces from the bulIcheadfrom a crack depth of about 1 nun when AE cracking indications
were ftrSt obtained. Also. from this crack depth there was aconsolidation of isolated overload failure areas inlO ductile tearbands • this cracking process is known to be acoustically"noisy". at least in some steels (Clark. 19n). Various fracturesurface features which have been correlated Previously with
substantial AE from crack-face rubbing were also observed froma crack depth of about 1 mm, viz.. significanl surface roughness and multiple crack branching (Manin. 1984; Morl andObata. 1988). Hence. the fractographic results provide inconclusive evidence as to the crack-related AE source delec:led inthe bulkhead tesL More insight into this AE source can begained by a comparison between the detailed features of the AEfrom the bulkhead obtained in stage ill in processing and the
expected features for AE due to crack propagation (whether
related to steady propagation or ductile tearing) and due tocrack-face rubbing. In particular, at least some of the AE events
due to crack propagation should occur on high loads towards
the maxima in load cycling, based on our previous laboratory
studies during the application of a single overload amidst otherwise constant amplitude load cycling (Scala and Cousland,1985) and also aircraft studies of overload-related cracking(McBride and Maclachlan. 1984; Scala and Coyle, 1987). Also,
AE events due to crack propagation should have a wide range of
amplitudes (McBride and Maclachlan. 1984). Therefore, thefact that, for all the cracks detected in the bulkhead, the crackrelated AE occurred over a narrow range of amplitudes and onlyat relatively low loads is inconsistent with crack propagation
as the source. However, such observations are consistent with
AE obtained from crack-face rubbing for relatively long cracks,
both during constant amplitude testing (Graham and Elsley,
1983; Kim and Weertman. 1984; Lindley et al., 1978; Morl andObata., 1988) and during our series of single overload studies.
Clearly, confmnation that crack-face rubbing was the source
detected in the bulkhead would require further studies to establish whether the features for rubbing AE under simple loading
conditions are also observed under simulated flight loading.
Whether such studies would be cost-effective could be questioned, provided that the source detected is a reliable crack indicator (see below).

toring. The use of a sensor with a small contact area was necessary in the bulkhead test due to the existence of several constraints, viz. space limitations, the need for meaningful feature
extraction to reject numerous extraneous events, and also therequirement for accurate time-of-flight event location (see Sec.
2.1). However, the type of sensor necessary for the bulkhead
test was much less sensitive than that used in our previous laboratory studies on aluminum alloys in which sensor selection
could be made without the constraints discussed above. Thus.
our hypothesis that AE due to fatigue crack propagation was
not detected in the present test but had been in some previous
studies is consistent with some reduction in detection capability due a combination of factors (v) and (vi) above, and could
also be affected by factors (i)-(iv). Clearly, the effects of factors (v) and (vi) could be modified in future bulkhead tests toimprove the likelihood of detecting AE associated with crack
propagation as well as crack-face rubbing.

In the present F/A-18 bulkhead test, the AE source detected
was very reliable, allowing identification of all cracks at a similar depth. However, the hypothesis that the detected AE wasassociated with crack-face rubbing rather than crack propagation needs consideration in terms of the possible reliability of
AE for future bulkhead monitoring. In particular, rubbing is by
its nature an erratic process. Hence, AE from rubbing has usually been characterized by erratic features such as occurrence formany cycles at one position on the positive gradient of theload cycle before ceasing completely or starting up at a different position on the positive gradient (Graham and Elsley.
1983; Kim and Weertman. 1984; Lindley et al., 1978; Morl andObata. 1988). The erratic behavior of crack-face rubbing could
account for the decrease in AE activity from cracks 2 and 3 justbefore bulkhead failure (Fig. 8), although this decrease could
also have been due to loss of data in the AE data overflows
towards the end of the test. While reliability of crack detection
could be questioned for a rubbing AE source, some differencesin the reliability of a possible crack propagation source would
also be expected in the anisotropic bulkhead material for crack
propagation along different directions. Hence, all of the above
suggest that AE monitoring of additional bulkhead tests isrequired to establish the reliability of the AE technique for F/A18 monitoring. Additional monitoring is also required of apainted bulkhead, as bulkheads fitted to operational aircraft are
painted, unlike the free-standing bulkhead in the ARL test, andpaint disbanding is one of the many spurious AE sources which
are likely to cause complications.

The hypothesis. that AE from crack propagation (whether
associated with steady fatigue crack growth or ductile tearing)
was not detected in the present test, requires some consideration, as this source has been detected in other aircraft aluminumalloys. both in laboratory studies (Buttle and Scruby, 1990a;
McBride and Harvey, 1987; Scala and Cousland, 1985; Scala etal., 1989) and also during monitoring of other aircraft components (McBride et al., 1991; Scala and Coyle, 1987). Various
factors are likely to affect the detectability of AE due to crack
propagation in an aircraft aluminum alloy in a given test, particularly (i) the inclusion size dislribution, (ii) the malrix condition, (ill) loading conditions, (iv) crack length and propagation rate, including the extent of any overload, (v) the source·
sensor separation. and (vi) the sensitivity of the AE sensor.
The effects of factors (i)-(iv) are difficult to assess. In particular, while long, Ihin. fractured inclusions were found on fracturesurfaces from the bulkhead (see Sec. 3.3), the inclusion size
distribution has yet to be determined for representative bulkhead material. Also, specific loading conditions and alloy heat
treatment affect greatly the detectability of AE due to crack
propagation (McBride and Harvey, 1987; Scala and Causland
1985; Scala et al.• 1989). Hence. fundamental studies on overload-related AE in representative bulkhead material would beneeded to assess adequately the effects of faclOrs (i)-(iv). A con·sideration of factor (v) shows that, in retrospect, the placementof sensors in the bulkhead test was far from ideal due to the lackof knowledge of all fatigue-critical regions at the start of testing (see Sec. 2.1). Hence, sensitivity was reduced compared toour previous testing because the sensors were not only up to afactor of 25 further away from the cracks in the bulkhead thanfrom cracks monitored in our laboratory studies but also moredistant than from cracks detected in our previous aircraft moni·

Another reliability factor which should be considered infuture bulkhead tests is the establishment of confidence limits
on the number of AE events required for reliable crack prediction. In the present test, each bulkhead crack was detectablefrom a crack depth of about 1 mm, with a large number of AE
events being obtained for AE cracking indications 2 and 3 fromthis depth. However, while AE cracking indication 1 was firstobtained at a crack depth of about 1 Mm. substantial activity
was only obtained when the crack reached a depth of 2 mm (Fig.
4 and Sec. 3.3). Hence, prediction of crack 1 at a relatively
small depth would have required confidence in the validity of
only a small number of AE events.
Several modifications to data acquisition are required if
optimal advantage is to be gained from AE monitoring of fur
58ther bulkhead tests. Improved discrimination between crackrelaled AE and extraneous sources could be achieved by the useof (i) additional sensors to extend the present 20 analysis to amore realistic 3D analysis, and (ii) guard sensors which areinvaluable for eliminating extraneous sources. Moreover, increased AE sensitivity to cracking and also improved location
accuracy could be gained by attaching sensors close to all thefatigue-critical regions (rather than near the limited number ofregions identified at the start of the ARL fatigue test). Then.
problems of overlapping criteria, such as obtained for cracks 1
and 4, could be signifICantly reduced. While location accuracy
could be improved for a free-standing bulkhead test. consideration also needs to be given 10 likely limitations in space avail.
ability for array auaelunent in an aircraft application. IcIealIy,
fume bulkhead fatigue tests would result in evaluation of both
optimal ground and in-flight array c:onfigurations.

complex structure is monitored. The sWdy also demonstrates
that AE is worth pursuing further as a possible technique for
future nondestructive evaluation of the F/A-18 bulkhead.

Re-evaluation of possible AE sensor types would also beof value for future bullchead monitoring. In particular, while Ihetype of sensor used in the present study was ideal for accurate
source location and pattern recognition analysis, both the sensor's length and susceptibility to disbonding if knocked sufficiently hard would make the sensor unsuitable for flight appli.
cations. Also increased sensor sensitivity could lead to improvements in the deteetability of cracking. Currently available alternatives include the poinl-contact sensor (Buttle andScruby, 1989, 1990a, b) which would have similar limitations
to the type of sensor used in the present work and more resonant sensors whose use would lead to a degraded capability foreliminating extraneous sources. Hence, some sensor development research could facilitate possible in-flighl applications.

Numerous ARL staff have made considerable conlIibutions
during the two years of AE monitoring of the F/A-18 bulkhead
test. B.C. Bishop carried out AE sensor attachment throughout
the test, and also provided useful advice on NDE for the bulkhead. P. Bates assisted with AE data storage. L. Sponder
designed and constructed the load interface to the DART. S.A.
Barter provided all the fractographic information used in thepaper. and he and Dr. G. Clark provided useful insight into failure mechanisms in the bulkhead. Finally, Dr. S.K.. Burke provided valuable advice and encouragement throughout the bulkhead monitoring.

Consideration also needs to be given to overcoming the
problems of the data overflows experienced late in the presentbulkhead test. The development of a more dedicated AE dataacquisition system which allowed faster data acquisition would
prove useful for this purpose. In the present study, catastrophic
failure occurred only one program after the crack causing failure
gave substantially increasing AE (Fig. 4). However, even half aloading program would be sufficient to identify such cracking
in a component. provided that some real-time processing wasincorporated in the data acquisition system. Cenainly, thedevelopment of real-time processing would be required if AE
monitoring of an operational F/A-18 aircraft were to become areality.

S.

Conclusions

AE processing following completion of the fatigue test of
the F/A-18 FS 488 bulkhead was successful in that all major
cracks, including the pen crack which caused failure of thebulkhead, were identified. The crack depth at which AE wasdeleCtable was about 1 mm for all cracks occurring in the bulkhead. although substantial emission from the port crack causing failure was only obtained when the crack reached about 2
mm in depth. The data processing suggested that the crackrelated AE events in the bullchead were attributable to crack-facerubbing rather than crack propagation. although backgroundswdies of AE from the bulkhead material under random loading
conditions would be required to confmn this suggestion. The
sWdy shows that AE associated with cracking can be distinguished, even when many extraneous sources are present and a

AE monitoring of additional bulkhead fatigue testing isrequired to establish confidence limits in terms of the numberof AE events required for reliable crack prediction in bulkheads
in operational F/A·18 aircraft. For such future fatigue testing,the present sWdy has allowed recommendations to be made forthe development of AE equipment to improve the AE deleCtability of cracking (for AE sources associated with both crack-facerubbing and crack propagation). The promising results obtained in the present bulkhead AE sWdy should provide theimpews for such equipment development.
Acknowledgmen ts

References
I. Anderson and G. Revell (1990), "F/A-18 fuselage station 488
free-standing bulkhead fatigue test", DSTO Aeronautical Research Laboratory Aircraft Structure Tech. Memo AR-006-111.
S.A. Barter, B.C. Bishop and G. Clark (1991). "Assessment ofdefects in F/A-18 488 bulkhead test article," DSTO Aeronautical Research Laboratory Aircraft Materials Repon AR·
006-618.
0.1. Buttle and C.B. Scruby (1989), NOT Internat., 22, 81-96.
0.1. Buttle and C.B. Scruby (1990a), 1. Acoustic Emission, 9,243-254.

0.1. Buttle and C.B. Scruby (1990b), 1. Acoustic Emission, 9,255-269.
G. Clark (1977), Metals Science, 11, 531-537.

w. Graham and R.K.. Elsley (1983), 1. Acoustic Emission. 2,
47-55.
P.H. Hutton (1985), "Develop in-flight acoustic emIssIon
monitoring of aircrafl 10 detect fatigue crack growlh - Volume
I." NADC Repon 85012-60.
C.l. Kim and 1. Weerunan (1984), Microstructural Characterization of Mazerials by NoNnicTo~opical Techniques. ed. N.
Hessel Anderson et al., RISO National Laboratory, Denmark,pp. 349-353.

T.C. Lindley, I.G. Palmer and C.E. Richards (1978), Mater. Sci.
Engng., 32, }-15.
S.L. McBride and J.W. Maclachlan (1984), J. Acoustic Emission. 3, 1-9.
S.L. McBride and J,L. Harvey (1987), Review 0/ Progress in
Quantitative Nontkstrllctive Evaluation 6A, ed. D.O. Thompson and D.E. Chimenti, Plenum, NY, pp. 353·360.
S.L McBride, M.R. Viner and M.D. Pollud (1991), Review 0/
Progress in Quantitative NondeslrllCtive Evaluation II, ed.
D.O. Thompson and D.E. Chimenti. Plenum, NY (in press).
G.G. Martin (1984), "Acoustic emission from 4340 steel during fatigue", Ph.D. Thesis, Monash University, Australia.
Y. Mori and Y. Obata (1988), Nondestructive Testing
Commun., 4, 11-21.
CM. Scala and S. McK. Cousland (1985), Mater, Sci. Engng.,
76, 83-88.
C.M. Scala and R.A. Coyle (1983), NOT InternaL, 16, 339·
343.

CM. Scala and R.A. Coyle (1987), J. Acoustic Emission. 6,
249-256.
CM. Scala, S.l Bowles and I.G. Scott (1989), Inzernat. Ad·
vances in NondestrllCtive Testing, 14, ed. W.J. McGonnagle,
Gordon and Breach, New York. pp. 219-258.
I.G. Scott (1981), Proc. 13th Symposium on NondestrllClive
EvallUJlion, ed. B.E.Leonard. NTIAC, pp. 210·219.
G. Weatherly, J.M. TilChmarsh and C.B. Scruby (1986), Mater.
Sci. Technol., 2, 374-385.

Maximum Curvature Method: A Technique to Estimate
Kaiser-Effect Load from Acoustic Emission Data
M. Momayez, F. P. Hassani and H. R. Hardy, Jr.
Abstract

This technique was ftrSt used by Kanagawa et al. (1976) toestimate stresses in rocks. Their slope change method to derermine the geo-scress in an underground power plant in Japan wasdemonstrated using tuff samples. The in-siru stresses estimaledby the AE method were then compared with the values obtained
from the overeoring method. Because the average geo-stresscomponent estimated by the AE (slope change) method washigher than the one using the overcoring method, they speculated that the stresses obtained by the Kaiser-effect techniquecorrespond to the maximum stress imposed on the rock mass
during the long geological age rather than the existing in-situ
stress.

This paper presents an objective technique to estimate
previous SIreSS level from a Kaiser effect experimenL The maximum curvature method can be easily implemented as a compurer program. thus allowing a large amount of data to be pr0cessed automatically. It is shown that this technique is more
efficient than another objective method. method of tangents.
in terms of accuracy and speed of execution. The only disadvantage in using the maximum curvature method is the fact that its
performance may be hampered if few acoustic emission data
recorded at a low sampling rate are available.

1. Introduction

There are some cases where the Kaiser effect cannot be
observed clearly by plotting the data using the slope change
method. Yoshikawa and Mogi (1981) presented another method
to remedy this situation. Their difference method consisted ofloading the sample once and plotting the data as load or stressversus cumulative AE counts. If one reloads the sample arrer
unloading. it can be seen that the AE activity for the second
loading cycle is very different from the ftrSt cycle. Since thedifference begins to increase markedly at the known previous
maximum, they used this difference to estimate the stress.

For many years. the only method to estimate the Kaisereffect load from acoustic emission (AE) data was to plot time orload venus cumulative AE count and to derermine the previous
level by eyeballing the deflection point in the curve (Kanagawa
et al.• 1976). Although this method produces good results ifhigh resolution data is available. it requires some experience indetecting the exact point where the change in the slope occurs.
More recenl1y, objective techniques such as the method of tangents have been proposed (Boyce. 1981). These meabods callfor some level of judgement in the analysis of data and theresults may not be particularly consistent or accurate.

Hayashi (1979) suggested a difference method based on Ute
sum of AE rate and the sum of AE rate squared. This tecltnique
was employed to successfully determine the anisotropic stressstate in a deep excavation near a nuclear power plant in Japan.
This technique requires one load cycle and AE data must be
expressed as AE rate for the sample tested.

The present paper discusses the details of an improved
teebnique fust applied by Momayez et al. (1990). The method
estimates the curvature at each point along the Kaiser.effectcurve and provides abe exact location of the deflection point,
which can then be used to determine the previous load level.
The method can be implemented easily as an algorithm on acomputer. It is shown that if the number of data points are large
enough, the maximum curvature method provides consistentand precise estimate of the Kaiser.effect load.

A somewhat objective procedure for determining the previous maximum stress has been employed by Boyce (1981).
Here. tangents were drawn to the quiet as well as the more active
portions of the stress versus AE data. The intersection of thesetangents were then used to defme the required stress value.
Hardy et al. (1985) applied statistical methods to the reload
stress versus incremental and total count data, in order to esti·
mate the applied load.

2. Methods of Estimating Kaiser Errect
In order to obtain the previous stress level in a sample
under load, it is customary to plot time or load readings versus
the cumulative number of AE events recorded during a tesL The
point where the curve changes its slope is usually used to estimate the Kaiser effecL

Hughson and Crawford (1988) used the felicity ratio toimprove the estimates of stress using the Kaiser-effect method.
The felicity ratio is defmed as the ratio of the stress estimatedusing acoustic emissions divided by the known previous maximum stress. By determining the felicity ratio for a given material at different stress levels. a felicity ratio profile is generated
by joining the points plotted on a graph of recalled stress versus felicity ratio. This profile is then used to improve theKaiser-effect load obtained from an experiment through a seriesof iterations.

Received 18 January 1992. M. Molnayu and F.P. Hassani are affilialed
with Depanmenl of Mining and MetaUurgical Engineering. McOill
Univcmty. Montreal. Canada H3A 2A7 and H.R. Hardy, Jr. is with
Department of Mineral Engineering, The Pennsylvania State University,
Univerrily Parle. PA 16802.

Journal of Acoustic Emission
Volume 10, Nmnber 3/4

All the methods used to derermine the Kaiser-effect load are
based to various degree on the judgement of the person per
61

073~0061-06S

Copyrightel992 Acoustic Emissioo Groupfanning the analysis. The statistical approach used by Hardy etal. (1985) is the fast attempt at automating the process of analyzing AE data in the search for previous stress. The problem
with this teclmique is that if one takes the best fit tangents(using linear regression) to the stress or time versus cumulative
AE counts, it is not guaranteed that the intersection of the twolines would produce acceptable values for the Kaiser-effect load.
In such cases. some degree of personal judgement must be
exerted to adjust the slope of either or both tangent lines inorder to get more accurace values.
A totally objective method for determining the Kaisereffect point is valuable in the sense that it could be implemented as an algorithm. therefore accelerating the process ofdata analysis and allowing stress values to be estimated using
the same set of rules. In addition, in cases where the change inthe slope of the stress or time versus cumulative acoustic emission data is not sufficienl1y sharp to guarantee the correct
detection of the Kaiser-effect point, an objective method would
provide the most accurate estimate of the previous load.

3

Maximum Curvature Method

Consider a typical Kaiser-effect curve. The change of slope
is indicative of the effect of the previous load applied to thespecimen. This manifestation of the effect of past load may
occur at a slightly higher or lower load level than the one
applied previously. depending on the specimen stale and test
conditions.
The Maximwn Curvature Method (MCM) implements theidea of detecting the change in the slope of the Cumulative
Acoustic Emission (CAE) data by calculating the amoWlt ofcurvature along Ibis curve. The maximum value for the curvaturecorresponds to the deflection point (i.e.• the graph of time versus CAE is most "curved" around the Kaiser-effecl point) which
is used to obtain the Kaiser-effect load or stress.
There are different approaches available to calculate thecurvature; e.g.• see Pang (1975). The method that provides thebest results with the data obtained from a Kaiser-effect experiment was found to be the one that computes the rate of changeof the angle. This idea can be most simply described as fol·lows: the time or load versus CAE curve has an "exponential"
shape. that is. it has the greatest curvature where the change inthe slope occurs. while the remaining portions can be approximated by straight lines. If one calculates the angle e between
this curve and the horizontal x-axis and differentiates theresulting series. a measure of the curvature along the entire
length of the curve may be obtained. Denoting the curvature by
K and the angle bye. we have:e =arctan(CAE/time)
K=d9!dt

The analytical formula for the curvature K of a function, f.
at the point (a,b) is given by Pang (1975):

I

K =

I

£" <a) 1311
(l + [rea)} )where rea) and £"(a) are the flfSt and second derivatives of thefunction f at the point a. Let us now calculate the maximum curvature of the exponential function f(x) eX. Since f(x) f(x)f'(x), the formula for the curvature becomes:

=

e

K =

=

=

II

:zam

(1 +e )

Differentiating K with respect to x and setting dK/dx = O. we
obtain the critical value for x:
x =·In..{2= -0347.

Therefore the exponential function has its maximum curvature at x -0.347. Figure 1 presents the graph of the exponential function over the interval [-5. 4] as well as its calculated curvatures using the analytical fonnula and the Maximum
Curvature Method. In addition. the plot of the exponentialfunction shows a curvature around the point x = 2 which can beseen by the eye. The analytical formula for the curvature cannot
discriminate this fact and therefore it does not show up in thegraph.

=

However. the estimated curvature using lhe Maximum
Curvature Method described above has its minimwn and maximum value at x -0343 and x 2.074 respectively. These values correspond to the maximum curvature determined analytically (x = -0347) and the one that appears in the graph of the
exponential function around the point x = 2.

==

OJrvature(using analytical formula)

;"'"
1

t
u
d

-s

-4

2

3

4

e

X-Values
Fig. 1 Graph of exponential function and its curvutures.

Asswning that the AE data is sampled at an interVal of one second and the angle 9 is expressed in radians, then the curvature K
will be in radians per second.

3.2 Application of Maximum Curvature Method to Acoustic
Emission Data

3./ Measuring the Maximum Curvature of the Exponential
Function.

In order to compute the curvature of the cumulative AE
data, the Kaiser-effect curve must be smoothed before the MCM
technique is applied. The amount of smoothing that one

SampleGESOIAm 3000p

OuDulative AE CoUDts

SampleGESOI

4000

OuDulative AE CoWlS

A

m3000
p
I
i 2000tuCurvatureI
i 2000
t
U

dl000

dl000
e

o

o~----=::::;=::::::::_----..--t--_

o

10

20

30

40

50

o

60

10

20

30

40

so

60

Load(kN)

Load(kN)

Fig. 2 Kaiser-effect curve and related curvature using raw CAE
data.

Fig. 3 Kaiser-effect curve and related curvature using smoothed
CAE data.

applies directly affects the position of the deflection point in
the time or load versus CAE curve. Depending on the quality
and quantity of AE data. a number of criteria can be developed tostop Ihe smoothing process. For example, the chi.square test
can be nm to find out Ihe degree of departure from lhe original
CAE data. A maximum deviation of 0.5% is usually sufficient toproduce the desired results. In general, the smoothing process
may be stopped as soon as a degree of departure equal to 0.1%
to 0.3% with respect to the original series is achieved.

(kN) for the sample GESOI. In Fig. 2, the raw CAE data wasused to calculate the curvature. In this case. the Kaiser-effectload was calculated to be 33.9 leN. Figure 3 shows the curvature
for processed CAE data using a S-point averaging filter. It canbe seen that smoothing CAE data affects greatly the shape ofthe curvature graph and the previous load using smoothed data
was calculated to be 29.6 leN. Table I presents the results for 8
Stanstead Granite samples by applying the Maximum Curvature

Figures 2 and 3 present the curvature as a function of loadMethod and the Method of Tangents.
To test the Maximum Curvature Method on experimental
data. 4-cm diameter Stanstead Granite cores were loaded to a
maximum level of 30 leN under displacement control and heldlhere for 5 minutes. The second load cycle was applied few
houn later and the maximum level was allowed to reach 60 leN.
For the second loading stage, the AE data was collected at asampling interVal of one second. A 250 kHz resonant frequency
transducer was used. while the monitoring system amplification and threshold were set at 54 dB and 5 mV, respectively.

4.

Although Figures 2 and 3 show the Kaiser-effect curve andits calculated curvature as a function of load, it was found lhat
plotting the data as a function of time produces much sharperand smoother curves. Figure 4 shows the same data as in Fig. 3
plotted as a function of time. The reason for the increased
sharpness is that the time-based data is sampled at a constantrate; that is, the independent series (x-axis) increases evenly.
For the same reason, when the curvature is computed, the CAE
data is differentiated with respect to time instead of load orstress.

Table 1 Results of estimating Kaiser effect using MCM und lhe
method of tangents.
5apIe

tlUllDbcr\
GIlSOlGaD
GI!SD

Hr'llLaIld~LaIld~1.-1lAllIdtkN'l

Maimuaaa-e
tkN'l:l9AS4

MdIIod of"lDpu

2U!I1,.,7:SS
».107

3QA

3Q6
3QS

In addition, another computer program was developed toestimate Kaiser-effect loads using the Method of Tangents. The
program was provided with two search algorithms namely, 0)
fast search and (2) complete search. Both algorithms fit two
straight lines to the quiet and active portions of the CAE curvein the least.squares sense. The complete search algorithm
looks for the set of all straight lines fitting the CAE curvethrough a series of iterations by applying the chi-square test
until the best two lines are found. The complete search algorithm was selected in this study and the goodness of fit for all
the samples analyzed was better than 99%. The intersection of
the two straight lines was used to estimate the Kaiser-effectload. As shown in Table I, the Maximum Curvature Method
processes lhe data in a much shoner time and more accurately
than the Method of Tangents. The reported execution time (inseconds) is the time it took to analyze all eight data files on a

4kNl

sue

GBS:M

3Q2

S3.7lD
30.141

GE3QS

3Q6

~

SI.Al11

GUl6
GI!:507

303

3Ul9l

"'799

3Q2

GUll

3Q6

31053
_ _3Q,I4S_ 3CI.I1.

:59.A71
».107

3SA61

---Sl.oc..taaeD. L250..taaeD D 4.32D

1Ime - 191..,70 soc

limo - 271S.63_

Discussion

63Sample
GESOI

4000

However. lhe 444 data points from specimen GESOI sampled
every one second produced a degree of departure equal to0.214% of lhe raw data when lhe averaging filter was applied
51 limes. Figure 6 shows the curvature along wilh the raw andsmoolhed CAE curves for the limestone sample. The original
CAE curve displays a very sharp Kaiser-effect point al 34.8
MPa while the maximum curvature calculated using thesmoothed CAE curve provides a stress value of 33.1 MPa. The
agreement between these two estimates can be attributed to lhe
quality of AE data; i.e.• the Kaiser-effect curve possesses a very
sharp deflection point at the previous stress level.

OuDuJalive AE CDUDIS

A

m3000
p
I

Quvalurei 2000I
U

d 1000e
O+--'-~-=---_=::::::::'_-----1~-_

o

100

200

300

400

1200

500

A SOO

Tune (seconds)

m
P
1 400
i
I
0
u

Fig. 4 Kaiser-effect curve and related curvature using smoothed
CAE dala as a function of lime.

4000

Kaiser Effect Poinl

OrigjDaJ

SO

d
e.4QO

A

m3000
p
I

Kaiser Effect Point-800

i 2000SUess(MPa)IuFig. 6 Application of MCM to shon but well-defmed Kaiser-eCfeet daJa.

d 1000
e

10

20

30

40

so

In general, if one wishes to use the Maximum Curvature
Method it would be more appropriate to sample the data at ahigh rate for the whole duration of the experimenL As far assmoothing the data is concerned. the treated data should notdeviate more than 0.3% from the original set depending on its
quality and quantity. in order to keep the location of the deflection point within the correct range. Further tests are being carried out to investigate thoroughly the performance of theMaximum Curvature Method on poor quality data.

60

Load(kN)

Fig. 5 Previous load eslimates using MCM and melhod of tan·
gents.
microcompuler (with 2S-MHz 80386 processor) using lhe
GAUSS Mathematical and Statistical System. Figure 5 presents
the results for sample GESOI using bolh methods.

S.

The Maximum Curvature Method was also used to analyze alarge amount of AE daJa. It was found thai the accuracy of values
produced by using this technique depends on two important fac·
tors. The first is daJa quality. that is lhe CAE data must clearlyshow a deflection point in the vicinity of the Kaiser-effectload. The second concerns the number of data points c:oDectedduring the experimenL When more daJa is available for a given
tesE, lhe CAE curve will have better resolution and lhe Maximum Curvature Method will provide more accurate values.

Conclusion

Studies have shown that a computer-based Maximum
Curvature Method can be used to analyze Kaiser-effect dataobtained from various rock samples. The method produces accurate results if conditions such as (1) the quality of data, in termsof the presence of a well defmed deflection point at the previous maximum and (2) the amount of data, in terms of a relatively high sampling rate are meL Another point to take into
accomlt when using this method is the degree of smoothing
applied to the original daJa. Studies have shown that if smoothing is applied indiscriminantly. the location of the deflection
point wiD change and the correct Kaiser-effect load may not be
obtained.

Let us consider the situation where few data points (lesslhan 100) are available. The amount of smoothing applied(using a S-poinl averaging filler) to lhe data will affect to agreat extent lhe shape of lhe Kaiser-effect curve. For example.
when the 5-point averaging filter was applied 7 times to 66
data points sampled at an interval of 10 seconds from a limestone specimen, lhe difference between the two curves wasfound by applying the chi-square test 10 be about 1.783% .

References
G.L. Boyce (1981). "A SWdy of the Acoustic Emission Response of Various Rock Types". Master of Science thesis.
Drexel University. Philadelphia.
64

H.R. Hardy. D. Zhang and lC. Zelanko (1985). "Recent Studiesof lite Kaiser Effect in Geological Materials". Proc. Fourth
Conjerent:e of AEIMS Activity in Geological StrllClures andMaterials. TransTech Publ. Clausthal-ZeUerfeld, Germany, pp.
27-55.
M. Hayashi (1979), "Acoustic Emission to Detect the Geostress", Proc. 4th InlemaJional Congress, Rock Mechilllics
Symposium. Montteux, Switzerland.
D.R. Hughson and A.M. Crawford (1988). "Felicity Ratio: A
Significant Factor in the Determination of Previous Sttess
through Kaiser Effect Gauging." Proc. 15th Canadian Rod
Mechanics Symposium. Toronto, Canada, ed. lH. Curran, pp.
147-154.
T. Kanagawa, M. Hayashi and H. Nakasa (1976). "Estimationof Spatial Geosttess Components in Rock Samples using lite
Kaiser Effect of Acoustic Emission". Proc. 3rd Acoustic Emission Symposium, Tokyo. Japan.
M. Momayez, F.P. Hassani and H.R. Hardy (1990), "Sttess
Memory Measurement in Geological Materials using the Kaiser
Effect of Acoustic Emission" Proc. 10th Inlemational Acoustic
Emission Symposium, JSNDL Tokyo, Japan. pp. 476482.
L.C.F. Pang (1975). "A Method for Measuring lite Curvature ofa Boundary on a 2-Dimensional Quantized Grid", Master of
Science thesis, McGill University. Montteal.
S. Yoshikawa and Ie. Mogi (1981), "A New Melltod for
Estimation of the Crustal Sttess from Cored Rock Samples:
Laboratory Study in the Case of Uniaxial Compression".
TeclOnophysics, 74. 323-339.

65

AEWG 36tb Meeting
One difference between the transfonnation behaviors of ~l ~ ~i and ~l ~ 'Yi was discussed from this point of view.
and the effect of fatigue damage on the change of this velocity, was examined. AE measurements were made using
two wide band AE transducers (MSW). AE signals were amplified at a constant gain of 60 dB through a bandpass
filter of 100 kHz to 1 MHz. Detected AE waveforms were analyzed by off-line FFI'.
Results obtained are as follows: (1) In the 'Yi transfonnation which has larger hysteresis. a large number of AE
events and higher amplitude AE were observed in the loading process. (2) The risetime of AE source, which isrelated to the transformation velocity in the'Yi transformation, was shorter than in the ~i transfonnation. (3) With
increase of surface fatigue damage due to plane strain bending. the risetime became shorter in the ~i transformation
because of the easiness of the transformation. From that result. it is considered that the risetime measurement would
enable a new method offatigue damage evaluation in these types of alloys.
KanjiOno
Acoustic Emission Behavior of Composites: Cooperative Studies
While stationed in Japan for the past two years, I have worked with two groups on composite AE research. I willreport on these results. One is AE monitoring during tensile fracture testing of resin-impregnated sttands of highstrength, high-modulus pitch-based carbon fibers. This was done at Nippon on Co. with A. Nanjyo and M. Mohri.
The influence of the degree of surface treabnent and the Weibull coefficient of filament strength on AE behavior was
also investigated. Resin-impregnated strands were made from pitch-based carbon fibers of a single lot with varying
degrees of surface treatment When the degree of surface tre8bnent of the carbon fiber was low. many filament pullouts were observed, accompanied by many long-duration (>75 1lS) AE signals having high peak amplitudes. Withhigh degree of surface treattnent.. almost no filament pull-outs and few long-duration AE signals were detected.
Resign-impregnated strands were made with different filament strengths having Weibull coetTJCients between 5.7 and
13. When the Weibull coefficient of filament strength was high. AE signals were observed close tot the fracture
stress. When the Weibull coefficient was low. AE signals occurred over a wide range, beginning at low stress.
Short-duration AE signals also exhibited a Weibull distribution, suggesting their origins of fiber fracture. Becauseof multiple fractures of a single fiber, however, the Weibull coefficients were greater than those measured for thefilament tensile strength. These results indicated that the short.cluration, low amplitude AE signals were caused by
the fracture ofindividual carbon fibers. Comparison studies were also made with glass andPAN-carbon fibers.
The second group was at Aoyama Gakuin University with M. Takemoto and H. Suzuki. Displacement signals weredetected from model composites (quartz fibers in polyester) and signal characteristics were compared with simulated
emissions. Fiber fracture. delamination and debonding have each distinct signal behavior. Fibers treated with silane
gave different results in terms ofrise time and amplitude ofP-waves.
EdLowenhar

Towards a Practical AE System using DSP
Digital signal process (DSP) is becoming more and more common place in today's world. This popularity has comeabout due to advances in high speed, high density digital integrated circuits. and the constantly declining cost ofdigital processing as a result of the high volumes and use in the military. industrial and commercial markelplace. The
ability to convert an analog signal into a digital equivalent for the evaluation of AE signals bas been an area of highinterest in the NDT community as users are trying to fmd out as much about the received AE transient (frequency
and time features) as possible. This presentation discusses the relative merits of digital signal processing of AE
account in the design of a practical AE system.
Kusuo Yamaguchi
A View of Instrumentation and Signal Processing for AE Monitoring and Future Directions
Frank C. Beall
AE From Creep ofWood-Based Composites
Harold L. Dunegan
Quantitative Crack Growth Measurements by Use of AE
Acoustic Emission techniques have been used routinely for many years for the detection and location of growing(Continued to page 90)66

Acoustic Emission Measurements on Bridges

H. Hick, H. Willer, E. Winter and F. Simacek
Abstract

acteristic sounds were interpreted as due to a mechanical resonance of the panially loose rebar, excited by friction and/or
progressive debonding fractures between rebar and concrete.
We hoped therefore, that this could become the basis for a nondestructive inspection method for the detection of loose or
broken prestressing cables in concrete bridges. However wewere not sure that deformations due to normal traffic loads
would be suffiCient to produce these sounds and we were pessimistic with regard to the deteetability of these characteristic
sounds in the presence of strong background noise levels.

A shon experimental program was curled out to investigate the suitability of acoustic emission analysis for the structural monitoring and checking of bridges. On a prestressed
concrete bridge we could recognize typical sounds. which wereinterpreled as friction noises generated by loose rebars. SomeOat Teflon supports were found to emit strong ultrasonic signals due to metallic contact after exhaustion of the radial gap.
On a concrete box girder bridge a craclc was investigated anddiagnosed as still acoustically active, despite a previous repair
attempt by injection.

2. Experimental Equipment
Piezoelectric accelerometers (B&K 4370, AET AC30L.

1. Introduction

AET ACI75L) were coupled via magnets to steel discs which
During the past ten years, we have concentrated on acoustic emission (AE) analysis as applied to the safety monitoringof large engineering structures of rock and concrete, likehydroelectric dams. deep mines and civil engineering structures. For such large struCNres AE analysis differs from seismology mainly by its smaller geometric scale and its higher
signal frequencies (which are in the audible range) (Hick.
1982). Significant emission phenomena are fracture, friction
and turbulent flow. Progressive material damage is accompanied by cracking noises. which should not be ignored. as thenoises give advance warning oC imminent structural failure.

had been glued to concrete surfaces. Signals were ftltered (1-25
kHz, 15-45 kHz and 125-250 kHz). amplifted by 80-100 dB andanalysed in the field by an FFT-anaiyzer and also monitored byheadphones. Further analysis in the laboratory was based on
analog tape recordings and digital disk files from the FFT-analyzer.

3. Measurements on a Large Prestressed Concrete
BridgeThe bridge. with an individual field span width of approximately 50 m, was known to have a number of prestressing
cables, which had been inadequately injected. To optimize thesignal-to-noise ratio. the transducers were mounted as close as
possible to the suspected cables and as far away as possible
from the road vehicle noise (at approximately half height of
the I-beams at the underside of the bridge).

Parallel to AE analysis in the context of rock burst
research in a deep lead mine (Grube Stefanie der Bleiberger
Bergwerksunion AG) and the permanent monitoring of a largehydroelectric dam (K61nbreinsperre, Maltaw), we performedlaboratory experiments on rock and concrete samples, prestressed concrete railway sleepers and a 10 m long prestressed
concrete beam during 1978· 1983 (Hick. 1986).

Traffic noise, as measured with the accelerometers, wasconcentrated in the 3 - 7 kHz range. with a mean level of approximately 500 p.-g. However. the noise varied strongly withindividual passing vehicles. It became immediately obviousthat the human auditory system was for that frequency range
much better at identifying the various noise sources, than any
signal analysis with the FFT-analyzer.

In all these investigations AE analysis proved to be a reliable indicator of progresssive microstructural damage in advance of macroscopic fracture, except when high noise levels
from some nearby mechanical equipment masked the AE signals. We were therefore not optimistic whether the traffic noise
would allow any useful observations on a bridge during normal
use. On the other hand, earlier laboratory experiments had
shown that prestressed railway sleepers with deliberately cut
prestressing cables produced characteristic audible ("ringing"and "ticking") sounds during bending deformation with increasing or decreasing loads. while intact samples produced only
high frequency fracture noises (Hick et aI., 1983). These char

Each individual passing road vehicle produced a ''hissing''sound. very similar to the familiar noise of a passing car on a
wet road surface. This sound in the 5 - 15 kHz range is probably
produced by the abrasive action of sand particles between theelastically deforming tires and the road surface. Some vehicles
produced a periodic ~ranling" sound. similar to the sound of avehicle with snow chains on a hard road surface. Figure 1 showsthatl.he signal consists of individual sharp peaks with an irregular pattern. which repeats itself with a period corresponding toa tire revolution at a typical vehicle speed of 66 lanIb. The
irregUlarity rules out ignition spark interference as a possible
explanation. We concluded, therefore. that this noise was gen

Received 15 April 1990. H. Hick, H. Willer are affiliated willi Auarian
Researcb Centre Seibersdorf, Fonchungszcnuum. A-2444 Seibersdorf.
Austria and E. Winter and F. Simac:ek are willi Community Depanment,
Magistral der Sladt Wien.
Journal of Acoustic Emission
Volume 10. Number 3/4

Q0730-005019211fX$l.mO
Copyrightli:ll992 Acoustic EmissiCIII Group

til.

REAL

REAL

V

V

-SL---L.._&...---L.._&...---L.._&...---L.._&...--L:--:-!.

TIME A

XI

11 a. 09Il15EC

LIN

'iJ

TIME II

0.4SEC

79.g(J1II5EC

6. 8970 V

Fig. 1 "Rattling" noise due to tires with pebbles.

LIN

'iJ

O.SSEC

9.9976 V

Fig. 3 "Creaking" signal near prestressing cables (mainly 8 •12 kHz).
sr---r--r-

r---r--r-

r---r--r-

r---,

~

L

,I

til.

REAL

REAL

V

V

I

IIL
TIME IITIME II

I. 4841l15EC

I. a28111SEC

Fig. 2 "Fracture" signal near prestressing cables (mainly 8 - 14kHz).
erated by pebbles locked within the tire-profile grooves of lorries. which came from a nearby building site.
Figure 2 shows a typical time signal of events. which were
frequently observed during the passage of a vehicle but also
occasionally during the relatively quiet pauses between vehicles. These events appeared like fracture noises, as observed inlaboratory tests on concrete samples. During the passage of avehicle they might have been caused by the crushing of somesmall pebbles on the road surface. However. their occasional
observation during quiet periods indicates. that some Sb'UClUral
damage process in the bridge itself must be their cause. Also.
mainly during the quiet pauses immediately after a very heavy
vehicle. we observed sometimes events as shown in Fig. 3.
which could be described as "creaking" and Fig. 4 which could
be described as "ringing", From their audible characteristics
and their similarity to the sounds observed in the laboratoryfrom a railway sleeper with a cut rebar. we believe that these
events are generated by prestressing cables upon deformation
due to friction and/or debonding fracture. A search for othersound generation sources such as a steel inspection gallery
mounted at the underside of the bridge provided no alternative

LIN

'iJ

40lIISEC
9. 675::1 V

Fig. 4 "Ringing" signal near prestressing cables (mainly 0.3 -2 kHz).
explanation, All these events could be detected across thewhole bridge section, but were more frequent and louder under
the city-bound lane of the bridge,
With a two channel system (with its sensors installed at
opposite ends of a SO m long bridge section). we could also
demonstrate that it was possible to locate such a source bY
propagation-time-difference measurement. For hydroelectric
dams and mines, with a relatively quiet background situation.
this can be done automatically by a seismoacoustic locationand monitoring system which we have developed (Hick, 1986).
Due to the strong interference from traffic noise this was however not applicable in the present case. We had to perform
cross-correlation averaging of selected events from a 2-ehannel
tape recording with the FFr-analyzer.
During later repair work, a systematic endoscopic examination via boreholes confirmed that the city-bound lane of lite
bridge contained a higher number of inadequately injected prestressing cables. than the other lane, as well as some concrete
damage.

20kHz AIAe/SV

8:Ael SOV

INST

0/16

ChA

2k

.354
E-l

M.3.

1

MAG
Vr

-1

I

-l

0

0
XJ

PWR SP A
25625.0Hz

LIN.0158 Vr

Y.;

50kHz

• 177

E+O

I

1I

M.l.
MAG
Vr

~

0
0
~

PWR SP A
25625.0Hz

LIN. 1806 Vr

Y.;

50kHz

Fig. 5 "Friction" noise spectra of "good" and "bad" bearing.

5

til.

REAL
V

-s

l...-_.l..-_.l..-_..L..-_..L..-_""-_.l-_...L-_..a-..._..a-...---I

TIME A
4. 516mSEC

LIN
Y.;

16mSEC
4.6338 V

Fig. 6 "Friction" noise signal of "bad" bearing.

4. Measurements on Flat Tenon Support Bearings
It is well known that dry metal-metal friction can produce
strong acoustic emission at ullJ'asound frequencies. For a curved
section of a city-motorway, mounted on pylons, it was known
from accurate field survey measurements, that some of the flat
Teflon suppott bearings had exhausted their radial gap due to an
excentric displacement and were in metal-metal contact with
their stop-rim. By a quick coupling method (using a small
magnet and a steel waveguide of 3 mm diameter and 300 mm
length) it was relatively quick and easy to reach into the narrowhorizontal gap between the bridge section and the pylons andto check a number of bearings for the emission of ultrasounddue to the small dynamic deformations of the bridge during its
nannal lJ'affic load.
Figure 5 shows the signal spectra (measured with a 25 kHz
resonant transducer) for a "good" and a "bad" bearing, as known
from the surveying measurements (note the difference inscales). Figure 6 shows time signals for a bearing due to friction with maximum amplitudes of approximately 9 V peak-topeak. while for a bearing without friction. only a noise band of
approximately 0.7 V peak-to-peak was observed. A surveying
measurement requires approximately 45 minutes per bearing,
While an AE analysis takes approximately 5 minutes.

5. Measurements on a Crack in a Box-Girder
BridgeBy chance, one of the maintenance workers had observed
the actual occurrence of a crack on a box-girder bridge as a loud.
thundering noise. The crack had a length of several meters and awidth of less than 1 mm. With a micrometer, it had also beenpossible to measure a dynamic variation of crack width withtraffic load. A repair attempt by injection of plastic cement had
been made some time ago.
The aim of our measurements was to find out, if this crack
was still acoustically active. Transducer locations were selected
in close proximity (10 em) to the crack center with respect tothe crack tip as well as some 10 m away from the crack, but
always at the inside of the box, and just 1 m below the traffic
lane surface. Low frequency noise from passing road vehicles
was very intense. Therefore, a transducer AC175L with 100 dB
ampliftcation and 125 - 250 kHz filtering was used. As accessto the inside of the box was difficult, only lightweight, transportable instruments could be used.

In view of the high signal frequencies, ring-down countrates were observed on a ratemeter during the passage of heavyroad vehicles. The following peak readings were observed at
the three transducer locations (but not simultaneously) for a
succession of several typical events
Transducer Location

Counts per minute> 1 V threshhold

10 em from crack center
10 em from crack tip

110, 660, 380, 420, 690
540, 930, 3330, 3720, 2320

10 m from crack

10,

60,

30,

70,

80

These large differences show that the background countrate due to traffic noise is relatively low (10 - 80 cpm), while
the crack produces strong signals, mainly at the crack tip. The
strong signals created at the crack tip may be due to new fracture as well as due to friction between relatively fresh and roughcrack faces. We lmow from laboratory experiments that older
cracks produce less intense friction noises, probably becauseof grinding wear and loss of contact between the crack faces(Hick et aI., 1983).
These observations proved, that the repair attempt had notbeen successful. This bridge has since been rebuilt.

6.

Conclusions

The AE signals observable on a prestressed concrete
bridge during normal use are a complex mixture of various
noise sources. Within the audible frequency range, their interpretation requires careful observation and evaluation. A routine
observation of these signals could give valuable information
about unusual prestressing cable conditions. AE signals in theultrasonic range can be used for rapid and easy identification of
metallic friction contact in suppon bearings. Ultrasonic AE
signals can also be used to detect further propagation of cracks
in concrete.

References
H. Hick (1982), "Anwendbarkeit der Schallemissionsanalyse
auf sicherheitstechnische Problcme der Felsmechanik" OEFZSBencht A0335.PH.347/82.
H. Hick (1986), "Praktische Anwendungen der Schallemissionsanalyse in der Bergschlagforschung und zur Staudammt1berwachung" OEFZS-Bencht A0821-PH-428/86.
H. Hick, T. Schmeskal, R. Schwara, S. Zagiczek, C. Werbik, J.
Nemet et al. (1983), "Untersuchung zur sicherheitstechnischen
Oberwachung sowie zur Fehlerauffmdung mittels Schallemissionsanalyse fUr Bauwerke aus Spannbeton" OEFZS-Bericht
A0404-PH-365/83.

Acoustic Emission Monitoring of the Sensitivity ofChemicals to Impact
Timothy G. Crowther, Adrian P. Wade and Nancy Brown
Abstract

amounts of sample materials (mg) are subjected 00 impact. The
indication of a positive result (an explosion) is very SUbjective
and operator dependent; therefore acoustic emission was considered as an alternative method for determining if the result
was positive. and hence improve the reliability of the sensitivity values obtained for explosives and propellants.

Acoustic emission monitoring has been used to improvethe method that induslly uses to determine the sensitivity ofcompounds to impacL This method is particularly important inmeasuring the safety of handling explosive compounds intransport. and in storage. The apparatus used presently in·volves the dropping of a weight from a height onto a small
sample. which is confmed in a specially designed enclosure. A
positive result only occurs when enough energy is supplied tocause an explosion. Whether a result is positive or negative issomewhat open to the inrerpretation of the operator. Signs of apositive result include smoke, piercing of a diaphragm, or theformation of a dark residue within the sample enclosure. The
amoWlt of potemial energy (height x weight) required to cause apositive result in at least 50% of tests is termed the sensitivity
value. Used in this conventional fashion. the instrument pro·duces a single YES/NO decision per experiment. Many experi.
ments are required to characterize each sample, in what is a very
tedious procedure. In this present work it is shown that acoustic
emission can be used to effectively monitor controlled explo.
sive reactions occurring within the drop-weight tester sample
cavity. The acoustic emission resulting from the impact wascaptured using a broadband transducer mounted on a clip, which
rested on the sample holder. Frequencies from 100 kHz to 1
MHz were captured. This has resulted in an automatic methodfor distinguishing between a positive and a negative result incalibration and solid sample tests. Spectrogram (time vs. fre·quency of emission) analysis suggests that acoustic emission
may be used to probe the mechanism of the explosion within
the sample container. The high non-repeatability of results forthe nitrornethane samples was due to the piercing of the "0·ring" surrounding the sample, rather than the expected ruptureof the diaphragm situated above it. The results show that better
design of the present drop weight apparatus must be undertalcen
to improve the reproducibility. Acoustic emission will provide
a useful means to quantify that improvement.

1.1 Explosives
An explosion is a rapid combustion in homogeneous
fueVair mixtures with the flame front passing through the mixture from the source. The result of an explosion is the production of a considerable quantity of heat and a large volume ofgas. The design and application of explosives are a science andexplosives are as capable of being controlled as are other products of industry. Steel can be hardened by explosives. The
intricate patterns that are created by fireworks are dependent on
the correct mixture of compounds, and a detailed knowledge of
their burning rates and flame colors.
An explosive can be classified as being either a deflagrating explosive (propellant), a high explosive (secondary ex·
plosive), or an initiating explosive (primary explosive). A
propellant is an explosive that bums at a steady speed and canbe detonated only under extreme conditions. An initiator (e.g.,
mercury fulminate) is an explosive that is extremely sensitive
to shock. High explosives normally burn without undue violence when ignited in an open space, but can be detonated by asufficiently large sudden mechanical or explosive shock. The
most important properties of explosives are the velocity ofburning or detonation. the explosion temperature. the sensitivity to impact and the power. Sensitivity and power are measured
on a relative scale.

Originally for sensitivity, picric acid was used as a standard compound, with an assigned value of 100 (Anon., 1965).
Nowadays, each machine used for sensitivity testing has its
own method of calibration. The present drop-weight method forsensitivity testing yields very imprecise values. This is partly
because the interpretation of results from the test is very subjective; for instance. the sign of smoke, the smell of a gas, orthe visual observation of a dark residue in the sample holder
have all been used to adjudicate a positive result. Therefore amore sophisticated method for sensitivity testing is necessary.
Acoustic emission (AE) is a possible alternative for the testingof explosives, since obvious amounts of audible AE result from
explosions. We decided to monitor AE resulting from the existing shock sensitivity testing procedure, and evaluate the efficacy of both AE monitoring and the present procedure fordetermining the sensitivity of liquid and solid explosives andcalibration compounds.

1. Introduction
There is an urgent need for an alternative method for evaluating the sensitivity of explosive compounds to impact. The
resistance of a material to impact.induced explosion is one ofits most valuable quantitative characteristics, as it gives assur·
ance of safety in handling, transportation and use. The present
method involves repetitive use of a drop.weight tester. Small

Received 24 June 1991. Timothy G. Crowther and Adrian P. Wade areaffiliated with Laboratory for Automated Chemical Analysis. Chemistry
Depanment. University of British Columbia, 2036 Main Mall, Vancouver. B.C. vor IZI, Canada, and Nancy Brown is affiliated with Dow
Chemical Canada Inc, South Vidal Smel, Samia, Ontario, N7T 7Ml.
Journal of Acoustic Emission

Volume 10, Number 3/4

71

073~S0I92/10071-082
Copyrightel992 Acoustic Emission GroupSOLTEC
DIGITIZERHEIGHT OF
PLATFORMCAN BE VARIED

WEIGHTSTACK.ED
ON THISPLATFORM

RELEASEMECHANISM
FOR
DROP-WEIGHT~~~ ---'--

CONDITIONINGAMPLIFIER

00
TRANSDUCER

CUP ASSEMBLYCONTAINING SAMPLE

Fig. 1 Drop-weight testing apparatus.

1.2 Drop-weight Testingardous chemicals is the principal function of the impact test
For this reason, materials are compared with known materials
on a relative basis. rather than on a standard numerical scale(Barbour. 1981; Bowden and Yoffe. 1952; Fordham. 1966).
Many different types of drop weight testers have been designed
and consequently many different values for the same explosives
materials have been reported. Even two machines of the same
design can give contrasting values.

In the early 1930s. the Bureau of Mines Explosives
Experiment Station introduced drop-weight testing for testing
the sensitivity of explosives used in the mines. There were twomachines designed. One was a small impact machine. which
was used for testing fine-grained or finely pulverized homogeneous explosive compounds. The size and design of the apparatus was similar to the Olin-Mathieson drop-weight tester usedin our work (Fig. I). and the same sample volumes were used.
With this machine a weight of 6 kg can be dropped from aheight of 50 em onto a sample of 20 mg. The other machine
was a large impact machine. which was used to test coarsegrained material (e.g.• compounded explosives such as dynamite). With this machine a 200 kg weight could be dropped
from 7.5 m onto 80 g samples. World War caused a considerable need for a more detailed study of the fundamentals involvedin the impact test, but little headway was made; however. theapparatus and testing procedures used were improved. In 1959
the Joint Anny-Navy-Air Force Panel on Liquid Propellant Test
Methods accepted the Olin-Mathieson drop-weight tester as thestandard method for detennining the impact sensitivity of liquids. Apart from drop-weight testing. there is no other quick
and simple way to evaluate solid and liquid propellants for their
explosive sensitivity.

Bowden and Yaffe (1952) showed that "hot spots" are thereason for initiation on impact, and not uniform heating. These"hot spots" can be caused by friction between crystal particles.
the compression of small gas pockets in the solids and even byheating of the solid by viscous flow. The "hot spot" theory has
also been conrumed by Cook (1958). The importance of crosssectional area. thickness of the sample. drop-distance. anddrop-weight used in impact testing were all verified by Africano(1959). Most machines have very good agreement for sensitive
explosives but vary considerably for less sensitive materials.
probably due to the difference in striker area.

n

The thermodynamic and physical properties of a propellant or explosive can be measured on an absolute basis. However, as will be discussed below. the chemical-kinetic properties (burning rate, spontaneous ignition temperature, ignition
delay. compression ignition sensitivity, detonation sensitivity. and thermal stability) are not fundamental properties,
and therefore cannot be determined on an absolute basis. Thesechemical-kinetic properties are dependent on the temperatureand reaction-velocity relationship of a system and its heat ofreaction, and are the critical factors on which the practicabilityof a propellant or explosive must be judged. They are, in turn,functions of the physical conditions goveming the rate oftransfer of heat between the system and surroundings. and between the reactants and products. This explains the effect ofpressure on ignition and burning rate. and the critical diameter
effect in detonation and deflagration propagation (Griffin;1961).

The impact sensitivity for a given sample may be defmed
as the potential energy value (height x weight) at which explosion occurs in 5M'c of tests. In liquid propellant testing the explosion is initiated by the adiabatic compression of the gas
volume present in the sample. This process is extremely complex, and prevents the fundamental significance of the testfrom being established. The same is true for the testing of solid
propellants. although the governing factors are different. Thereis. however. no standard impact test for solids at the present
time.
The determination of the relative safety of handling haz72

STEELBALL~~CJ,.------'(;OPISTON

CAP
~

D

•

BOD Y

~~

VENT
DIA PHRAGM

0,0

'\

CU P

J I

0 -RING'\~::;AMPLE CAVITYPLUNGERBRASS CUP
CONTAININGSAMPLE/BODY

PLUNGER TIP

SOLID SAMPLE

Fig. 2 a) Sample holder for liquid samples (lOp). b) Sample holder for solid samples (bottom).
The Olin-Mathieson drop-weight test was designed to provide an accurate sample volume. a controlled bubble volume(air pocket within the cavity), and complete sealing of thesample cavity to prevent leakage during the impact and explosion. The prevention of leakage resulted in a large reduction in
the impact energy required for initiation of explosion, so thatthe method has been able to differentiate between materials
covering the full range, from insensitive fuels to highly sensitive explosives.

When a weight falls. the transfer of energy from thefalling weight to the sample is a function of many factors:
namely; the mass and velocity of the weight; the mass and surface hardness of the striker and anvil; and the cross-sectionalarea, ch.ickness, physical composition, and degree of confmement of the sample. The last of these factors is often dependent
on the constant thickness of the diaphragm used to confme theliquid sample. This thickness proved inconsistent throughout
the experiments detailed in this paper. In facl, many of thesevariables are difficult to control. even though the test conditions are arbitrary.

Compression ignition (Griffm; 1961) can be a complicated process when there is both liquid and vapor present in the73using a torqometer. Finally. the sample holder was situated in
the base of the drop-weight tester.

sample cavity. For the simple case. where just vapor compression is involved. adiabatic conditions can be assumed and thetemperature increase during compression can be expressed as afunction of the compression ratio and specific heat ratio of thegas or vapor. However, when both liquid and vapor are present
the adiabatic assumption can no longer be true. since there canbe a Iransfer of heat from the vapor to the liquid. The size of the
bubble is very important, since the rate of Iransfer of heat fromthe bubble to the SurrOlDlding liquid is inversely dependent on
the size of the bubble. Consequently for very small bubbles
adiabatic conditions cannot occur. Sample volume and temperature are therefore very important properties Cor accurate sensitivity measurements.

Solid Samples: The sample holder for solids is depicted inFig. 2b. A solid sample of mass 20 mg was weighed out on an
analytical balance, and deposited in a brass cup (8.0 mm
3.1 nun depth. and 031 mm thickness). The plunger tip (J.7
nun diameter) was !hen carefully lowered into the cup. The sample bolder was !hen placed onto the base of the drop-weighttester.

m.

Acoustic Emission Detection: The broadband transducer(Model FACSOO, Acoustic Emission Technologies Inc., Sacramento. CAl used for all measurements was mOlDlted using
insulating tape onto one end of a flexible strip of stainlesssteel (2 mm thick. 2.5 em width and 20 em length). which, inturn, rested on the sample holder. Petroleum grease was used to
improve the coupling between the Iransducer and the strip. andbetween the strip and the sample holder. The output of thetransducer was connected to a conditioning amplifier (Type2638, Bruel &. Kjaer. Naerum. Denmark). This provided aswitch-selectable gain of 0 to 60 dB in 1 dB steps. and several
bandpass fllter ranges. The ac output of this amplifier was connected to a digitizer (Model SOA 2000. Soltec Inc.• San Fernando. CAl. The digitizer transferred the data via an IEEEAS8interface to the hard drive of a PCIXT class personal computerfor later processing. The digitizer was controlled using thesoftware provided by lhe digitizer's manufacturer.

Radiometric and speclroscopic methods (Buntain et al.,1988a. b) have been used to investigate the decomposition of
explosives on the drop-weight machine. and the initial radiometric results revealed consistent, sequential emissions for
specific impacted explosives. To our knowledge, this work
(Crowther, 1991) is the first application of (ullrasonic) acoustic emission to this process.

2.

Experimental

2.1 Reagenls
Water (distilled and deionized. Chemistry Depanment.
University of British Columbia).
Dichloromelhane (AnalaR grade. BDH Chemicals, Toronto, Ont.).
Ammonium Dichromate (AnalaR grade. FISCher Scientific.

23 Method

Fair Lawn. NJ).

Initial Experiments: Improvements to the stability of thetester were made by bolting the tester to a rectangular steel
plate of 19.1 mm thickness. 914 mm length, and 610 nun
width. which in turn was mOlDlted in a wooden frame. A trigger
mechanism was also added to facilitate a more reproducible
release of the dropping weight. These improvements greatly
increased the reproducibility of the experiment.

Nitromelhane (AnalaR grade. Aldrich Chemical Co. Inc.•
Milwaukee. Wl).

2.2 Equip17U!nl
All experiments involved the Olin-Mathieson drop-weighttester (Technoproducts Division, Quantic Industries Inc.• San
Carlos. CAl. All the equipment was provided by the manufacturers of the drop-weight, unless otherwise stated. The height
and weight of the dropping weight could be altered. Different
sample holders were used for solid and liquid measurements.

Water Calibration: The purpose of the calibration procedure was to determine the sensitivity value at which hydraulicpressure from the sample causes a plDlcture of the diaphragm.
The sensitivity value for the water calibration sets the maximum sensitivity value. The sensitivity value for nitromelhane
must lie below the water sensitivity value in order to eliminate
the possibility that the diaphragm has ruptured because of
hydraulic pressure.

Liquid samples: The sample holder for the calibration test
and other tests on liquid samples was as shown in Fig. 2a. A
steel cup (25.4 mm diameter, 15.9 mm depth). with a cylinder(9.5 mm diameter. 12.7 mm depth) cut out, was positioned in
the body of the sample holder. A rubber a-ring (Type AN62275.6.1 mm 10. and 7.1 mm 00.) was placed in the bottomof the cup and pushed down fmnly using the piston (9.2 mm
diameter and 19.1 mm height). The sample solution (0.03 ml)
was injected into !he cavity created using a fixed-stroke syringe(No. 4250, Nimetrics COlpOration, Anaheim, CAl. and a stainless steel diaphragm (038 mm thick and 9.2 mm diameter) was
dropped Oat onto the a-ring. The piston was placed onto thediaptlIllgm. and was designed with a vent hole which is blocked
by the diaphragm. A steel ball (21.0 mm diameter) was placedon the piston to prevent it from moving upwards after the
explosion. The last stage in assembly was the addition of thecap to the body of the sample holder. This cap was screwed
onto the body and tightened to a torque of 0.08 kg.m (Jlb.in)

Calculation of Sensitivity Value: The sensitivity value forliquid or solid samples is this potential energy value (height x
weight) at which the probability of explosion is 50%. The
50%point (£SO) was determined by a statistical method known
as !he "Up and Down" method (Dixon and Massey. Ir.• 1957).
The sample cavity was filled with 0.03 ml of distilled water.
After every test the diaphragm and O.ring were changed, and allparts of the sample holder for liquids (Fig. 21) were cleaned
with a cloth soaked in dichlorome!hane. The conditioning
amplifier gain was set to 30 dB, and a 50 kHz-2 MHz bandpass
fdter was used. A sampling interVal of 7 J1S. and a record lengthof 65536 (64 k) data points was used. initially. to measure thetime of !he bounce of the dropped weight. These conditions
provided a time window of 389.9 ms (with 70 ms pre-trigger).

74

Table 1 Procedure for calculating sensitivity value.

NUMBER OF TRIALSTOTAL
E/Kg em 1 2 3 4 5 6 7 8 91011121314151617181920 +POSSIBILITY
140.0

+

+

- -

133.0

+

+

+

- + - -

+

-

126.0

+

-

Once Ibis was established. the sample size was then increased
10 128 k samples and the sampling interVal changed 10 200 DS.
so as 10 satisfy Nyquist ftequency conditions. and create a time
window of 26 MS.

"

0

100

- - 3 7

30

+
+

-

-

7

0

0

3

setting was cl!anged 10 400 kHz-2 MHz when it was realizedthat most of the acoustic emission observed was at 400 kHz andbeyond. A 6 kg weight was first released from a height of 50
em. If this test was negative. then the sample could not be
tested. If the test was positive (as indicated by deformation of
the brass cap. signs of gas evolution. or discoloration of thesample) then the weight was reduced to 1 kg. Thereafter. for apositive test the new weight (3 kg) would be half the previous
weight (6 kg). The procedure was continued by adding or de·creasing the test by half the previous weight until a change insign has occurred for a change of weight of approximately 5%of the IOtal weight (e.g.• for a 2 kg weight the test would flJ1ish
once Ihe change in weight had reached 0.1 kg). Once this condition was achieved. the results of a further 20 trials were aCt
quired. After every test the brass cap was changed and the plunger tip was wiped clean using dichloromethane.

A weight of 4 kg was dropped from a height of 35 em. If
the diaphragm punctured. or stuck to the piston., then a positive
result was deemed 10 have occurred. and the weight was sequentially reduced in steps of 200 g until a negative result was
obtained (no puncture of the diaphragm). From this point.
another 20 trials were camed OUL During these. whenever a
positive result was obtained. the weight was reduced by 200 g,
and if a negative result was obtained., 200 g was added. Once aminimum of 20 tests was completed. the IOtal positive andnegative tests were marked down, as well as their respective
probabilities (Table 1). The mean value. ESO, was then estimated by linear interpolation between the percentages eitherside of the 50% point (ESO 135.0 kg.em for the data of TableI). The mean may also be taken &om abe aridtmetic mean of all
abe tests (positive and negative). The sensitivity value wasalways presented to the nearest 1/10 kg em.

=

3. Results and Discussion
3.1 Inilial Modi{ICOlwn of the Drop-weighJ Tester
Bouncing of Ihe dropping weight initially provided a serious problem; since the bounce occurred at 360 rns (Fig. 3). it
was thought this could interfere wilh a long-lasting acoustic

Liquid Propellant Test: The digitizer conditions were similar 10 those used for the water calibration except that a smaller
record length (16384 samples) was used 10 make data slOrageeasier. The conditions provided a time window of 3.244 Ms.
which avoided the inclusion of a bounce. if it occurred. The
sample cavity was filled with 0.03 ml of nitromethane.
Initially, a 6 kg weight was dropped from 20 em; if puncture of
the diaphragm occurred. the weight was decreased 10 1 kg. If thelatter test appeared positive then the height was reduced until anegative result was obtained. The weight or height was gradually reduced, or increased. until a change in sign (positive or
negative) was obtained with a change of approximately 5% of
the total weighL Once these conditions were attained. 20 moretests were carried out. If the sensitivity value exceeded the energy required 10 rupture the diaphragm by hydraulic pressure(i.e. the value obtained from the water calibration), then thetest was aborted. Again the diaphragm was changed every timea test was completed. irrespective of whether the test was positive or negative.

usa
v

•

BOUNCE. OF DIlOP-UlGHT ASSElfBLY
0CCUll1lBD AT 380 IDI

o

i

.~

L

'"

8.8

o

J95
nldE

Solid Explosive Test: The same digitizer conditions were applied as used for tests on liquid samples. A brass cup was filledwith 0.02 g of ammonium dichromate and placed in the solid
sample holder (Fig. 2b). The conditioning amplifier gain was
set 10 40 dB. with a fllter setting of SO kHz-2 MHz. The fllter...............

390

<_>

Fig. 3 This trace indicates that the bounce occurs at 360 ms.
This enabled the window 10 be reduced for further experiments. _eliminating the noise associated with the bounce.

75

signal. To avoid all possibility of such interference the sampling time was restricted to less than SO ms. Furthermore, theimprovements to the apparatus (i.e. bolting of lite drop-weighttester to lite steel plate) completely removed lite occurrence of abotmce for the water calibration (but not for other samples).
Under such conditions all tests which appeared positive fromtheir root-mean-square (mu) voltage values, also resulted in theperforation of the diaphragm. The modified apparatus therefore
facilitated a fast YES/NO answer to the impact procedure, andreliable determination of a positive result for lite water calibration. Before the modifications, the results were extremely inconsistent for all experiments, and a sensitivity value for lite
water calibration could not be obtained.

tra for the duration of experiment, in the hope of being able toassociate the frequencies observed with the various stages ofimpact and reaction. The spectrograms for the liquid and solid
sample tests with no sample present (blanks) are shown in
Figs. 48, b, respectively. The liquid sample holder spectrogramshows a distinct peak at 300 kHz, which is vastly different tothe broad band of frequencies shown in the spectrogram for thesolid sample holder. The variation is caused by the different
way in which the striker interacts with parts of the sample
holder cup assembly. For instance, the liquid sample holder
contains a rubber O-ring, which would be expected to have relatively low resonant frequencies, and prevents the collision ofche two metal swfaces (the steel diaphragm with the base of thesample cup); however, direct collision between two metal surfaces (the pltmger with che brass cup) occurs wilhin the solid
sample holder, and thus would be expected to produce higher
frequency emission.

JJ WlJIer CtJ1jbrlJlwn
The modified apparatus was calibrated using distilled water. It was fotmd that a positive result could be obtained. andlItat this result could be verified not only by puncture of thediaphragm. but also by the lack of a botmce after the initial

1••

DOUllCE nAS NO. SEEN EXCUJ1lED BY
REDUaNC TIlE TIllE 1lIIlIO. TO ZS IIllI

o

13

26

TlldS (m,)

Fig. 4 a) Spectrogram for lite blank liquid sample holder withno sample present (top). b) Spectrogram for lite blank solid
sample holder with no sample present (bottom).

3.2 Fretllle1lCY-time Spectra/or Liquid and Solid Sample HoltUrs(Empty)·1.B99-iJ.,....,.......,........,......-t-r.,....,....,......,....,..........,...,.."T""T"..,...,.."T""T"..,...,.."T""T".....,..

We sought to visualize and understand the change in frequency spectrum present over the time period of the experimentby use of spectrograms. A computer program, TRAPS (Lee etal., 1990), was applied to the signals obtained from the impact, to obtain time-resolved averaged (frequency) power spec-

Fig. S Tune-domain signal for the calibration of the apparatus
using water. a) Trace from a negative result (top). A bounce
occurred at 360 ms (not shown). b) Trace from a positive result.
No bounce was seen at 360 ms (bottom)

o

13
TlMB

26

em.)

sttiking of the sample holder. The time domain signal for an
experiment in which a bounce occurred (Fig. Sa) was considerably different to that from a positive result (Fig. Sb) and therefore the two could readily be distinguished. The rms voltage ofthe signal over a frequency range of SO kHz-2 MHz was used to
predict the result of lhe impact on lhe sample. The rms voltage
values for the positive tests were always considerably less thanthat for the negative tests. in which a bounce was always observed. Table 2 shows that. when lhe bounce was removed fromconsideration by using a smaller time window. the positive
result had a higher rms voltage values (7.2±1.9 mY) than thenegative result (4.8:i:l.0 mY) at the 90 % confidence level.
Furthermore, lhe release of the explosion products (the water
calibration experiments produced water vapor) at high pressure
will undoubtedly lead to the generation of sound waves.
Generally. if the nns voltage value is more than 6.0 mY. lhen
the test is positive and rupture of the diaphragm has occurred.
The sensitivity value for water was calculated to be 133.1 kg
em (Table 1). with an arilhmetic mean of 134.4 kg em. This
ESO was consistent with values from previous tests conducted
elsewhere (5). Attainment of this value allowed the testing of
the liquid propellant (nittomethane) to be carried out. knowing
the potential energy required to rupture the diaphragm by hydraulic pressure alone.

of acoustic emission other than that produced by the initial
impact. The specttum indicates that rupture of the diaphragmoccurs approximately 1.6 ms after the initial impact. This is inagreement with previous values (Anon.. 1965) obtained else
where. The increase in intensity of the frequency band centeredabout 300 kHz after approximately 1.6 ms (Fig. 6a) is associated with the rupture of the diaphragm by hydraulic pressure.
Where no rupture has occurred this increase is absent, as seen in
Fig. 6b. The boiling of water is known to give similar spectta
with the majority of emission at frequencies below 300 kHz.
The peak around 1 MHz is believed to be the movement of thesample holder parts in the initial impact. and the friction
thereby produced.
3.5 Uquid Sample Testing of Nitromethane
The test for nittomethane proved difficult because the explosion of the sample refused to pierce the diaphragm. but instead favored cutting through the O-ring, and scoring the sideof the piston. Because of this. the time domain signal obtained
was found to vary considerably. The higher magnitude of rms
voltage values. relative to those from the water calibration. isin part due to the different time window considered. A typicalnegative result is shown in Fig. 7. It proved impossible to calculate the sensitivity value (ESO) for nittomethane because novalue had a 50% chance of exploding (Table 3). None of thepositive tests mentioned in Table 3 resulted in rupture of thediaphragm; therefore. a positive result was assigned when the
O-ring and piston had been scored. The maximmn weight of
6.05 kg could not be exceeded since the sensitivity value would

3.4 Spectrogram of Water
The acoustic intensity obtained from calibration tests(Fig. 6) was considerably greater than that obtained from a
blank test, in which the sample compartment contained no distilled water (Fig. 4a). This observation confmned the existence

Table 2 Correlation of rms voltage values with positive and negative results for water calibration.

TEST NO. HEIGHT/em

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35
35

WEIGHT/Kg E/Kg em

4.0
3.8
4.0
3.8
4.0
3.8
3.6
3.8
4.0
3.8
3.6
3.8
4.0
3.8
4.0
3.8
3.6
3.8
4.0
3.8

140.0
133.0
140.0
133.0
140.0
133.0
126.0
133.0
140.0
133.0
126.0
133.0
140.0
133.0
140.0
133.0
126.0
133.0
140.0
133.0

71

pas NEG REMARKS RMS/mV
+
+
+
+
+

+
+
+
+
+

- BOUNCED- BOUNCED-- BOUNCEDBOUNCED-- BOUNCEDBOUNCED- BOUNCEDBOUNCED- BOUNCED- BOUNCED

9.069
4.159
8.384
5.836
7.562
7.631
4.750
4.555
7.214
6.350
5.702
4.736
8.126
4.071
7.379
5.437
4.650
3.841
4.649
5.624

Table 3 Lack of correlation between rms voltage values and positive and negative results for nittomethane.

TEST NO. HEIGHT/em

1
2
3
4
5
6
7
8
9
10
Jl

12
13
14
15
16
17
18
19
20

20
20
20
20
20
20
20
20
20
20
'20
20
20
20
20
20
20
20
20
20

WEIGHT/Kg E/Kg em POS NEG REMARKS

6.00
5.95
6.00
5.95
6.00
6.05
~6.05

6.05
6.05
6.05
6.00
6.05
6.05
6.05
6.00
6.05
6.05
6.0l>
6.05
6.05

120.0
119.0
120.0
119.0
120.0
121.0
121.0
121.0
121.0
121.0
120.0
121.0
121.0
121.0
120.0
121.0
121.0
121.0
121.0
121.0

+
+

-

--

+

+

+

---

RMS/mV

75.8
147.1
45.07
181.2
108.9
255.0
148.8
180.9
258.3
79.7
343.7
236.4
98.9
61.1
73.9
310.1
71.6
161.3
196.2
58.9

1'1~

Fig. 6 Time-frequency domain traces for water calibration experiments. a) A positive result (left): the 300 kHz (impact) peakdecreases in intensity after 1.2 ms. This is followed by an increase in the peak intensity due to rupture of the diaphragm andsubsequent release of water vapor. b) A negative result (right): abe 300 kHz (impact) peak again decrease in intensity of the after 1.2
Ms. but the diaphragm does not rupture. and so the 300 kHz emission does not increase thereafter.

78

3.6 Spectrogram of Nilromet1tJJM

UBB I

The inconsistency of the frequency spectra for negative
results for nitromethane are shown in Fig. 8. The positive
results provided equally inconsistent frequency spectra. Thereseems much randomness in the intensities of the 3D surfaces
generated and. while the surfaces exhibit the same general frequencies as fOlDld for the liquid sample holder, no distinct spectral features exist which distinguish a positive from a negative
result.

3.7 Solid Sample Testing of AmmoniIUn Dichromale
For the solid samples. again much variation was seen in
the way the explosive deforms the brass cap, and the degree towhich the sample actually explodes. One reason for this variability is almost certainly due to the grain size of the solid
explosive sample (Bowden and Yoffe, 1952). If samples wereground to the same particle size distribution then the results
would be improved. However. grinding of explosive materials
bears its own hazards. A typical time domain signal can be seenin Fig. 9. The time domain signal always consisted of several
intense bursts of acoustic emission followed by a decay after
2.5 ms. The calculation of the sensitivity value was straightforward and a value of 180.1 kg em was obtained. A high nns
voltage value indicated a positive result, whereas a low rms
voltage value indicated no detonation (Table 4). The acoustic
detection of detonation proved to be very dependent on where,
and how well the transducer was coupled to the metal strip resting on the sample holder, and where the strip was placed on theTJ)l£ (IIII)

Fig. 7 Time-domain signal for a typical negative result from
nitromethane.
be too close to that for hydraulic rupture, and the maximum
amount of weight that could be added was 6.05 kg. This restriction limited the test to a maximum value of 121 kg cm andtherefore if the test proved negative at this value (which it fre
quently did) then the value could not be further increased. Thisfactor. along with the absence of puncture of the diaphragm.
cutting and or melting of the diaphragm. and the random alternative escape route of the reaction products up the sides of thepiston. resulted in acoustic nns voltage values that did not follow any particular pattern. However. Table 3 indicates that ahigher rms voltage is generally associated with a negative
result.

1:

,

,

~,

i~
i""-

I'~

~

~'"

~,

i,

,
" ..- "

"<c'_

..-~

~-

,~

'<:-~

'""-

-t...

-

•

.

. --.......
Fig. 8 SpeclIOgrams showing the inconsistency of negative results for nitromethane samples. The variability in the spectra is due
largely to the liquid sample holder. The diaphragm did not rupture, but rather the sample scored the O-ring and escaped through the side.

79

Table 4 Correlation of rms vollage values with positive and negative results for ammonium dichromate.

TEST NO. HEIGHT/em WEIGHT/Kg E/Kg em POS NEG REMARKS RMS/mY

1
2
3
4
5
6
7
8
9
10
11
12
1:\

1..
15
16

17
10

19
20

50

GO
50
50
50
50
50
50
50
50
50
50
50
50
50

GO
50
50
50
50

3.65
3.60
3.55
3.60
3.65
3.70
3.65
3.60
3.65
3.60
3.65
3.70
3.75
3.80
3.75
3.70
3.65
3.70
3.65
3.70

182.5
160.0
177.5
180.0
182.5
185.0
182.5
J80.0
182.5
180.0
182.5
105.0
187.5
190.0
107.5
165.0
182.5
105.0
182.5
105.0

+
+

+
+
+

+
+
+

+
+
+

-

-

---

26.98
30.76
9.77
24.87
15.42
38.00
65.55
20.79
42.59
26.01
24.38
58.39
42.30
31.10
14.97
48.61
46.07

noise. and some of the acoustic emission detected was fromtheir deformation and cracking.

1.800,
I,
I

I
I

3.8 Spectrogram of Ammonium Dichromate,I

Again it was observed that the acoustic intensity for the
spectrogram of ammonium dichromate was significantly greater than the acoustic intensity observed for a blank test (nosample present). We were expecting to hear the fracture processes involved in the crushing of the solid sample. and thesounds of subsequent reaction. The initial spectra for ammonium dichromate indicated that there might be acoustic activity
at frequencies beyond 400 kHz, and therefore a higher frequency
band filter (400 kHz-2 MHz) was used. The acoustic emission
above 400 kHz provided a fairly reproducible frequency spectrum. The difference between a detonation (Fig. lOa) and no
detonation (Fig. lOb) was readily seen (as expected from therms vollage values) in the intensity of the emission spectrum.
Likely reasons for the increase in intensity of the emission
spectrum include crystal fracture. release of explosion products,
and Ihe high frequency emission associated with Ihe deformation and cracking of the brass cup after the sample detonated.

TIME (ms)Fig. 9 Typical time-domain signal for the ammonium dichromate solid sample.
sample holder. The reason for this is probably due to the easewith which the released explosion products create sound waves
which are intense enough to reach the transducer, through the
thick steel of the sample holder.

4.
Experiments to increase Ihe intensity of sound released due
to an explosion resulted in a procedure which was too noisy(audibly) for the laboratory. These experiments used stainlesssteel cups (12.7 rom diameter. 6.1 rom depth, and 2.5 rom
thickness). The intention was to increase the amount and reproducibility of the acoustic energy by (i) improving the confinement of the reaction gases within a stronger cup. and (ii) removing any effects caused by the variability in fracture of thebrass cups. The original brass cups resulted in less audible

Conclusions

Acoustic emission has been successfully applied to the
dropweight tester method for measuring the sensitivity of
explosives and propellants. Modifications made to the dropweight apparatus eliminated problems in calibration caused bybounce of the dropweight and generally increased the reproducibility of operation of the impact procedure. Once these
improvements were made, acoustic emission successfully monitored a reliable series of instrument calibration experiments.
80nns voltage provided a numerical basis for assigning a positive
result, and was more reliable than the previously uncertain
YES/NO assignment of the result by the operator. Problems
with the (standud) design of the liquid sample cavity resulted in
the failure to quantify the sensitivity of liquid propellant samples.
This work suggests that acoustic emission should prove to
be a useful tool for redesign of pans of the apparatus which arepresently the major sources of non-repeatability. The sample
holders must be designed to respond in a more reproducible way
when an explosion has OCCU11'ed. The diaphragms provided by
the manufacturer were too thick for successful application to
the liquid propellant tested. and should be redesigned. Since thetemperatures and pressures generated in the niaomethane experiments were 100 high for the rubber O-ring 10 withstand,alternative Oring materials and designs should be evaluated.
The strength of the brass caps used for solid samples should be
increased.
The ability to measure acoustic frequency spectra at intervals of 0.15 ms or less throughout the brief course of the dropweight experiment has transformed this instrument from adevice which provided a simple YES/NO decision per experiment into one which yields numerical information in two
dimensions. Currently. the time taken to perform the necessary
computations on large data sets can be significant, but nearreal-time spectral analysis will be possible with a digital signal processor. Spectrogram analysis indicates that acoustic
emission may provide an alternative means to determine sample explosion rates.

Acknowledgmen ts
Special thanks are due to Richud Ironmonger for his assistance with modifications to the apparatuS and his knowledge of
explosives. The authors also thank Brin Powell and Brian
Snapkauskas for their engineering assistance. This work wasmade possible by Grant 550886 from the Institute for Chemical
Science and Technology. and matching Grant 580389 from the
Cooperative Research and Development Program of the Natural
Sciences and Engineering Research Council of Canada.

...-._

References
A. Africano (1959), "Maximum Rate Theory of Impact Sensi·
tivity", presented at the Cryogenic Engineering Conference,
University of California, Berkeley.

1,2t-1t7

Anon. (1965), Data Bulletin #66830, Technoproducts Division, Quantic Industries Inc., San Carlos, CA, p. 1.
R.T. Barbour (1981), Pyrotechnics in Industry, McGraw Hill,
New York.

Fig. 10 Time-frequency spectra for impact sensitivity testingof ammonium dichromate at 182.5 kg em. a) DelOnation occurred (top). This is a positive result, as indicated by the intense
peak arolDld 900 kHz. The intensity of a positive result isgreater than a negative result. b) No detonation occurred(botlOm). The peak around 900 kHz is not so pronounced forthis negative result.

J.P. Bowden and A. D. Yoffe (1952), Initiation and Growth of
Explosion in Liquids and Solids, University Press. Cambridge.
G.A. Buntain. T.L. McKinney, T. Rivera, and G.W. Taylor(1988a), 19th Annual Conference for ICT. Federal Republic of
Germany.

and determined the presence or absence of reaction within thesample chamber for solid samples. In both cases the acoustic

81

G.A. Buntain, T.L. McKinney, T. Rivera. and G. W. Taylor(l988b), 9th International Symposium on Detonation. Silver
Spring, MD.
M.A. Coole (1958), The Science of High Explosives, Reinhold
Publishing Corp., New Yorle.
T.G. Crowther (1991), M.Sc. Thesis, University of British
Columbia. April.
WJ. Dixon and Fl. Massey, Ir. (1957), An Introduction to
Statistical Analysis, 2nd Edition. McGraw Hill, New York.

S. Fordham (1966), High Explosives and Propellanls, Pergamon Press, London.
D.N. Griffin (1961), 'The Initiation of Liquid Propellants andExplosives by' Impact", presented at Propellants, Combustionand Liquid Rockets Cont., Palm Beach, FL, Apr. 26-28,
American Rocket Society, paper No. 1706-61 (Source: International Aerospace Abstracts, 1961).
O. Lee, Y. Koga. and A.P. Wade (1990), Talanta, 37, 861.
S.F. Samer (1966), Propelkmt Chemistry, Reinhold Publishing Corp., New York, (1966).

82

Study of Acoustic Emission Generation in Sliding Motion

Musa K. Jouaneh, Richard Lemaster and Frank C. Beall
Abstract

AE signal analysis has been used in several contact studies(Rangwala et aI., 1988; Dornfeld lind CIi. 1984) in the past andthe results obtained showed that AE was sensitive to parameterssuch as the contact separation distance lind the initiation ofslip. Our objective in studying sliding motion is to understandhow the geometrical properties of a surface affect the characteristics of the AE signal that is used to monitor this interaction.
A possible configuration for the AE method is to attach the AE
lransducer to a probe that is dragged over the surface. Due to thecontact nature of the proposed technique. it is speculated that it
will be sensitive to rough surfaces. AE technique offers several
attractive features. The AE signal produced is a function of the
distribution of surface asperities. It is a low cost technique that
CIIn be used at moderately high speeds, and can operate in realtime.

Increasing interest in automation requires the developmentof a variety of sensors for monitoring and feedback purposes.
One such area is taelile sensing where there is a need for sensors to characterize the interaction between a probe and the SW'face. Acoustic Emission (AE) is sensitive to plastic deformations ahat occur in a material due to dynamic contact. This
paper investigates AE signal generation during sliding motion
when a probe moves over a surface. The objective is to Wlder·stand how geometrical properties of the SW'face affect the characteristics of the resulting signal. An AE sensor, connected to aprobe through a waveguide. picks up the AE signal as the probe
is moved over the SW'face. A needle, bar. and a rod were used as
contact probes. The AE generation mechanism is assumed to be
primarily due to the following two sources: impact of the moving probe with surface asperities, lind deformation of asperities
due to sliding motion. Coated abrasives rllnging from 600 to
36 grit size were used as testing surfaces.

Since surface characteristics directly affect the interaction
process in sliding motion, it is of interest to describe the current techniques used to measure surface characteristics. The
mechanical stylus and the optical profiIometer are the mostwidely used teclmiques (Sherrington and Smith. 1988a), withthe mechanical stylus teclmique being one of the oldest forrecording surface profiles. The stylus, a pointed needle with around diamond tip, is held from a supporting fulcrum through alever arrangemenL In operation. the stylus is dragged over thesurface at a constant speed. and a record of the vertical motionof the tip is kepL Amplification of the profile features is provided by the lever ratios in the coMecting linkages. A transducer, such as a Linear Variable Differential Transformer (LVDT), transforms the mechanical motion of the stylus into
electrical signals.

Experimental results using various feed speeds and thethree different probes have shown that the AE event COWlt isthe most sensitive to variations in grit sizes while the rms
voltage lind mean peak amplitude per event of the AE signal are
only sensitive for low grit sizes. In addition, the needle lind bar
are more sensitive thlln the rod probe. For hard materials. suchas co71-81ving probe with asperities is the more dOnUnllnt
source of AE. This work suggests that. with further study, AE
might be able to be used to measure average SW'face roughness
characteristics.

1. Introduction
Usually, two configurations of the stylus teclmique havebeen used: the true datum stylus, and the surface datum stylus(Maxey, 1964). The true datum stylus gives a true profile of thesurface. since the vertical motion of the stylus is with respect

Increasing interest in tactile sensing as a means of providing feedback on surface texture requires the development ofsensory techniques to capture this information. Humans perceive texture through dynamic sensing in which some form ofcontact motion is involved (Hollerbach, 1984; Schiff andFoulke. eds., 1982). Dynamic sensing is needed to Wlderstandfriction and wear in contacting surfaces, and to detect lind control slip in robotic grasping operations. A review of dynamic
sensing techniques is given in Cutkosky lind Howe (1988). An
essential part of the sensing process is to understand the interaction between the sensor and the surface. One possible method
for assessing contact characteristics is acoustic emission (AE)sensing.

to an accurately Oat or curved shape which corresponds to thenominal shape of the specimen. The surface darum will hide
(2nd order) irregularities since the stylus records only differences between the surface and a skid or shoe ahat rides on thesurface. Since in areas of gentle curvature. the plane of the skid
is close to the surface, the tracing will appear as a horizontal
line corresponding to the initial zero-set position. This highpass filtering of surface wavelengths larger than the skid size isa shortcoming of surface datum stylus. While the true datum
stylus configuration can record all variations from the nominal
shape, it is a more time consuming method thlln the surface
datum one. and requires extensive setting up procedure to alignthe specimen and datum (Sherrington lind Smith. 1988a).

Received IS May 1991. M.K. JOUllJleh is affiliated with Depanment of
Mechanical Engineering & Applied Mechanics, University of RhodeIs1llJld, Kingston, RI 02881, and R. Lemaster and F.C. Beall are with
Forest Producu Laboratory, University of Califomia, Richmond, CA
94804.
Journal of Acoustic Emission
Volume 10, Number 3/4

Although, current stylus designs can offer high verticalresolution, they have some limitations. The dynamics of motion prevents rapid transverse movement «7.6 cm/min), and830730.oo50192/10083.()89
Copyrighttell992 Acoustic Emission Groupusing a pin-on-disk experimental setup. In wear sliding tests,
the AE rms voltage signal was characterized into three distinct
regions depending on Ihe time elapsed from Ihe start of thetest. In the velocity and load tests, the mean level of AE wasshown 10 be an increasing linear function of sliding speed
under different loads.

since it is a fragile device, it requires a clean enviromnent andthus is not applicable to on-line production conditions. In
addition. the width of the stylus tip must be small compared 10
the lateral size of the surface irregularities.
The other commonly used technique, optical profilomelry,generates a profile of the surface using a laser beam that replaces the diamond tip in the mechanical stylus technique.
Different configurations of profilometers have been developed,
but all are made primarily of two parts: a light source with alens focusing system, and a deteclOr 10 monitor the intensity of
the reflected light. While optical techniques have desirable
properties (Sherrington and Smilh, 1988b) such as they are
non-contaeting and Ihus do not damage the surface, Ihey are
adaptable to operate in an on-line fashion, and they do not
require special specimens 10 be prepared, they have some limitations (Stedman, 1989). The horizontal resolution is dependent on the wavelength of the incident light and Ihe numericalaperture of the focusing lens. Also, errors might occur in nonhomogenous specimens due to variable phase changes onreflection. Furthermore. there is a limit on profile angle orslope that can be measured by !his technique.

The above experimental studies have shown that AE issensitive 10 contact when the relative velocity between contact
surfaces is high. AE has also been shown to be sensitive toslipping motion of a workpiece. Dornfeld and Handy (1987)
have investigated the influence of normal load, material combination, surface finish. and relative velocity on Ihe rms voltage level during slipping motion. Their experimental setup
consisted of an alwninwn block mounted on a dynamometer onwhich specimen blocks of various materials and surface finish
were placed. The tangential force and the rms voltage of AE
were recorded at Ihe inception of slip as Ihe blocks were moved
along the alwninum plate at different speeds. For the case ofsteel on aluminum, an interesting dependence of AE spike
amplitude on nonnal load, surface finish, and velocity wasobserved. At high velocities, the AE amplitude increases with!he normal load for rough surfaces and shows the opposite trend
for smooth surfaces.

This paper is divided inlO several sections: in the next section a review of previous studies of AE in contact motion is
given; Sec. 3 discusses the assumed mechanism of AE generation in sliding motion; Secs. 4 and 5 discuss Ihe experimental
approach used to test the ideas; and Sec. 6 presents and discusses the data obtained. The last section presents our conclusions for this study.

In a study of slip detection for cylindrical workpieces,
Rangwala et aI. (1988) discuss the generation mechanism of AE
during slip motion. Using a two-fmger gripper with rectangular
fIngers attached to an industrial robot, Ihey examined the rotational and translational slip modes. They showed that rotational slip, which involves a transition from line to point contact, generates a high frequency, attenuating type of AE signal.
For translational slip, which does not involve a change of contact mode, the AE signal is a periodic signal with superimposed, high frequency activity. It is speculated that AE generation during sliding is produced by surface asperity deformation.
formation of microcracks, and separation of asperities from thesurface due to fatigue and microcutting. It is seen that Ihe above
swdies have only concentrated on detection of slip or toolworkpiece engagement, but not on a study of surface geometryeffect on AE generation in sliding motion. For identifying texture. it is important 10 understand the characteristics of AE signals as a probe is moved over a surface.

Z. Previous AE Studies in Contact Motion

Several investigators have examined the use of AE in contact studies. Much of the discussion below follows from
Rangwala et aI. (1988). In one of the rll'St attempts, Dornfeld
and Cai (1984) conducted grinding experiments in which therms voltage level of AE was monitored during the process ofwheel sparkout. Sparkout is defined as the end of material
removal as indicated by the end of spark generation. They
showed that the AE energy level is very sensitive to sparkout.
As the level of engagement between the grinding wheel and theworkpiece gradually decreases, so does the AE level. Sparkout
is then marked as the point where the AE energy level drops 10
lhe backgroWld noise. The above authors have also shown that
lhe AE energy level can be used 10 indicate the instant of rll'St
contact by monitoring the AE level as the gap between thegrinding wheel and the workpiece is decreased. A sharp increasein the signal is observed as the gap goes to zero. By using asuitable threshold, the instant of rU'st contact can then be monitored.

3. Sources of AE In Sliding MotIon
Previous studies on friction and wear mechanisms(Czichos, 1978; Suh. 1981) show that materials can exhibit
two basic responses to sudace traction: deformation of the sur·face and subsurface, and fracture of solids. Both have been
demonstrated as potential sources of AE (Ono, ed., 1979). In
sliding contact, additional sources of AE from friction and wear
include impulsive shock due to asperity collision and debris(Sayles and Poon, 1981), and microvibration excited by stickslip at the interface (Dornfeld and Handy, 1987). In using AE
for studying sliding motion, our technique will use a probe !hatis moved over !he surface. In Ibis configuration, the AE generation mechanism will be assumed to be primarily due to the two
foUowing sources: 1) Impact of the moving probe with surface
asperities, 2) Deformation of asperities due to sliding motion.

In another investigation, Fleischaker (1986) investigated
Ihe sensitivity of !he AE rms voltage to sparkout in grinding.
Using a grinding wheel Ihat was mounted on a milling table,
several runs were made in which Ihe workpiece was moved inboth directions with a preset dep!h of cut. The above tests haveshown a similar trend to Ihe experiments discussed in the previous paragraph. In an investigation of sliding contact andwear, Iiaa (1989) has studied Ihe influence of process variables
such as loading condition, sliding speed, and distances on the
AE generated from the sliding contact of metal-metal pairs844. Experimental Setup

Lever arm

Device holder

Sensor adapter

The experimental setup consists of five parts: a device
holder. a sensor lever arm, a sensor adapter. an AE sensor. and a
waveguide-probe piece. Figure I shows these parts. The sensor
assembly. which was mounted on the lever arm. was attached toa Cincinnati milling machine. The lever arm position can be
adjusted to apply a given effective load at the probe end. In
addition. lead counterweights molDlted on the lever arm are used
to change the effective load. The sensor adapter. an angleshaped piece which holds the AE sensor and the waveguide.
probe piece. can be adjusted to change the orientation of the
waveguide-probe about two axes. The waveguide-probe is anylon·66 cone-shaped part made of two pieces that are screwed
together (see Fig. 2). The AE sensor rests on the base of theupper part and probes of different geometry are attached to thelower pan. The probe piece has small holes in its side which
provide an outlet for the excess AE couplant between the probe
piece and the waveguide. This design aUows for different probe
pieces to be attached to the sensor without changing the sensor
position while also insuring good coupling. In addition. thearrangement offers a convenient way for replacing probes due
to wear.

I@
AEsensor~::F==1C?

Waveguide-probe piece

Fig. 1 A schematic of experimental setup.

1-1 ~-1

A needle. a thin rectangular bar (edge). and a rod (roller) are
the three different probes that we used in this study. Table 1
shows a prome of each probe with its dimensions and material
properties. Due to the different contact areas of the threeprobes. the counterweights in the above set were adjusted differently for each probe to obtain a particular effective probeload. The loads were 0.020.0.060. and 0.19 kg for the needle.
bar and rod probes respectively.
Table 1 Probe data.

11111_1-I l- 0.15
Needle

Bar

Rod

Promo
SoctlOn

Note: All Dimensions are In Inches.

Rod l'rClbo

IlHdI8 I'l'cllo

Ber I'I"cbe

Hardenocl Clll'bon
Stool

Hal'denocI SpMnQ

Hardsnocl Cerbcn

Stool

Stool

V
r-~ aw-j

U U~!--ll.68 aw--jFig. 2 A cross section of the waveguide-probe piece.
The assumption is that these two components occur at thesame time with the relative conlribution of each depending on
the hardness of the surface. The probe marerial should be madeof hardened marerial that has good wear resistance and is a goodtransmitter of AE signals. The normal load effect can be investigated by designing a setup which can apply a variable effective load at the probe. Since the probe is intended to study sliding motion. its design should allow for maintaining contact
with the surface at all times. The dynamics of moving the probe
at high velocities over the surface calls for a design which minimizes vibrations. These considerations were taken into
account in designing the experimental setup discussed in thenext section.

The workpiece consists of len 203 nun x 38 nun slrips of
3M silicon carbide coated abrasives that were glued to a rectan·
gular piece of fiberboard. Coated abrasives were selected since
they are readily available and they represent random surfaces(Pandit et aI.• 1977; Phadke et aI•• 1975). typical of the primary texture components of ground or lapped surfaces (Peklenik and Kubo. 1968). The slrips were equally spaced 19 mmfrom each other using Avery self-adhesive white labels. The
labels provide a smooth transition between the different coatedabrasives. The coated abrasives samples were made of closed
coat grits on dry/wet cloth with grit sizes ranging from 600 to
120. Table 2 lists the relevant data on the coated abrasives
used, and the appendix provides a brief description of coated
8S

Coat

Backingmoving the rod probe (low sensitivity) at a speed of 0.68 cmls.
The AE events for four threshold levels ranging from 0.25 to
1.0 V were monitored. While the 0.25 V threshold level gives
greater signal amplitude. the 0.5 V level shows a beuer discrimination between the different coated abrasive surfaces.

ClosedDry/wet6. Results and Discussion

Table 2 Coated abrasives workpiece data.
Abrasive Index
600
500
400
360
320
240
220
180
150
120

size (11111)
14.5

18.2
22.1
27.3
34.3
51.8
66.0
80.0
95.0
116.0

Several parameters that are available on the AET 5500
weIe initially considered. After much data analysis. only threeparameters were kepL They include the event count, the mean
peak amplitude per event, and the rms voltage. These parameters were chosen since they describe different but complementaIy components of the AE signal. The above parameters werecomputed for every 1.27 mm displacement so that the effect ofspeed variation could be compared. The time interval between
separate events was set to 2S6 J1S on the AET 5500. For illustration purposes. the average of each parameter over the coatedabrasive sample length (38 mm) is displayed.

abrasives struCture. The coated abrasives workpiece was held on
to the milling table using screws. The vertical position of thetable was adjusted so that the sensor lever ann was oriented
horizontally with the probe touching the workpiece. At thisposition, the axis of the conical waveguide was oriented at 100
with respect to the vertical direction. The output of the AE
sensor is connected to a fl1ter and a 40 dB preamplifier. The
output of the preamplifier is then fed into an AET 5500 acoustic
emission system where the signal is amplified by an additional
40 dB gain before being processed.

220r---:-=-:--"""":"---:-----------.....,
Roller Probe
200 - • 08 1n/1ll1n• 16 In/1ll1n
180 - • 30 1n/lllin
160

S. Experimental Procedure.. -.. -- • • •Since many combinations of filters and AE transducers canbe used, several tests were performed to determine the besttransducer-mter combination. The tests were performed bymoving an earlier version of the needle probe over a finely fmished hardwood workpiece at a speed of 6.7 mmls. The wood
surface represents a weak surface interaction situation. andhence suitable for measuring sensitivity. Using an AET(Acoustic Emission Technology Corporation) AC3G-L, AC75L, ACI75-L, and AC375-L resonance transducers with five different filters: 15-45 kHz, 45-90 kHz, 625 Hz-125 kHz, 0.1250.25 MHz, and 0.25-1.0 MHz, the rms voltage level of the AE
signal was monitored. The ACI75-L and the AC375-L transducers showed the most sensitivity in terms of bigher magnitude
mean nns values for most of the filters used. with the AC375-L
performing better for the bigher ftlter ranges. The AC375-L
transducer was then selected and similar runs were made using
625 Hz-G.I25 MHz and 0.125-1 MHz ftlters. In this case. thefrequency content of the amplified signal was analyzed. The
data shows that most AE activity was in the frequency range
100400 kHz. The tests showed that a 375 kHz transducer witha 0.125-1 MHz filter would be the most sensitive in our application, and subsequently all runs were made using this combination.

c: 140

::I
0

u

120

c: 100

lI:I

>
w

80
60

-

40 20

-

00

•
•

••
•
•
• •

• •
•• •

•

I

20

•
•

• •

•
•

•
•

•
•

I

•r

•

I

40

60

80

100

•
I

120

Average grits lze (um)Fig. 3 Average event count vs. average mineral particle diameter (rod probe)
Figures 3-5 show plots of the average event count for thethree probes used. The data is plotted against the average mineral particle diameter size (Anon.• 1984; see Table 2 for CODversion to abrasive index). Since this parameter is understood

to be a crude approximation of coated abrasive geometry. morequantitative representation of coated abrasives geometry usingparameters such as the ones obtained from a broad probe proftlometer (Gagliardi et al., 1985) is underway. Several trends
are observed in the data. The fll'St is the needle and bar probe are
more sensitive to variations in grit size and show more noticeable patterns thaD the rod probe. The rod probe shows a decreasing event pattcm only for particle diameter sizes larger
than SO IUD. This can be attributed to the fact that the geometries of needle and bar probes cause more impact with the asperities. The second trend is that the events decrease as the grit
size diameter increases. since fewer asperities per unit length
will come into contact with the probe. While the intensity of
impact increases as the grit size increases. the event count isinsensitive to the signal amplitude as long as the signal exceeds the threshold level.

AE data was collected for the three different probe geometries at three different speeds. The speeds were 0.35 cmls wbich
for simplicity is referred to later as 0.35 cmls. 0.68 emil, and1.21 emla. The speed ratios are approximately in geometricproportion, and the 1.27 cmls speed is the maximum tablespeed for the milliDg machine. In each run. a different segmentof the coated abrasives is traced.
Since the event count and duration are dependent on thespecification of a threshold level. a threshold level needs to be
specified. A 0.5 V value was used for the different probe geometries and speeds used SO that a comparison of the sensitivityof each probe could be made. The 0.5 V level was selected by
86

220
200-

Edge Probe

••

180-

••

160-

....e 140 ,..
:l

....
u 120
....e 100 lQ

a
>

W

BO I-

•
•

60 ....
40 I-

....

250

Needle Probe
30 In/Illin

§ 200

B

....C

•

•

III

>
w

•
•

....• • •

•

II

..

300

008 in/Illin
D 16 in/Illin,. 30 1n/lllin• 30 in/Illin,. .. ·1.....
..

• • • ••••

Needle Probe••....•......•....; .. ·1

16 lnllllin

20 I-

00

I

I

,

I

I

20

40

60

BO

100

Average grlt slze

120

(um)

Fig. 4 Average event count vs. average mineral particle diameter (bar probe)

OL.-......J..IL.-......ILlL...-....ILlL...-..JL.IL...-....._...I...I._......._,I."I,I,_........----II

300,....-----------~~"._-=-.....,..---____..

220
200

"'"

•

120 I-•....e 140 l:lQu....e 100 lu>w

BO I60 ,..
40 "-

•

•

•

§ 200
CI

u

....C
.t
50

•

•• •

...

08 lnlaln

....

III

OL.-......I..lL....-........L...-....I..l

• •

• •
•
•

. •,. •,.

,.,,; , ..... '. 'I. . . ·1 ..

20 I-

00

250

008 in/Illin
D 16 In/Ilin,. 30 in/min• 30 in/Illin"'"lBO I160

Needle Probe

Needle Probe........._..LL..oI_..LLL----J

Displacement•••

Fig. 6 Event count data for needle probe.

..............

1

I

1

I

1-

20

~

~

00

~

Average grit 81ze....J..jL....-..&..I_......._The rms voltage is plotted in Fig. 7. Unlilce the event
COUDt parameter. the voltage increases as the speed increases
for the three probes used. This occurs since the intensity of
impact increases with increasing speed. While fewer events are
generated at higher speed. the change ill momentum is higher
resulting ill an increase in the rms value. As for the data trend,
the average rms incn:ases up to a particle diameter size of S2
J.LDl and then slightly decreases as the puticle size increases.
This occurs because as the interVal between events increases.
the increased signal intalsity due to the grit size increase stops
overriding the decrease ill the number of impacts aroUDd a grit
size of SO pm. The rms voltage is not sensitive to changes inthreshold level, UD1ike the event counL The rIDS voltages fromthe needle and bar probes have a higher sensitivity than the rod
probe, similar to the trend in the event COUDt data.

~

(UID)

Fig. S Average event count vs. average mineral particle diameter (needle probe)
The third trend is that increasing the travel speed resuUs ina decrease in the event COUDL This caD be attributed to the factthat at higher speeds less contact occurs with the grit due to the
dynamics of the probe. The doUCd curve for the needle and theedge probe data illustrates the effect of changing the thresholdfrom 0.5 to 1.5 V for the 12.7 mmlS nan. The noise level isreduced in this case and a smoother relationship is obtained.
However. the threshold level should not be set very high sincein that case very few events are dereeted resulting in a reduction
in sensitivity. A signal-dependent threshold level similar tothat used by Pan and Domfeld (1985) will eliminate the problem of specifyiDg the threshold level. Figure 6 shows plots ofthe event count for the three different speeds. The figure shows
the variation in this parameter value, which is similarly exhibited by the other parameters considered, and when using otherprobes. Due to these fluctuation, the measured parameters caD
be related to surface geometry only ill an average fashion. In
what follows. only data plots for the needle probe will be
ShOWD due to space considerations.

The mean peak amplitude parameter is ploued ill Fig. 8.
The mean peak amplitude increases up to a particle diametersize of S2 JUD, and then either slowly increases or remain flat.
This parameter shows a simDar trend to the rms voltage for low
puticle sizes. The mean peak amplitude does not depend on ameasurement time interval and therefore does not decrease as
the spacing between grits incn:ases. The anomaly in this parameter for the 12.7 mmls run is due to the decreased sensitivity
at the 0.5 V threshold level. A smoother and less variable trend
is obtained when the threshold is set to 1.5 V as shOWD in the
doued curve in the figure. While fewer events are generated at
higher speeds. the signal amplilUde is higher at higher speeds
fJ1

2.5

-

•
or. "

J:

1.5-

~

...

"

"

2.0 -

-

"

•

•

•

0

1.0

~

"

•

"

•

••
•
•

3. The event count is the AE parameter most sensitive to variation in grit sizes. However, it is threshold dependent The rms
voltage aud the mean peak amplitude per event are sensitive forlow particle size diameters. Since all the parameters show anon-negligible fluctuation, AE can only be used to indicate
properties averaged over a length of the sample.

.,

•

I

•

• • •

• •

UI
%

more sensitive to surface interactions thau a more rigid probewith wider area such as the rod.

••

4. Increasing the speed results in a decrease in the event countbecause less contact is occurriDg with the grits due to the
dynamics of the probe. It also results in an increase in Ihe rms
voltage due to a net increase in momentum transfer, and auincrease in the mean peak amplitude due to au increase in theintensity of impacts.

Needle Probe

0.5~

o 08 In/lDln
D 16 In/1l1n
II~ 30 In/,lnI .
°0~--:2t:.0:----f.40:----::6=O-~B~O:----:-:10~O----:-:!120

Average grit size lum)

S. Increasing the grit size results in a smooth decrease in theevent count since less contact with asperities is occurring perunit length. There is a limited increase and then a gradualdecrease in the rms voltage since the increased signal intensity
does not override the increase in the time interval between
events aDd a gradual increase in the mean peak amplitude occurs
since the intensity of impact increases.

Fig. 7 Average rms voltage VI. mineral particle size (needle
probe).

70r-------------------,
iii 65~~ 60-.......

:Il } 55-i!iSO-......

....

: "

.,.

e

l

•

.- c·

..

If

"

•
.,
••••

-

.,

••

..

'O

••

.,
••

.

For future wort. as part of developing a general relationship between surface geometry and AE signal characteristics, a
different characterization of coated abrasives is needed for more
descriptive representation of their geometrical features. Also,
it would be of interest to study sliding motion for general surfaces other than coated abrasives.

.,
••

.,

•

Needle Probe•o 08 In/mln
D 16 In/mln" 30 In/mln• 30 In/mln

c

IV

~ 45I

I

I

I

Acknowledgement
Thanks to Dr. E. Duwell of 3M Company for providing
information about coated abrasives, to Kevin Strauss for his
helpful discussions on designing the experimental setup, andto T. Breiner for providing some of the aualysis programs.

I

400~--:2t:.0:----f.40:----::6=O-~B~O:---1-:-:0~0----:-:!120
Average grit size (um)Fig. 8 Average mean peak amplitude per event vs. mineralparticle size (needle probe).

References
Anon. (1984), "American National Standard for Grading of
Certain Abrasive Grain on Coated Abrasive Material", ANSIIASC B74.18.

due to the increased intensity of impact resulting in a higher
mean peak amplitude.

7. Conclusions

M. Culkosky aDd R. Howe (1988), "Dynamic Tactile Sensing".
ROMANSY 88: 7th C1SM- lfToMM Symp. on the Theory andPractice of Robots and ManipulatoIs, Udine, Italy.

This paper investigated AE generation mechanism in sliding motion. Using the surface interaction between probes ofdiffereDt geometries and coated abrasives as a source of AE. thestudy investigated the effect of speed. contact geometry, aud
surface grit s1z.es on the AE signal obtained. Since coated abrasives are made of very hard grits (9.6 Mobs) that are rigidly
attached to their backing, some of the conclusions stated below
may not be applicable to soft materials where other modes ofAE generation mechanisms are more predominant The study
conclusions are:H. Clichos (1978), T,ibology, Elsevier, Amsterdam.
D. Dornfeld and C. Haudy (1987), "Slip DeleCtion using
Acoustic Emission Signal Analysis," Proceedings of the 1987
IEEE International Conference on Robotics and Automation,
Raleigh, NC, pp. 1668-1675.
D. Dornfeld and H. Cai (1984), A An investigation of GriJlding
and Wheel Loading using Acoustic Emission," Trans. ASME,
Journal of Engineering for Industry. 106(1), 28-33.

1. The impact between a probe and asperities of a hard surface isa more dominant source of AE than deformation of asperities.

D. Dornfeld and C. Pan (1985), "Determination of Chip
Forming States using Linear Discriminant Function Technique

2. A flexible probe with small contact area such as the needle is88with Acoustic Emission", 13th NAMRC Cont.. SME, Berkeley,
CA. pp. 299-303.
T. Fleischaker (1986), Analysis of GrineliDg Process Using
Acoustic Emission Signal Analysis, M.s. Report, Departmentof Mechanical Engineering, University of California at
Berkeley.

M. Stedman (1989), "Limits of Surface Measurement by Optical
Probes," Surface MeD.SUTement and ChtJraeterimtion, JCBIl M.
Bennet, eeL, Proc. SPIE 1009, pp. 62-67.

N. Sub (1981), T,ibophys;cs, Prentice-Hall, Englewood Cliffs,
NJ.
Appendix: Background on Coated Abrasives

J. GagIiaIdi, I. Hong and Eo DuweU (1985), "Analysis of Coaled
Abrasive Surfaces with a "broad probe" promometer'", 13thNAMRC Conf., 5MB, pp. 321-325.

J. Hollerbach (1984), "Tactile Sensors and the Ioterpretation ofContact Features", Workshop on Intelligent Robots: Issues andAchievements. SRI IotemalioDa1.MenloPark.CA. November,
pp. 143-152.
C. Jisa (1989), Intelligent Acoustic Emission Sensing in
MachiDiDg for Tool Condition Monitoring, Ph.D. dissertation,
Department of Mechanical EngiDeering, University of California at Berkeley.
C. Maxey (1964), "Measuring Texture and Contact Area of EndWood Surfaces," Materials Research and Standards, 4(6), 279285.
K. ODo, ed. (1979), Fundamentals 01 Acoustic Emission.
UCLA, Los Angeles, CA.

s. Pandit, F. Nassirpour and S. Wu (1977), "Stochastic
Geometry of Anisotropic Random Surfaces with Application to
Coated Abrasives," Trans. ASMB, J. Engineering for Induslry,99, 218-224.

J. Peklenik and M. Kubo (1968),"A Basic Study of a TlueeDimensional Assessment of the Surface Generated in a
Manufacturing Process", Annals of the CIRP, 16, 257-265.

This appendix follows from reference (AnoD.. 1984).
Coated abrasives are multiple point cutting tools that are available in a variety of geomelric configurations. A typical coatedabrasive is produced from three basic raw materiaJs: a minenl,an adhesive "bond", and a bacJciDg. Figure Al shows a crosssection of a typical coated abrasive. The backing serves as thebase upon which a coating of adhesive knoWD as the "mate"coat is uniformly applied to anchor a single layer of mineralparticles. Individual mineral particles are applied to the makecoat and oriented in an electrostatic fashion or with a mechanical process to maximize the probability that a particle will bepositioned with its loog axis petpendicular to the bacJciDg.
After the make coat is cured, a second coat of adhesive, moWD
as the "size" coat, is applied over the surface to further support
the particle and give rigid support 10 the backing. Coated abrasives are commonly available in "closed" ad "open" coatproduct vuiatiOD. The close coated abrasive is one whose bacting is fully covered with mineral particles to maximize thenumber of cutting points available. The open coat CODstlUction
widens the area between particles ad produces a self-cleaning
action during grinding. The weight designation A, C, .., etc.
refers to the weight of a given number of coated abrasive
sheets.

Grit

Slze~

M. Pbadke, F. Burney and S. Wu (1975), "Evaluation of CoatedAbrasive Grain Geometry and Wear via Continuous Time Series
Models," Wear, 31, 29-38.
S. Rangwala, F. Forouhar and D. Dornfeld (1988), "Applicationof Acoustic Emission Sensing to Slip Detection in Robot
Grippers," lot. J. Mach. Tools Manufact., 28(3), 207-215.
R. Sayles and S. Poon (1981), "Surface Topography ad
Rolling Element Vibration", Precision Engineering, 3, 137-

144.
W. Schiff and E. Foulke, eds. (1982), Tactual Perceptio,,: A
Sourcebook, Cambridge University Press, Cambridge.
I. SberriDgtoD and E. Smith (1988a), "Modem Measurement
Techniques in Surface Metrology: Part I; Stylus lnsttuments,
Electron Microscopy and Non-Optical Comparators," Wear,
125(3), 271-288.
l. SberriDgtoD and E. Smith (l988b), "Modem Measurement
Techniques in Surface Metrology: Part U; Optical Measurements," Wear, 125(3), 289-308.

Make Coar""" Backlng
Fig. Al Cross section of a typical coated abrasive.

AEWG 36th MeetiDg

cracks in engineering materials and structures. Although it is an excellent technique for detecting and locatingcracks. it has not been an effective method to use for objectively determining the amount of crack growth occUlling.
This is due to the "color" introduced by plate-like structures on the transient elastic waves and the "colol" introducedby the transducer and electronics. Attenuation as well as dispersion of the stress waves also plays an important rolein changing the characteristics of the AE signal as a function of distance from the source.
This report shows the results of research presently in progress to remove the effeclS of "color" introduced by a structure, transducers. electronics. and attenuation on AE signals from a growing crack during an Acoustic Emission test
in order to estimate the amount of crack area generated during the test. This is accomplished by the use of controlledhydrogen embritdement cracking of a high sttength boll. Acoustic Emission signals generated from aacking of thebolt are injected via waveguide into the structure at a known crack location. Following failure of the bolt, theamount of crack area generated due to crack growth prior to failure of the bolt is measured.. The total number of AE
counts from each channel is recorded for this amount of crack area and is then used as a calibration criteria for eachchannel for estimating the amount of new crack area generated by the known crack in the sttueture. The fixture holding the bolt, as weD as the bolt and waveguide also contribute "color' to the AE signal from the cracking bolt.
Therefore, it is necessary to measure these effects in order to more accurateiy calculate the crack area from the structural crack. The approach being used to measure these characteristics will also be discussed in this report.
Jerry W. Whittaker
Characlerization of the ASL Parameter
Electton beam (EB) welding of uranium produces large amounts of acoustic emission (AE) having a broad range of
amplitudes. The Average Signal Level (ASL) parameler has been used to summarize and quantify such emission. The
parameter's ability to average the effects of numerous signals and its large dynamic range make it particularly useful.
We performed experiments to provide a better understanding of the response of the ASL circuitry to AE produced
under various conditions. The effects of emission type (continuous. burst and mixed). signal amplitude and frequency. and recording inslnUnel1t were evaluated.
Baxter H. Annstrong
Low Sttain Level AE due to Seismic Waves and TidaVIbermoe1astic Sttains Observed at the San Fancisco Presidio
Oh-Yang Kwon and Dong-Jin Yoon
Monitoring ofLubrication Quality in a Simulated Journal Bearing System by AE
A simulated journal bearing system was built and used for developing a rotating machinery diagnosis scheme withacoustic emission monitoring. Since Ihe quality of lubrication is thought to be the most critical condition for sOWld
operation of a journal bearing system ilS acoustic emission behavior has been studied under various lubrication conditions deliberately contaminated with alumina and iron powders. To change lubrication conditions. 0.1 to 0.5% involume of alumina powders whose particle size are 1. 8 and 381J11l were added to the lubricant The minimum clearance between the journal and the bearing was kept as 15 J1D1 by selecling the load and rotating speed..
Results showed that AE activity increased considerably with inaeasing speed of rotation whereas it inaeased moderately with increasing volume fraction of contaminanlS. The erosive action of alumina particles can atCOunt for thisresult. On the other hand, AE activity was virtually DOt affected by increasing panicle size from 1 to 8 J.UD, then was
drastically inaeased with 38 JlJD panicles. This implies that the AE source mechanism is different when the panicle
size is larger than the minimum clearance (15 J.UD). The bearing surface could have been under plowing acdon ofalumina panicles and was found severely worn after about 10 minuleS ofoperation.
The objective of this study is early detection of abnormal operation condidons in rotating machinery with journalbearings. Various paramecers and conditions which affect the AE activity at the boundary layer ofjournal bearings areto be systematically investigated.
Yakichi Higo
The Method for Measurement of Sensitivity Degradation of AE Transducer
Clinton Heiple, S.H. Carpenter and DL. Armentrout
Comments on the Origin of AE in Fatigue Testing of Aluminum Alloys(Continued to page 96)

90

The Role of Acoustic Monitoring as a Diagnostic Tools in
Nuclear Reactors
Sbahla Keyvan and Ron King
Abstract

The pwpose of Ibis paper is:• To address the role of AE monitoring as a diagnostic toolfor nuclear plant sW'Veillance.
• To identify cmrent areas of application of AE for diagnostics in nuclear power plants.
• To identify current and potential areas of application in
LMRsIEBR-n.
• To provide information to encourage acceptance of thetechnique by evaluation of its application at LWRs.
A representative sample of thirteen nuclear plants (LWRs) wereselected on the basis of:• Plant type (boiling water reactors or BWR and pressurized water reactors or PWR).
• Commercial operation.
• Geographical location.

This paper presents the results of a sW'Vey on acoustic
monitoring applications in light water reactors and an assessment of application of acoustic monitoring technology inExperimental Breeder Reactor n (EBR-n), the only power producing liquid metal fast breeder reactor in the United States. The
main objective of this paper was to obtain information on various applications of acoustic techniques in light water reactors(both boiling water reactors and pressurized water reactors) andto assess applications for EBR·n from it's past experiences.

1. Introduction
Among the phenomena found usable in detection of incipient equipment failure are the acoustic emission (AE) phenomenon. and the resonant frequency phenomenon. The acoustic emission phenomenon is most applicable to material faults.
The resonant frequency phenomenon applies to rotating machinery, since the relative motion rather than material itself isthe source of the sound. In rotational equipment, such as pumps
or generators, a form of acoustic energy exists when a failme
mechanism is in process or has created a very small defect. Thisis the resonance frequency phenomenon and is created by shock
excitation of a rotational mass spring over a defect, as a ball ina ball bearing rolling over a spall. All components of themechanical equipment resonate when shock excited, thus creating a wide range of frequencies.

Table 1 gives the list and brief information about these
nuclear power plants. This assessment is primarily based on
the information directly provided by each plant on the subjectof acoustic monitoring applications (Keyvan, 1989). Applications of acoustic technology in nuclear reactors for surveillance and diagnostics are increasing. Currently. this technique
is utilized in the following areas:• Loose parts monitoring.
• Leak detection monitoring.
• Valve position monitoring.
• Sodium boiling detection (in LMRs).
Table 2 provides information on the cmrent areas where acoustic techniques are utilized. and the task or function of the corresponding monitoring system in the LWRs in the United States.

In the decade of the 1960's, engineers became interested inutilization of the AE phenomenon in studies under the categoryof nondestructive testing (NOT). AE is defmed as transient elastic wave generated from release of energy in material; e.g., as aresult of plastic deformation. AE sensors respond to pressme
waves over a wide range of frequencies. Hence any events creating detectable pressme waves such as pressme boundary leaks,
or loose parts can also be detected by AE sensors, thus broadening the application of AE techniques. This paper presents anevaluation of acoustic monitoring applications in light water
reactors (LWRs) and liquid metal reactors (LMRs), and specifically addresses applications of acoustic monitoring technology in Experimental Breeder Reactor n (EBR-D), a liquid metal
reactor.

1.1 Loose Parts Monitoring in Lighl Water Reactors
According to the US Nuclear Regulatory Commission(NRC) Regulatory Guide 1.133, any reactor licensed since 1978
must have systems to detect loose parts and components withinreactor vessels and primary coolant systems. Early detection ofloose parts in LWRs is important from a safety point of view,
since the loose parts could either be pan of a safety related system or could damage a safety related system by their interference with the reactor coolant system (RCS). In addition, early
detection of loose parts can prevent extended outages, reduce
downtime for repair of damage caused by undetected parts, andcould provide an indication of degradation of reactor internals.

Received 18 June 1991. S. Keyvan is affiliated with Dcpanment of
Nuclear Engineering, School of Mines and Metallurgy, University ofMissouri. 222 Fulton Hall, Rolla, MO 65401-4748 and R. King is willt
Argonne National Laboratory, Argonne West, EBR-n Division, P.O. Box
2528, Idaho Falls, m 83403-2528. This worle was supported by the U.S.
Depanment of Energy, Reactor Systems Development and Technology,
under Contract W-31·1Q9·ENG·38.

Joumal of Acoustic Emission
Volume 10, Numbcr3/4

91

Loose Pan Monitoring Systems (LPMS) are applied. toboth PWRs and BWRs. However. PWRs have experienced moredamage due to loose parts than BWRs. Although LPMS are similar for both BWRs and PWRs, requirements for the number of
sensors and sensor locations are different in these two types of
LWRs. Background noise in a BWR is also different from a
PWR because of BWR's relative low flow in the lower reactor
vessel, steam flow noise in the upper vessel, and the recircula073().(l()S0I92l1 0091.Q9S
Copyrightel992 Acoustic Emission GroupTABLE 1 List of LWRs in the Survey of Acoustic MonilOring Program.

lYPE

PLANrNAMECOMMERCIALOPERATION

Philadelphia Electric Co.
Philadelphia Electric Co.
Gulf State Utilities Co.
System Energy Resources. Inc.
Carolina Power & Light Co.
Toledo Edison Co.
Louisiana Power & Light Co.
Tennessee Valley Authority
Arkansas Power & Light Co.

Peach Bottom 2 & 3
Limerick 1 & 2River Bend 1
Grand Gulf 1
Brunswick 1 & 2
Davis-Besse 1
Waterford 3
WausBarAN01&2

1065
1055
936
1250
790
866
1104
1177
836. 858

BWR
BWR
BWR
BWR
BWR
PWR

PWR
PWR
PWR

1974
1986. 1989
1986
1985
1971. 1975
1977
1985
Indefinite

1974. 1980

Table 2 Areas of utIlization of AE technique and its fimction.

AEAPPUCATION
Steam relieve valve position monitoring
Continuous vibration monitoring
Steam leak detection
Check valve testing
Portable AE system
Loose part monitoring system
Sodium boiling detection
Transfonner partial discharge detection
Drywell valve packing leak detection
Safety relief valve pilot leakage detection

tion flow noise in the middle region of the vessel. PWRs
appear to have a higher operating noise level than BWRs.
Although LPMS are generally used to detect structureborne sound resulting from the impact of loose parts. BWR's
experiences indicate that they can also be used as a means formonitoring of internal structural integrity. LPMS in BWRs
have been able to detect problems other than loose parts. suchas valve mechanical problems. steam dryer problems and metal
impact signals associated with cracked sparger brackets (Mayoet al.. 1988).
Proper loose part monitoring in PWRs requires a minimumof six sensors in the reactor vessel: three for upper vessel head;
three for lower vessel head; and seven sensors for monitoring
the steam generator. A U-tube steam generator requires a mini·
mum of two sensors in the primary inlet region. One sensor isnecessary on the primary coolant outlet, one near the top of lhe
tube bundle. two sensors at the upper tube sheet, and one sensor
at the lower tube sheet.
Proper loose part monitoring in BWRs requires a minimumof twelve sensors in the reactor vessel distributed three each atlower vessel head. core support plate, feedwater inlet, andsteam outlet. To install the sensors on the surface of a component, four different mounting methods can be used. The best is
the drill-and-tap method. A strapped or clamped sensor is also
acceptable. The third choice is magnetic sensor mounting withepoxy. and the last choice would be magnetic attachment. After

FUNcnON/fASK
Monitoring for open valves
Monitoring critical rotating equipment
Monitoring valves for seat leakage
Monitoring of check valves to detennine valve condition
Detection of valve leaks
Detection of loose parts in the reactor
Detection of boiling in sodium
Monitoring transformers for internal corona
Detection and identification of leaking valves
Monitoring second stage pilot valve for leakage

installation. sensors must be checked (without removal) for
system response verification. Two methods of impact calibration are recommended (Mayo et al.• 1988); namely. pendulum
impact, and an insttumented hammer. Each method has its own
advantages. The pendulum impact involves simple data acquisition and analysis; while the insttumented hammer requires
fewer tests.
Loose part signal characteristics must be used in order tominimize false alarms and responses to events with sources
other than loose parts. Alarm set points are typically set about
five times the background noise level (Mayo et al.. 1988).
Bandpass filters are also necessary to reject low frequency
noise associated with flow and pump noise. and high frequency
sensor resonance response. Typical loose part signal characteristics are determined from relationships between amplitude andfrequency as a function of mass. energy. distance and shape ofloose parts. In addition, relationships between parameters suchas wave velocity. frequency and thickness of the transmitting
struClUre can be used to validate a loose part related signal.

1.2 Acoustic Lealc Monitoring System (ALMS)
The physical principle behind acoustic leak detection isthat fluid flowing out of a component, (i.e.• a pipe) generates
high frequency continuous noise. The frequency depends on thesize of the leak. and the fluid pressure. Generally. the smaller
the leak size or the higher the pressure. the higher the frequency. A unique characteristic associated with leaks is that aleak-related signal is a continuous signal. In detection of leakage via the AB technique, signals are fIrSt amplified and filtered
to discriminate against the environmental noise, and then therms voltage of the signal (which is the most applicable method
for continuous signal) is measured. Basically, there are two
types of leakage: one associated with components susceptible
to leakage such as pump seal and valves: the other is leakage
associated with a pipe crack or break. Successful application of
the AE technique for detection of a pipe crack or break depends
on the information and knowledge of the characteristics of leakand background noise, attenuation characteristics of the leaknoise with distance, as well as the relationship between AE
noise and leak rate. Generally. the acoustic level (rms voltage)increases as the leak increases until the point where the differential pressure across the leakage point decreases, which causes
the acoustic level to drop.

Leak detection in LMR is another active area in application of acoustic teclmology. The acoustic monitoring programin Super Phenix (a French liquid metal fast breeder reactor) involves water leak detection in the steam generator. The principle of the acoustic leak detection in the steam generator isbased on the generation of acoustic waves from the following
phenomena:• Vibration of the supporting structure.
• Stress wave generation due to rapid temperature increaseof surrounding structure.
• Generation of pressure wave in sodium due to bubbling ofwater and gaseous product (hydrogen) from water-sodium
reaction and due to turbulence of the jet.

These phenomena are the secondary effect arising from thefailure of water circuit tightness within a steam generator causing water (or steam) to be injected into the surrounding sodium.
These leak-related acoustic waves propagate to the external
shell of the steam generator where they are detected by thetransducers.

Performance of a leak detection system are evaluated basedon the following three criteria: (1) sensitivity, (2) location and(3) quantification. None of the presently acceptable leak detection methods qualify in all three criteria. Acoustic methods
have good sensitivity and ability to determine leak locations,and moderate quantification capability. AE leak detection capabilities are comparable to other leak detection methods prescribed in national standards (McElroy. 1982).

In addition, an acoustic technique was used to detect thesodium leak in the "barillet" of Super Phenix. The barillet is alarge double-walled cylindrical sodium tank located close to theprimary tank. It is used for transfer of fresh fuel to the core andtemporary storage of spent fuel for decay. Among the several
methods used (i.e.• helium detection. 133Xe (xenon) detection
method, thermography and an acoustic technique), the acoustic
technique was a successful method and provided the earliest
indication of the leak location.

2. Acoustic Emission Technology for LMRs
This section provides a review of the areas of applicationof acoustic techniques in LMRs in foreign countries. The advantages and difficulties in applying the acoustic techniques ineach area is also discussed. General areas of monitor ing via
acoustic techniques in LMRs consist of:• Sodium boiling detection.
• Leak detection.
• Cavitation detection.
• Loose part detection.

Another application of the acoustic teclmology in French
reactors is the use of a pattern recognition instrument for monitoring acoustic signals in the Phenix reactor core. Phenix is
another smaller French LMR. This pattern recognition instrument utilizes "fuzzy-logic" classification and a self-training
procedure, and is based on acoustic signals measured in the coreof the Phenix reactor. The instrument is also used as a test
bench for further applications such as acoustic surveillance ofthe steam generators of the Super Phenix. Successful application of this tool to detect a known anomaly, i.e., the incipience of cavitation, has confmned the sensitivity of the instrument.

In the area of application of acoustic techniques in detection of boiling in LMRs. major efforts have been undertaken in
the past decade. In sodium boiling. the collapse of vapor bubbles is the source of acoustic noise. which in highly subcooled
conditions gives rise to sharp high-amplitude pressure pulses
and generates noise with frequency components extending wellbeyond 100 kHz (Burton, 1977). Detection of this noise requires: .
• Discrimination against background noise.
• Knowledge of the transmission of acoustic waves in reactor pools, structures and effects of gas bubbles.

3. Application of AE Technique in EBR-I1
EBR-II is a US Department of Energy liquid metal fast
breeder power generating reactor. The reactor started operationin August, 1964. EBR-II has a full power output capacity of
62.5 MWt • Reactor coolant is liquid sodium. ERR-II is a pooltype reactor with the core, reactor vessel. and primary systemcomponents all submerged in 86.000 gallons of sodium at
371"C which are contained in the primary tank (see Fig. 1).

An acoustic signal from boiling is highly impulsive.
Hence, to take advantage of this characteristic. the detection
method can consist of either detecting the impulses in the time
domain, or detecting a high frequency signals in the frequency
domain. Achieving high sensitivity and early warning requires
sophisticated data processing. although simple signal processing is sufficient to provide trip protection.

Acoustic monitoring teclmiques have been applied to
EBR-II as part of the acoustic surveillance program and acoustic
monitor development program for the pUIpose of:• Diagnosis of primary sodium pump anomaly• Development of correlations between mechanical (control rod motion) and neutron noise.
• Noise investigation within the Intermediate Heat
Exchanger (IHX).

Two major advantages in boiling detection via acoustic
techniques are: 1) a single detector can monitor a large volume
of core due to acoustic signal Iransmission in the medium. and2) there is no need to mount a detector within the fuel elemenL
93

j I

;:1
I

j

SUllASSEIIBLYRANSFER COFFIN

IIlTEIlIIEOIATEIlEAT EXClWiGER

PRIIlARY TAIIKREACTOR_----7"

PRIllARY SOOIUll PIIMP III

BLAST SHIELD.......---'3-=1Ioi-1i-..r::1I-f-- SAFETY ROGS III

AIR BAFFLE TANK

Fig. 1 Schematic diagram of Experimental Breeder Reactor n (EBR-n).
primary tank beside lite fuel handling mechanism, 2.6 m fromlite tank centerline. equispaced between lite two primary pumps
and diametrically opposite lite IHX. The microphone andwaveguide were inside a perforated basket (60% open area) at
the boaom of the 3.3 m long shield plug. The main objectives
for installation of dtis equipment were to test long-term performance of high-temperature acoustic devices in sodium. todevelop correlations with other signals, and to obtain information on acoustic background noise as a function of sodium flowrate and reactor power level.

The acoustic monitoring program at EBR-II also included
lite vibration signature analysis from accelerometers mounted
on plant components such as primary pumps and sensors onlite IHX. The vibration signature analysis of pump No. 1(which had experienced a disturbance on September 29, 1970)
indicated a variation in vibrations at harmonics of lite pumprotational speed. Subsequent disassembly of lite pump revealed
an uneven build up of sodium oxide on lite pump shaft causing
lite shaft to bind (Anderson et al., 1972).
Anollter experience willt lite application of an acoustic
technique in EBR-II relates to lite incident involving lite loudbanging noise heard on lite night of November 14, 1970 in lite
vicinity of the IHX. Noise signature analysis was utilized toconfirm lItat: a) an evacuation tube was a likely suspect, b) lite
noise source was eliminat ed .after complete removal of lite tube
(Anderson et al., 1972).
.

The result of tests with this equipment showed that significant information can be obtained from the high temperature
acoustic devices, particularly in assessing the relative contribution of primary flow noise and reactor power noise to total
background noise (Anderson et aI., 1974).
The G-I plug probe has recently been upgraded. The
waveguide and the microphone are replaced willt dtree new
microphones for monitoring lite fuel handling system.

Additional acoustic monitoring capability was added to
EBR-n in 1973 by installing and utilizing a microphone and an
acoustic waveguide located in lite acoustic monitor shield plugat nozzle G-I in Ihe primary tank cover. The position of Ihese
two detectors was about 1/3 m below lite sodium surface in the

The upper plenum acoustic emission experiment was performed in 1976-1977. The main purpose of this experiment94was to obtain information on the acoustic background noise in
the upper plenum region. The result of this experiment indicated lhat the acoustic data were stationuy in the broad sense(or weakly stationary). It was concluded lhat the high frequency
acoustic noise source was the global flow noise produced by thefuel subassemblies and local hydrodynamic effects. The low
frequency noise data were attributed mainly to vibro-acoustic
effects (Carey et aI., 1977).

W.M. Carey, A.P. Gavin, J.P. Bobis, S.H. Sheen, T.T.
Anderson, R.D. Doolittle and R.W. Albrecht (1977), "The
Detection of Sodium Vapor Bubble Collapse in a Liquid Metal
Fast Breeder Reactor"; Progress in Nuclear Energy, vol. I,
Pergamon Press, London. pp. 437-468.
P. Jax and V. Streicher (1989), "AE Application and Recent
Results in Nuclear Components", J. Acoustic Emission,8(1/2), S53-S56.

Another acoustic experiment at EBR-n was performed forthe purpose of obtaining acoustic background noise (Anderson
et aI., 1912). The sensor locations used for this experimentwere: Three reactor cover hold downs; the top of the valve stemin a primary system throttle valve; and the upper motor flangeof each primary pump system. The result of this test also indicated a flow dependent background noise. In addition, tests
were performed on the core component test loop (CCTL) to perform an independent check on the flow dependency of the
background noise at EBR-D. The CCTL reduced flow tests indicated flow dependent background noise similar to the accelerometers signals in the EBR-D (Anderson et aI., 1972).
4,

S, Keyvan (1989), "Survey oC Acoustic Monitoring Applications in Nuclear Reactors and Assessment of Applications Cor
EBR-n", Enterprise Energy & Research. ANL Report, EER-8902, July.
G. LeGuillou, R. Berger and M. Brunet (1977), "Boiling
Detection in Fast Reactors by Noise Analysis Studies
PerCormed in France", Progress in Nuclear Energy, vol. I,
Pergamon Press, London. pp. 409-426.
1.D. MacLeod, E. Catling and C.G. Taylor (1977), "Acoustic
Detection of Boiling in LMFBRs: An Estimate of Sensitivity
Derived From Experiments During Ibe Commissioning of
PFR", Progress in Nuclear Energy, vol. I, Pergamon Press,
London, pp. 469-485.

Conclusion
The utilization of AE technology for diagnostics in light

Waler reactors in the US has been supported by NRC in the areaof loose part monitoring. Vessel integrity testing in LWRs,
however, is not performed by an acoustic technique, rather via
techniques specified in ASME code and approved by NRC. It
should also be pointed out that allhough Ibe areas of application of acoustic monitoring cover quite a wide range, not every
power plant is utilizing this technique in all of Ibe areas specified in Table 2. This clearly suggests that AE technology is not
utilized to its fullest potential. Some plants apply Ibis technique based on the NRC requirement, hence only Cor loose partmonitoring. ThereCore, not all utilities are benefiting from thefull potential and capabilities oC this diagnostic 1001. The successful application and tests oC AE technology in LMRs alsosuggests that it will be put to use for diagnostics in next generation advanced reactors.

J.W. McElroy (1982), "Acoustic Emission Leak Detection
Methods for Light Water Reactor Application", Philadelphia
Electric Company, DOE Report COO-35208.3, August.
C.W. Mayo, D.P. Bozarth, G.N. Lagerberg and C.L. Mason(1988), "Loose-parts Monitoring System Improvements",
Electric Power Research Institute, EPRI NP-5743, March.
H.B. Patel and A.W. Cook (1989), "Acoustic Emission Leak
Monitoring in Pressurized Piping", J. Acoustic Emission,8(1/2), S101·S104.

References

C. Sklarczyk and E. Waschkies (1989), "Detectability oC
Defects in Reactor Pressure Components by Location andInterpretation of AE Sources", J. Acoustic Emission, 8(1/2),
S93-896.

T.T. Anderson. A.P. Gavin and J.P. Bobis (1974), unpublishedinformation. Argonne National Laboratory.

J.A. Thie (1982), "The Third Specialists Meeting on Reactor
Noise", Nuclear Safety, 13(5),T.T. Anderson. A.P. Gavin. J.R. Karvinen and KJ. Reimann(1972), "Detecting Acoustic Emission in Large Metal Cooled
Fast Breeder Reactors", Acoustic Emission, ASTM STP 50S,
American Society for Testing and Materials, Philadelphia. pp.
250-269.

D. Williams (1984), "Acoustic Emission Testing of Reactor
Coolant Pump Motor Flywheels", Power Engineering, Pemwell Publishing Co., October.

H.L. Balderston (1972), "The Broad Range Detection oC
Incipient Failure Using the Acoustic Emission Phenomena",
Acoustic Emission, ASTM STP 505, American Society for
Testing and Materials, Philadelphia, pp. 297-317.
EJ. Burton, P.G. Bentley, tD. Macleod and J.A. McKnight(1977), ''The Development of Techniques for the Surveillanceof LMFBR", Progress in Nuclear Energy, vol. I, Pergamon
Press, London, pp. 393-408.

95

AEWG 36th Meeting
The size of acoustic emission (AE) signals expected from inclusion fracture during fatigue testing of7075 aluminumhas been estimated on the basis ofprevious measurements of AE produced by the fracture of boron particles ~rated into 2219 aluminum. The AE signal size expected from deformation in the plastic zone ahead of the fatigue
crack was estimated from the results of tensile tests on 7005 aluminum. The signals predicted from both processes
are near or below the noise level in the fatigue experiments and are therefore far too small to account for the signals
actually observed. Nearly simultaneous fracture of multiple inclusions could produce signals as large as thoseobserved in fatigue tests of 7075 aluminWll, however, fatigue tests of 7050 aluminum produced signals as large orlarger than in 7005. Since 7050 has substantially fewer inclusions than 7005, the simultaneous failure of multiple
inclusions is unlikely to be a major AE source in fatigue testing of either aluminum alloy. Thus, the most probablesouree of acoustic emission during fatigue testing of 7005 and 7050 aluminum is the crack advance itself. The measured crack advance per cycle is large enough to release sufficient elastic energy to account for the AE signals
observed.
William H. Prosser and M.R. Gonnan

Plate Mode Acoustic Emission Signals Produced by Impact
Previous research has demonsttated that acoustic emission signals propagate as the extensional and flexural platemodes in thin plates. In this research, plate mode signals generated by impact sources on aluminum plates are presented. The effect of variations in impactor velocity on the ampliblde and frequency content of the plate mode signals
is demonsttated. Theoretical predictions of the flexural mode displacement based on a normal mode solution to theclassical plate theory equations of motion have been made. The input forcing functions for the theoretical calculations were varied to simulate variations in impactor velocity. Qualitative agreement with the experimental signals
was obtained.
Qixin Huang and Kanji Ono
AE Characterization of CarbonlEpoxy Quasi-isotropic Composites with Pattern Recognition
The structure failure of carbon fiber reinforced epoxy composites is a complicated process consisting several micro
failure mechanisms, especially, certain areas within a struCb!re system will develop local instabilities long before thestructure fails. Besides, the structure made of such composite may be subject to catastrophic failure without any
apparent indication. Therefore, to ensure the structural reliability and to reveal its failure characteristics are majortasks for practical applications of such composites. Fiber fracmre, matrix cracking, splitting (matrix cracking along
fibers) and delamination are common micro-fracture modes occmred in the failure process of the composite. Thesemicro-fractures are typical acoustic emission (AE) sources in composites, and can be detected by AE detecting
systems.
Characteristics ofAE signals generated in the failure process of quasi-isotropic carbon fiber reinforced epoxy composites are investigated by pattern recognition analysis in this sbldy. The AE signals are recorded in the fann of digitizeddata and analyzed by employing a trained K-nearest neighbor classifier which can classify signal types of unknown
waveforms. The identified signal types are then correlated to different failure modes so that the entire failure process
can be more precisely described by distributions of various signal types or failure modes in the loading stress range.
Features of AE signals from the failure process of a ±45° angle-ply and simulated plate waves propagating in thespecimen were also investigated to help discriminate feab1reS of various signal types and correlate identified signal
types to sources.
S.H. Carpenter, C.R. Heiple, DL. Armentrout, F.M. KusblS and J's. Schartzberg
Acoustic Emission Produced by Sliding Friction and its Relationship to AE from Machining
Acoustic emission (AE) was measured during block-on-ring sliding friction tests as a function ofvarious experimental variables. A major goal of the investigation was to determine if the characteristics of AE generated by friction
correspond to those of AE produced by single-point machining. An excellent correlation was observed between AE
and friction force at constant sliding speed for both general features and detailed behavior. The AE in friction tests
increased sharply with sliding speed and only slightly with increasing load. This result corresponds to the observation in machining that AE increases strongly with cutting speed and is usually only modesdy dependent on feed
speed and depth of CUL Furthermore, AE in friction testing was much lower for a low-friction couple; corresponding
to low AE when machining low-friction materials. Thus, the friction test results support the hypothesis that(Continued to page 102)
96

Acoustic Emission Produced by Sliding Friction and its
Relationship to AE from Machining
S. H. Carpenter, c. R. Heiple, D. L. Armentrout, F. M. Kustas
and J. S. Schwartzberg
Abstract

data clearly indicates a close correlation between AE and friction force and establishes that rubbing friction is the major
source of AE in the friction tests. The results also suppon the
hypothesis that rubbing friction is generally the primarysource of AE in single-point machining.

Acoustic emission (AE) was measured during block-onring sliding friction tests 10 determine if the characceristics ofAE generaced by friction correspond 10 those of AS produced bysingle-point machining. An excellent correlation was observed
between AE and friction force at c:onstant sliding speed for bothgeneral feanues and detailed behavior. The AE in friction tests
increased shaJply with sliding speed and only slightly withincreasing load. 111is result corresponds 10 the observation inmachining that AE increases sttongly wilh cutting speed and isusually only modestly dependent on feed speed and depth of CUL
Funhermore. AE in friction testing was much lower for a lowfriction couple; corresponding 10 low AE when machining lowfriction macerials. Thus. the friction test results support Ihe
hypothesis that rubbing friction is generally the primarysource of acoustic emission during single-point machining.

2. Experimental
A commercial block-on-rotating ring friction testing machine (Favie1le Le Valley. model LFW 1) was used during the investigation. All of the friction tests were performed dry without
any type of lubrication. Blocks with a face width of 6.35 mm
were placed in contact wilh a rotating ring of face width 8.9 mm
and radius 17.s mm. The normal force between the block andthe ring. as well as Ihe angular speed (or sliding speed) of thering could be easily changed over a wide range of values. An
additional and convenient feature of Ihe testing apparatus used
was the ability 10 continuously monilOr Ihe friction force andAE during the entire tesL

1. Introduction

The AE was detected with a commercial resonant piezoelecmc sensor (Physical Acoustics. model ~O) which has a reso·
nant frequency of approximately 170 kHz. The sensor was
mO\D1ted on the housing holding the test block. The outpUt of
the sensor was amplified 40 dB by a Panamemcs model SOSOB
preamplifier. The output of the preamplifier was then fed into a
Hewlett Packard 3400A rms voltmeter. The measured value ofIhe rms voltage was used 10 characterize Ihe AE. The output ofIhe rms voltmeter was coMected to both a smp chart and a digital ttansient recorder. The analog output for the friction force
was also fed into Ihe transient recorder. The ttansient recorderprovided an accurate measurement and correlation of the AE and
Ihe friction force. It was set 10 measure both Ihe rms voltage
and Ihe friction force every 50 Ms. yielding a 410-s record for a
8.192-point ttace. The data were Ihen saved on a hard disk and a
new collection started. Values plotted subsequently for friction
force and tins voltage are an average of many individual points
measured by the ttansient recorder. All of Ihe AE rms voltage
values have been corrected for noise and are referenced 10 Ihe
output of the transducer. A schematic diagram of Ihe experimental arrangement used is provided in Fig. 1.

Heiple et al. (1991) have recently measured acoustic emission (AE) produced during single-point machining of a variety
of macerials. These measurements were undertaken as part of aprogram 10 evaluate AE for monilOring 1001 wear. The results
indicated Ihat the major source of AE during single-point machining is rubbing friction between Ihe nose/flank of Ihe 1001
and the freshly machined surface. This hypothesis is consistent
wilh the results of a limited number of measurements of AE
generated during friction testing (Jiaa and Dornfeld, 1990; Kita
et al.• 1980) in situations where large numbers of wear particles
were not likely 10 be presenL
Acoustic emission measurements were \D1dertaken during
additional sliding friction tests 10 decermine if the characteristics of AE generated by friction correspond 10 Ihose of AE pr0duced by single-point machining. Both AE and friction force
were monilOred as a function of applied load and sliding speed.
Friction testing used a block resting on a rotating ring in orderto minimize the buildup of wear particles during Ihe tesL The
presence of wear particles, as occurs in standard pin-on-disk
tests, is known to greatly increase the level of AE (Jiaa andDornfeld, 1990).

The primary materials tested were a carbide block on eilher
a 440C steel or a 6061·T6 aluminum alloy ring. The carbide
block was chosen 10 simulate a carbide cuning tool. The rings
used, 440e steel and 6061-T6 aluminum. are similar 10 maceri·
als used in previous single-point machining experiments(Heiple et al.• 1991). An additional experiment was also carriedout using a 01 1001 steel block with a diamond like carbon(Ole) ftlm against a 440C steel ring. Two basic types of experiments were carried out: (1) a constant load was maintained
while the sliding speed of Ihe ring was increased in a step-wise

The macerial combinations investigated were chosen to be
similar 10 metal combinations used in previous single-point
machining experiments (Heiple et a1.. 1991). Analysis of theReceived 30 MardI 1992. S.H. Carpenter, c.R. Heiple and D.L. Annentrout are affilialed with Plutonium Technology. EG&G Rocky Flats, P.O.
Box 464, Golden, CO 80402 and F.M. Kusw and JS. Schwartzberg arewith Martin Marica.a Corp.• Astronautics Group, Denver. CO 80201.

Journal of Acoustic Emission
Volume 10. Number 3/4

g'f

0730-OOS0192l1 0097-101
Copyrigln@1992AcousticEmission Grouptransducer

800
40 dB preamp
20-1.000 kHz

D
•

nns
voltmeter

o

'it
c

DIAMOND FILM. STEELALUMINUM· CARBIDESTEEL ·CARBJDE

0

zi

~0

strip
chart

transientrecorder

400

C
0

i

if
0

Fig. 1 Acoustic emission measuring system.

200

100

0

Applied load (Newtons)
Fig. 3 Friction force versus applied load at a constant sliding
speed of 0.18 mls.

800
FRICTION FORCE

'it
c:

I~

o STEEL. CARBIDE• ALUMINUM· CARBIDE

400

J!
c
0

80

120

160

200
nme (sec)

240

0

i
1:

280

""

Fig. 2 Example of variation with time of friction force and AE
resulting from sliding friction between me carbide block and440C steel ring as the load is increased in a slepwise fashion.

0

0
0.00

0

• • •

0

• •

0.25
Sliding speed (mls)•
0.50

Fig. 4 Friction force versus sliding speed at constant applied
load. (Steel-carbide load 44.5 N, aluminum-carbide load 11.1 N)

manner, and (2) a constant sliding speed was maintained Whilethe load was increased in a step-wise manner.

The AE rms voltage increased markedly as sliding speed
increased (Fig. 5). The cause of the sharp increase in acoustic
emission at the highest speed tested for each material has notbeen determined. However, it is probably related 00 heating of
the ring and block and oxidation of the ring. When sliding
speed was constant and applied load increased, only a slightincrease in rms voltage with increasing applied load was de.
tected. Fig. 6. The increase in ems voltage for increasing load
is clearly much less than that observed for increases in sliding
speed.

3. Results
A ponion of a data rrace from me strip-<:han recorder,
showing bom AE rms voltage and friction force, is shown in
Fig. 2. In this particular trace, both the AE and friction force
data are fairly well behaved. This was not always the case; insome instances, significant variation of both rms voltage andfriction force was observed with time for constant experimental
test conditions. Average values of the nns voltage and friction
force are used in all the following figures. Each point is an average of 1700 00 2000 values acquired by the transient recorder.

The acoustic emission generated during block-on-ring friction tests correlated very well with earlier results from singlepoint machining investigations. As in the friction tests, rms
voltage increased rapidly with increases in cutting speed during
single-point machining. AE signal level versus cutting speed
is shown for a variety of materials in Fig. 7. It is less apparent
how 00 relate increasing load at a constant sliding speed during

As expected. the friction force increased in a linear mannerwith increasing applied load at constant sliding speed; see Fig.
3. The friction force was less sensitive 00 increases in sliding
speed at constant load. as shown in Fig. 4.

98,

0.08.--------------.
•ALUMINUM· CARBIDE/o STEEL· CARBIDE

-

>

200

/

100

/

~

-

0

Fig. S Acoustic emission nns voltage versus sliding speed atconstant applied load. (Steel-carbide load 44.5 N. aluminumcarbide load 11.1 N)

~

:::l

J
2
1

0.8.,----------------,

>

-

•

ALUMINUM· CARBIDEo

STEEL • CARBIDE=•0... . •
,

•

•
I

o

I

I

I

100 150 200 250 300
Cutting Speed (em/sec)

350

Fig. 7 Acoustic emission produced during machining various
materials as a function of cutting speed. Depth of cut 0.25 mm(except 0.13 mm for 4340-HTI steel); feed speed 0.046
mm/revolution. The data for 304 stainless steel were takenusing different bits. (Heiple et al•• 1991)

0.4

.,>

E

•

o .
o 50

at

10

•

•

• •

LEAD

E

a

• • •

TEFLONo•

8061·T8

0

100

•

•

••• fI/I

w

8co

• •

434o-HT1c:.S!
co.! 100E

0.00+----=--=--.,.-------1
0.00
0.25
0.50
Sliding speed (ml8)

• • •
•
•

3D4

•

different applied loads at a constant sliding speed of 0.18 m/s(sliding distance of 15 - 18 m). A single straight line gives agood fit to both sets of data. It is not known if the single
straight line fit is a general result or if it is a result of the
carbide test block.

0.0 +-----------,r-------~
200
100
o
Applied load (Newtons)
Fig. 6 Acoustic emission rms voltage versus applied load at aconstant sliding speed of 0.18 mls.

As mentioned earlier. rms voltage and friction force were
not always as well behaved as shown in Fig. 2. For example,
Fig. 11 shows data from the transient recorder for a portion of
the friction test of a carbide block on a 440C steel ring. A significant variation in both rms voltage and friction force wasobserved. although test conditions were held constant.

friction testing to single-point machining. During singlepoint machining operations at a constant cutting speed. it ispossible to either increase the depth of cut or to increase thefeed speed. As shown in Figs. 8 and 9. only a modest increasein the nns voltage is observed in either case. Thus, in either
single-point machining or block-on-ring friction testing. theAE generated is primarily dependent upon relative velocity; i.e.
cutting speed or sliding speed.

A good correlation of major features of the two data traces
is obvious. However, a good correlation can also be shown toexist even on very minor features by evaluating the ratio offriction force and rms voltage on a point by point basis (50 ms
per data point). Figure 12 is the ratio of friction force and rms
voltage traces for the data presented in Fig. 11. An almost constant value of the ratio is obtained. The results of Figs. 10 and
12 clearly indicate the excellent correlation of the friction
force and the nns voltage for both short-term and long-term
behavior. Identification of the detailed micro-mechanical processes responsible for the friction forces and AE is beyond thescope of this work. Nevertheless. it is clear that friction between the block and ring is the source of AE in these experiments.

An excellent correlation between rms voltage and friction
force at constant sliding speed was observed for both shortterm and long-term behavior. Figure 10 shows long-time datafor rms voltage and friction force plotted against each other.
Data for both 440C steel and 6061-T6 aluminum alloyrings are shown. Each data point in Fig. 10 represents theaverage value over 8S - 100 seconds of friction force and rms
voltage from the transient recorder. Individual points represent
99

40

• •

•

80
60
40
~ 20
c
0
S

20

434D

-

o

~ 40

-

• •
• •

•

•

co
co

ew

8061-T8

-

~
co 40

4

:::I

0

Te'LON

u

<co

2

0.2
0

o

LEAD

•

304

•
•
•••
•

40

434ll-HT120o8081·T8•• • • •

••

~

o

• •

20
0

•

•••

0.4
0.2

I

I

I

0.1

0.2

0.3

• • •
o
..
.
o 0.2 0.4 0.6 0.8

. 0.4

•

•

4
Te'LON2o•. .
1 1.2 1.4

LEAD

Depth of Cut (mm)Feed Speed (mm/rev)

Fig. 9 Acoustic emission prodUced during machining various
materials as a function of depth of cuL Cutting speeds varied forthe different materials. but were near 100 cmlsec; feed speed
0.046 mmlrevolution.

Fig. 8 A<:oustic emission produced during machining various
materials as a function of feed speed. Cutting speeds varied forthe different materials. but were near 100 em/sec; depth of cut
0.25 mm (except 0.13 rom for 4340·HTI steel). (Heiple et al.•
1991)

4.

0.50

Conclusions• ALUMINUM· CARBIDE-0

STEEL· CARBIDE>-

The primary source of AE during single-point machining
has been proposed to be rubbing friction between the nose
and/or flank of me tool and me freshly machined surface.
Acoustic emission (as measured by rrns voltage) and friction
force were continuously monitored during block-on-ring sliding friction experiments to determine if the characteristics of
AE produced by friction corresponded to those of AE producedby machining. The blockton-ring technique was chosen tominimize complications in the AE data from the presence ofwear particles. Materials investigated were chosen to be similar
to those previously investigated in single-point machining
experiments. Analysis of the data allows the following conclusions.

E
CD

m
CD

=0 0.25..E>•

0.00 ~-------.,..-------io

An excellent one-to-one correlation between AE and friction force was obtained for both general features and detailed
behavior. Any changes in friction force. at a constant sliding
speed. were accompanied by corresponding changes in rms
voltage. The data clearly demonstrate mat the source of AE insliding friction tests is rubbing friction between the block and
ring.

400
Friction force (Newtons)

800

Fig. 10 Acoustic emission rms voltage versus friction force atconstant sliding speed.
sliding speed and only slightly dependent upon load. In machining. AE was strongly dependent on cutting speed and only
modestly dependent on feed speed and depth of cut. The AE infriction tests was dramatically lower for the low-friction coupleof tool steel rubbing on a diamond-like carbon coating. In machining tests. AE produced was dramatically lower when the
low-friction materials lead and teflon were cut. The friction test
results therefore suppon the hypothesis that rubbing friction is

Acoustic emission behavior observed in sliding friction
experiments is completely consistent with AE behavior in earlier single-point machining experiments. The AE (rms voltage)in friction tests was observed to be strongly dependent upon100

generally lhe primary somce of AE during single-point machining. Since friction is the primary AE somce, changes in AE
characteristics with tool wear are expected to be strongly dependent on lhe material being machined, as observed. Tool
wear monitoring is possible with AE because AE characteristics
change with tool wear. but the magnitude and even direction of
the changes are material dependenL- ..

AcknOWledgment.-

This work was supported by the Department of Energy,
Albuquerque Operations Office. Their support is gratefully
acknowledged.
Rererences

280

300

320

340

360

380

400

C.R. Heiple, S.H. Cupenter and D.L. Armentrout (1991).
Proceedings of the 4th World Meeting on Acoustic Emission
and lst International Conference on Acoustic Emission in
Manufacturing, ed. SJ. Vahaviolos, ASNT, Columbus. OH, pp.
463-470.

Time (sec)Fig. 11 Example of short-term variations in friction force andacoustic emission rms voltage at constant applied load andsliding speed for carbide on 440C steel.

C.L. liaa and D.A. Dornfeld (1990), Wear, 139, 403-424.
T. Kita, K. Kogure. Y. Mitsuya and T. Nakanishi (1980), IEEE
Trans. on Magnetics, MAG 16,873-875.

280

300

320

340

360

380

400

Time (sec)Fig. 12 Friction force trace in Fig. 11 divided by the acoustic
emission trace on a point-by-point basis. The nearly constantvalue obtained demonstrates a high correlation between variations in friction force and acoustic emission.

101

AEWG 36th Meeting.rubbing friction is generally the primary source of acoustic emission during single-point machining. Preliminary
tests were also conducted to determine if AE could be used to detect failure of thin coating or films during sliding
friction.
D.L. Armencrout and S.H. Carpenter
The Effect of Precipitate Forming Elements in A1-4.S% Mg Alloys on the Measured Acoustic Emission and on
Liiders BandDefonnation
Acoustic emission (AE) measured in A1-4.5% Mg alloys with varying amounts of additional elements which fannprecipitates in this system. During tensile testing, the samples defanned inhomogeneously with multiple yield
points - often called Uiders bands. Calculated strain rates during Uiders defonnation were greater than 10 s-l. 1luee
distinct regious could be observed in close examination of the drop in stress associated with the yield points. The
AE signal peak was found to occur during the region of steepest decrease in the stress. The magnitude of the stressdrops which is proportional to the amount of plastic deformation in the individual yield points correlated well withthe height of the corresponding rms voltage bOISts.
James R. Mitchell
A Standard Test for Stress Corrosion Cracking with AE
Thomas F. Drouillard
Acoustic EmissionlRestrained Bend Test to Detect Embrittled Braze Joints
The braze joint between Monel tubes and betyllium parts must be adequate to provide structural integrity throughout
the life of the structure. The joint must withstand all of the stresses imposed during manufacture and handling without cracking or forming a leak path. In response to a number ofproduction joints that were found cracked, an acoustic emissionlrestrained bend test was developed and implemented in production as a nondestructive testing method todetect braze joints that many already have existing cracks or embrittled braze joints that might be susceptible tocracking. This was a proof test in which the joint was stressed while being monitored for acoustic emission frombrittle fracture. This paper summarizes the results of using the AElrestrained bend test to effectively son out goodand bad braze joints. and to defme good and bad braze joints. The paper presents data showing a direct correlation
between acoustic emission test results and typical microstructures for both good and bad braze joints. and identifies
the relationship between cracking and the fonnation of a brittle intermetallic which was caused by overheating of thejoint Results for both induction brazed and furnace brazedjoints are given.

A.G. Beattie
Development of an Acoustic Sand Detector
RobenKrauz
AE Interests at US Bureau ofMines

102Comments on the Origin of Acoustic Emission in Fatigue
Testing of Aluminum Alloys
C. R. Heiple, S. H. Carpenter and D. L. Armentrout
Abstract

dormant COnb'Oversy over the origin of the second AE peak in
7075 during tensile deformation.

The size of acoustic emission (AE) signals expected from
inclusion fracture during fatigue testing of 7075 alwninum has
been estimaled on the basis of previous measuremenlS of AE
produced by the fracture of boron particles incorporaled Duo
2219 aluminum. The AE signal size expected from deformationin the plastic zone ahead of the fatigue crack was estimaJedfrom the results of tensile teslS on 7075 alwninum. The signals
predicled from both processes are near or below the noise levelin the fatigue experimenlS and are therefore far roo small 10 lICCOlDlt for the signals actually observed. Nearly simultaneous
fracture of multiple inclusions could produce signals as large as
those observed in fatigue leSlS of 7075 aluminum. however.
fatigue teslS of 7050 aluminum produced signals as large orlarger than in 7075. Since 7050 has subslantially fewer inclusions than 7075, the simultaneous failure of mUltiple inclusions is unlikely to be a major AE source in fatigue leSting ofeither aluminum alloy. Thus. the most probable source of
acoustic emission during fatigue testing of 7075 and 7050 aluminum is the crack advance ilSelf. The measured crack advanceper cycle is large enough to release sufficient elastic energy to
8CCOlDlt for the AE signals observed.

Buttle and Scruby (1990) have just reported the results ofan elegant series of experimenlS in which the source locationand signal-radiation characteristics were measured with highacc:uracy during fatigue tests of 7075 aluminum. They concluded
that the signal sources were crack advancement" because thesignals came from locations close 10 the crack plane and cracktip, and because the signal·radiation paaem was in best agreement with that of a microcrack source.
Heiple et al. (1990) measured the AE signals produced by
the fracture of boron particles in an aluminum alloy matrix during !enSUe deformation. and compared the signals to the energy released by particle fracture. We can use these resullS 10
estimate the size of signals to be expected from inclusion frac·ture in 7075. The resullS of measuremenlS of acoustic emission
produced by tensile deformation can also be used to estimate
the amount of acoustic emission expected from deformation in
the plastic zone ahead of the growing crack tip. Here, we compare these estimates to experimental resullS during fatigue leSting of 7075 aluminum.

1. Introduction

1, Results and Discussion

Recent work by McBride et al. (1988) and Buttle andScruby (1990) has revived questions on the origin of acoustic
emission (AE) during deformation and fatigue of aluminumalloys. McBride and Maclachlan (1983) originally proposedthat the source of AE during fatigue crack growth in 7075 alu·
minum was fracture of Mg-Si inclusions by the advancing
fatigue crack. This conclusion was based primarily on aremarkable agreement between the number and area-size distribution of the inclusions in a section parallel to the fatigue·
crack plane and the number and amplitude distribution of theobserved AE signals. More recently. McBride et al. (1988)
fOlDld large numbers of fractured inclusions in the plastic zoneof the fatigue crack in 7075; indeed. there were about 100 frac·tured inclusions for each AE evenL They have also performed
similar leSlS on 7050 aluminum (McBride. 1991). This alloy issimilar to 7075. but has a much lower inclusion content. In
spite of the much lower inclusion content. about half as manysignals were generated in 7050 as 7075, and the signals were
somewhat larger than in 7075. Their observations led them
(McBride et aI., 1988) 10 propose that the source of AE during
fatigue crack growth was intense plastic now ahead of thegrowing crack. Their observations also rekindled the long·

There are four plausible mechanisms for the origin of
acoustic emission during fatigue crack growth in 7075 andother alwninwn alloys. These possible AE sources are:(1) Fracture/decohesion of individual inclusions by the advancing fatigue crack.
(2) Nearly simultaneous fracture/decohesion of multiple
inclusions in the plastic zone as the fatigue crack advances.
(3) Dislocation motion (plastic flow) in the plastic zone.
(4) Crack advance.
Evidence bearing on these mechanisms is presenled below.
Heiple et al. (1990) measured the acoustic emission produced by the fracture of boron particles in 2219 aluminum Iensile bars during deformation. The energy released by fracture of
the particles was calculated from an expression due to Kant
(1979) (equation I), using fracture sizes obtained from detailed
metallographic observations.
~ =413(1 - v)(a¥/E)RIn equation I, ~E is the energy released, v is Poisson's ratio forthe matrix. a is the applied tensile Slress, and E is YOlDlg'S
modulus for the matrix. R is a function of the ratios of the shear
moduli and Poisson's ratios for the matrix and inclusion. andhas a value of about 0.5 for boron in aluminum. A linear relation was fOlD1d between the calculated particle fracture energy

Received 30 March 1992. The authors are affiliated with Plutoniwn
Technology, EG&.G Rocky AIlS, P.O. Box 464, Golden. CO 80402.
Journal of Acoustic Emission

Volume 10, Nwnbcr 3/4

(1)

103

0730-0050192/10103-106
Copyrighte1992 Acoustic Emission Group

:
c
o

Q.

III

2.4

i'"

2

II

a:Ci1
II _ 1.6.!!'Oi ~ 1.2

III

~

.

t-

III

oZ

.5~

--

III

~! 0.8
II

E
"0

>

•

S

~

0.4

,

o 4--=i==:;:=--r-........___..-........___..--I
o
0.2
0.4
0.6
0.8
Signal Amplitude (mV)O~.....,..---,r---r-....,.-r--r---"T-"T'""""'.....,..--t

o

1X10'

Fig. 3 Impulse response of the rms voltmeter vs. AE signal
amplitude from the fracture of smaIl boron particles. Values refem:d to transducer output.

2X10'
2

SIgnal Energy (Yolt sec)

Fig. 1 Boron particle fracture energy vs. AE signal energy, Iefetred to transducer output. The point plotted with a .. +" is froma particle with a branching crack. (Heiple et al., 1990)

The impulse response of the ems voltmeter is a non-linear,more scanered function of peak signal amplitude than of signal
energy for real AE signals. For AE signals from the smallest
boron particle fracaures, the relation between the impulse response of the fInS voltmeter ad signal amplitude (both n:ferred
10 the transducer output) is plotted in Fig. 3.

II

I)

c

o

a:.
f;

II

We will use these results 10 estimate abe minimum particle
fracture size required to produce the AE signals observed by
McBride and Harvey (1985) in fatigue tests on 7075 aluminum.
This estimate is subjec:t to a number of uncertainties. Fast, themechanical properties of Mg-5i inclusions are not known.
These properties affec:t the value of R in equation 1. However, it
is unlikely that they differ substantially from those of boron,
and in any event R is a slowly varying function of inclusion
mechanical properties (Heiple and Cupenter, I987b). Thus, avalue of 0.5 will be assumed for R. Second, sample geometry
significantly affects the measured signal energy from the same
evenL The boron particle measurements were performed on
small, round tensUe bars with a Dunegan-Endevco 5140 transducer direcdy on the gauge length. On the other hand, the Brueland Kjaer 8313 transducer in the fatigue experiments was 38 cm
away from the fatigue crack, ad the samples wen: thin strips(McBride el al., 1988). The signal sensitivity was therefore
less in the fatigue experiments. However, the extent of this difference is not known. Thus, larger fractures than estimated (onthe basis of the boron particle experiments) are required to ~duce the signals aclUally observed in the fatigue experiments.

2

II=::

.!! g
::J 0
a. ..

E u

-= 11 1

i'-'

E

'0

>

O*"'-r-.....,..--r---.-.-.,-"T"""-r--r-,----.-r----i
-12

o

10x10

-12

20x10

SIgnal Energy (Yol~ sec)

Fig. 2 Impulse response of the fInS voltmeter vs. AE signal energy from the fracture of small boron particles. Values refeaed
to transducer output. (Heiple et at, 1990)
41E and the energy 5 of the resulting AE signal. Fig. 1. The
signal energy 5 was rakeD to be the integral of the signal voltage squared (refetred to the transducer output). The n:lation is~m~X~~~For aluminum, v =0.33, E '" 74 GPa, and R is taken to be 0.5(Heiple el at, 1990). The yield stress for 7075-T6 is 430 MPa,and the ultimate tensile Slmlgth is 570 MFa. Taking the stressnear the crack tip to be about 570 MPa, equation 1 for
fracture of a inclusion of radius r becomes (r in mm, 41E inergs)

with 41E in erg and 5 in V2-s. The response of an rms voltmeterto a single AE burst signal is a voltage spike. The height ofthis spike or pulse above noise (Prma> is referred to here as theimpulse response of the voltmeter. Heiple et at (1990) demonstrated that the impulse response of an rms voltmeter to bothindividual AE signals from particle fracture and individual artificial signals from an AE simulator was linearly proportional
to signal energy, 5 (see Fig. 2). The relation isPnIB = 1.2 X lOti 5(4)Since 41E =4.1 x 109 5, and P

r3 '" 1.7 X 1012 PI1III'

(3)

IDIS

= 1.2 X lOll 5,(5)where r is in mm and Pnm in JlV. In the boron particle experiment, the smallest particle-fracture signals which could teU

with PnIB in JlV n:ferred to the transducer output and 5 in V2-s.

104

ably be det.ee:ted with the rms voltmeter produced a voltmeter
impulse response of about 0.4 p.V (Fig. 2). This corresponds toa fracture radius of 9 p.m, or a fracture area of 250 p.m2. The
largest inclusions observed in the fatigue experiments had
areas less than 100 p.m2, and only 0.5~ were larger than SO.,un2 (McBride and Harvey, 1985).

The plastic zone radius r is given approximately by
r= (KJO)212n(6)

where K is the slress intensity at the crack and 0 lies about
midway between the yield and ultimate stnmgth. For 1075-T6,(KIo)2 a 2.5 mm, SO r • OA mm. (BuUle and Scruby (1990)
estimated r between 0.15 and 0.4S mm in their fatigue experiments on 7075 Al.) McBride et at. (1988) gives the crack advance as 0.1 to 0.2 p.mtcycle. sample thickness was 4.7 mm,so the maximum deforming volume/cycle is about 7.6 x 1()"4Because signal amplitudes (rather than the impulse re-sponse of the rms voltmeter) were used for signal detection in
the fatigue experiments, smaller signals could be detected. The
noise threshold was set at 0.1 mV in the fatigue experiments(McBride et al., 1988), which would correspond approximately
to 0.04 P.V rms-voltmeter impulse response (Fig. 3), if that response could be observed. (An impulse response of 0.04 p.V
corresponds to less than 2S p.m deflection on a 20 em smp
chart when the scale is such that the system noise level is overhalf full-scale.) A PIUII of 0.04 p.V would result from a fractwe
radius of 4 p.m, or an area of SO p.m1. Thus, less than 0.5,*, ofthe inclusions observed in 7075 fatigue tests are capable ofproducing signals as laIge as the noise threshold used, and thelargest signals observed would require particle fracture areas
exceeding 600 p.m2.

mm'.

A value for the strain rate is more difficult to estimate, but
an order of magnitude approximation caD be made. The ~quency of the fatigue test was 0.5 Hz, with an approximately
triangular stress-time history. Crack propagation was assumed
to occur in the top 2K of the rising load cycle; i.e., over 0.2
s. If the total strain is assumed to be 0.11 in the plastic zone
each cycle (0.11 is the strain to failure for 7075-T6), then thestrain rate is O.5S/s. For the tensile tests, the deforming volume was 250 mm' at a strain rate of 5.6 x Ut5/s. Thus the rmsvoltage peak height from the plastic zone ahead of the fatigue
crack can be estimaled in the fonowing manner:

Therefore, the AE signals observed in fatigue tests of 7075
and other similar alloys caJI1lot arise from the fracture of single
inclusions, in agreement with the later conclusions of McBrideet al. (1988). In principle, the advancing fatigue crack could
fracture many inclusions nearly simultaneously and produce
detectable signals. In McBride and Harvey (1985), the medianinclusion area was 2.7 ~ (0.93 p.m radius) and the mediansignal amplitude (for signals above a 0.1 mV lhresbold level)
was 0.2 mV. A 0.2 mV signal corresponds approximately to
0.08 p.V rms-voltmeter impulse response (Fig. 3). The estimated energy release which would produce a signal of this sizeis 2.7 x 10" CIg5. It would require the simultaneous failure ofabout 170 of the median-sized inclusions to release this muchenergy at once. Experimentally, only about 100 fractured inclusions were found for eadl AE event (McBride et al.. 1988).
Thus. it appem unlikely that simultaneous fracture of mUltiple
inclusions is a major source of acoustic emission during fatigue
testing of 7075 aluminum. This conclusion is verified by observations on 7050 aluminum (McBride, 1991). This alloy has
substantially lower inclusion content than 7075, yet the AE
signals produced during fatigue of 7050 are as large or larger
than those prodUced in 7075 aluminum.

(0.2 P.V)" /7.6 x 10" em' O.5S Is
V 0.25 CfJiJ 5.6 x 10" Is= 0.03 P.V

This value assumes that the rms voltmeter sees the peakvalue over a time comparable to the voltmeter time CODStanl, as
in a tensile tesL However, the ac;tual signal occurs over 0.2 s(or less). The signal energy for a continuous sinusoidal signalof 0.03 p.V rms for 0.2 sis 2.4 X 10'16 V2-s, giving a predicted
impulse response of the rms voltmeter of 3 x 10-5 P.V (equation3), if the signal is Ircatecl as an impulse. The ac;tua1 value will
lie between the continuons signal estimate (0.03 p.V) and theimpulse estimate (0.03 nV). Since the noise threshold in thefatigue tests corresponds to 0.04 P.V nos voltmeter impulse
response, signals from deformation are below the noise level,aDd in any event are far too small to account for the larger
signals observed in fatigue.

The fmal possible mechanism is elastic enCIgy released by thecrack advance itself. The area of crack advance (0.1 to 0.2 .,unacross a thickness of 4.7 mm) is 470 to 940 p.m2. This area isconsistent with the 600 p.m2 crack area calculated above which
would be required to produce the larger signals observed in
fatigue testing. Signal amplitudes are expected to decrease withincreasing temperature, both because the yield Sb'eSS decreases(AE oc 0 2 in equation 1) aDd because the sudden crack jumps
probably become smaller. A sharp decrease in sigDal amplitudewith temperature was observed by McBride and Harvey (1985).
The number and size of AE events during fatigue is only
indirectly related to inclusion conteDl, as observed in 7075 and7050 aluminum. Thus, experimental observations and signal
size estimates demonstrate that the AE source in fatigue ofaluminum alloys is the crac;k advance itself, as proposed by
Buttle and Scruby (1990).

The third possible AE source is dislocation motion in theplastic zone ahead of the fatigue crack. Heiple et al. (1981)
have measured acoustic emission produced by deformation of7075 aluminum and other alloys. The acoustic emission waschancterized by a peak near yield (the first AE peak) which bas
been shown to arise from dislocation motion. The maximum
rms-voltage value of the quasi-continuous AE signal near yieldin 7075 for any heat treatmeDt attempted was about 0.2 p.V. The
rms-voltage value of the continuous or nearly continuos signal
produced by dislocation motion is known to be proportional to
the square root of sb'ain rate and deforming volume (Heiple and
ClJPCDter, 1987a). If the volume and strain rate of the plastic
20De in fatigue tests can be approximated, then the rms-voltageof AE signals can be estimated from tensile tests.

The results derived above are also consistent with the accepted mechanism of inclusion fracture and decohesion as the105source of the second AE peak (typically occuniDg beyond 1CJ,
plastic strain) observed in tensile deformation of 7075 andother aluminum alloys. There are large numbers of inclusions
present in 7075 and some other aluminum alloys. The fracnue
and dec:ohesion of many of these inclusions at nearly the sametime produces the almost continuous AE signal often observed.

References
OJ. Buute and C.B. Scruby (1990), J. AcousL Emiss•• 9. 2A3254.
C.R. Heiple and S.H. Carpenter (1987a), J. AcousL Emiss., 6,
177-204.

Cousland and Scala (1981) bave measured the acoustic
emission from both 7075 and 7050 aluminum in tension. They
also made detailed measurements of the size distn'bution of inclusions in both materials. The second peak was substantiallylqer in 7075 than 7050. and the 7075 had substantially moteiIIclusions ill every size range than the 7050. The 7075 bad a
substantial number of iIIclusions in the 100 to 1000 p.m2
range. The inclusions in the materials tested by Cousland andScala were both larger and mote numerous than those in the materials tested by McBride and Harvey. Fracture of the big inclusions is expected to produce large individual bursts and a"spiky" rms-voltage curve, as observed. The 7050 had telatively few of these laIge inclusions, and exhibited a muchsmoother rms-voltage trace. Neither material produced acoustic
emission in compIeSsion. These results are not consistent withdislocation mechanisms, and are completely consistent with
the AE soun:e being the fracture and deeohesion of inclusions.
3.

CoR. Heiple and S.H. Carpenter (1987b). J. AcousL Emiss., 6.
215-237.
C.R. Heiple. SoH. Carpenter and MJ. Cm (1981). Mel Sci..
IS. 587-598.
CoR. Heiple, S.H. Carpenter and S.S. Christiansen (1990). Acta
Metall. Mater.• 38, 611-618.
R. Kant (1979). ''The Elastostatic Axisymmetric Problem of
Cracked Sphere Embedded in a Dissimilar Matrix," Doctoral

Thesis. University of California, Berkeley. CA.
S.L. McBride (1991), UnpUblished IeSCarCb.
SL McBride, P. Bowman and K.L McRae (1988), Progress in
Acoustic Emission IV. eels. K. Yamaguchi et al., The JapaneseSociety for Nondestructive Inspection. Tokyo, Japan. pp. 528535.

Conclusions

The size of AE signals expected from inclusion fracture lIDd
plastic flow in fatigue tests of 7075 aluminum has been estimated. Tho estimates are based on measurements of acoustic
emission from the fracture of boron particles in a 2219 aluminum matrix and from tensile tests of 7075 aluminum. Failureof the inclusions observed in 7075 fatigue tests is ptedicted to
produce signals far too small to account for AE signals actuallyobserved. Simultaneous or nearly simultaneous fracture of multiple inclusions also is not likely to be a major source in viewof the observation that signals similar to those detected in
7075 are produced during fatigue of 7050 aluminum. The latteralloy bas substantially fewer inclusions than 7075. The AE
signal produced by deformation in the plastic zooe ahead of thegrowing fatigue crack is also too small to account for the observed signal.

SL McBride and J. Harvey (1985), J. AcousL Emiss.. 4,
S220-S223.
S.L. McBride and J.W. Maclachlan (1983). J. AcousL Emiss.,
2. 179-185.
S. McK. Cousland and CoM. Scala (1981). MeL Sel. 1S. 609614.

Thus, the AE signals ate produced by the crack advance itself. The elastic energy released by crack advance is consistent
wilh the size of AE signals produced. Calculated and observed
results ate also consistent with inclusion fracture and decohesion being the source of the second AE peak observed in tensile
deformation of 7075 and other aluminum alloys.
AcknOWledgment
This work was supported by the Department of Energy,
Albuquerque Operations Office. Their support is gratefully
aclalowledged.

106

Acoustic Emission of Wood during Swelling in Water

Stefan Poliszko, Waldemar Molinski and Jan Raczkowski
Abstract

Particularly pronoWlced permanent changes in wood areobserved in the case of me simultaneous action of two or more
Variables (e.g.. mechanical stress and moisture), which can beseen as the so-called, mechano-sorptive effecL Raezltowski(1969), concerned with mechano-sorptive effects in wood, haspresented a thesis about lhe superposition of a creeping process resulting from changing orientation of defmite boWlds on
the substructural level wim Ihe simultaneous destruction process. The latest analyses of the creeping process by AE memod(Morgner et al., 1980; Beall. 1986; Niemz and Hansel, 1987)
seem 10 have fully conftrmed Ibis mesis. However, as far as weknow, me literature does not provide any data on me appearance of structural defects in wood during moistening. Thus assuming that bener recognition of this problem can conttibute
10 more accurate determination of the causes of me mechanosorptive effect, a project was undertaken to explain acoustic
effect phenomena in moistened wood.

In order to characterize acoustic emission (AE) generaled
in wood swelling in water, Ihe total eYent counts, the amplilUdedisln'bution of AE signals and rale of their generation weredeleImined. The Jdnetics of Ihe increase in IOtal number of AE
signals was compared 10 Ihat of wood swelling in Ihe same
conditions. A rapid increase in Ihe number of AE signals generated in time unit was observed when volumetric swelling of asample reached about 20% of the maximum swelling. Togelher
wilh the measurements of AE effects in swelling wood, Ihemeasurements of critical strains of wood tensil-tested in radial
direction for different moisture contents have been performed.
The values of wood swelling in water, which generale AE signals. are comparable to mechanical deformations of wood tissue upon its failure. The obtained results indicate that the main
source of AE in swelling wood are microcracJcs induced by nonhomogenous distribution of hygroslress. locally exceeding thestrenglh of Ihe investigated material. It was found that theintenSity of acoustic effects in the swelling wood is proportional 10 the microcracks area on me longitudinal-radial andlongitudinal-tangential plane of the wood. The results of temperature measuremants indicate mat me activation energy ofbond dissociation within me microcracks takes a value about
12.5 kJ/mole. A temperature dependence of AE total event
counts exhibits a rapid decrease at 55'C, which may be taken as
Ihe glass-transition temperature for the amorphous components of wood.

2. Experimental Procedures
Because of me characteristic high sorptive Slress, beech
wood (Fagus sylvalica L) has been chosen for investigationsof acoustic emission in wood on its moistening in water. The
experiments were carried out on sample, in me shape of rectangular prisms of Ito 5 em in height (along me fibres) and squarecross-sections wim me edges of 2, 3, 4 and 5 em. The samples
were made of one block 2 m long in such a way mat the crosssection of Ihe smallest samples covered Ihe same yearly increments as me central pan of the samples wim the largest crosssection. The samples prepared in this way were gradually driedin laboratory dryer to me oven-dry state. The density of woodin me oven-dry state was within Ihe limits of 680 to 112
kg/m 3.

1. Introduction
Investigations of wood properties by means of acoustic
emission (AE) melhod have been carried out for several years.
This method is most often applied to get a better insight intome process of wood destruction due to mechanical effects(Poner et al.• 1972; Morgner et at, 1980; Sato et aI., 1984;
Noguchi et al.. 1986). Recently, Ibis memod has become usefulin investigations of cracks formation in wood during its drying(Noguchi et al.• 1980, 1982, 1983. 1985; Becker, 1982;
Honeycutt et al, 1985; Wassipaul et al.. 1986). From me above
papers, the source of AE signals generated in wood are microcracks appearing mostly in Ihe radial plane. In case of wood
wim clear structural nonhomogenity me source of AE can also
be microcracks on the bonduary of annual increments. The
appearance of Ibis kind of structural defects at relatively low
Slress (much below me proportionality limit) is directly related
10 irreversible changes in me examined material.

The measurements of acoustic emission during moisteningof Ihe samples were performed on DEMA-20 apparatus (Tech·
pan-Warsaw) equipped with a resonant piezoelectric transducer(500 kHz). The sensor was always mOWlted in the middle of Ihesample cross-section (Fig. 1).

In each experiment the same amplification level of 85 dB
was used. The discrimination threshold was selected in such a
way Ihat Ihe release of air bubbles was not registered. During
me moistening of the samples Ihe changes in effective rms
voltage of me AE signal as well as me AE total event counts
were also registered. A block diagram of the measuring system
is given in Fig. l.

3. Results and Discussion

Received in August 1989. in ~ed fonn. I July 1990. The authors areaffiliated with Depanment of Physics and Chair of Wood Science,

In me fl1st stage of the investigations of acoustic emis·
sion in swelling wood, the Jdnetics of me increase in the total

Agricultural University of Poman. Wojska Polsk1ego 38/42. PL 60-637
PomaD. Poland.

Joumal of Acoustic Emission
Volume 10. Number 3/4

0730~SO~10107-111

107

Copyrightel992 Acoustic Emission GroupDiscriminator

Amplifer
AE Transducer

Pref1~~?Il amplifer

Converter
BCD/BIN

CounterO/A Converter

x-v

x-v

RMS

RecorderRecorderXL- Longitudinal

XR- Radial
XT- Tangential

SampleFig. I Block diagram of Ihe equipment for AE measurements.

I( t)La r----:_---_-----:-:::-;-;:-""-::-~---~LO

I max as/"I

as C£max

,

I

/

,-

MC(t) 1,0
MCmax 0,9dye t)

b

1-

It
I max

o

0,8

I

0,7

a

0
Q

20

0\0

60

80

100 120 140 160 180
Time. t [min]0,6 I- b

Fig. 2 Kinetic curves of acoustic emission (a) and volumetricswelling (b) of beech wood in water.

0,5 l-

number of AE events was compared wilh Ihe kinetics of Ihe increase of volumetric wood swelling (ely), measured in the same
conditions. As follows from the exemplary kinetic curves presented in Fig. 2. in Ihe first stage of water penetration in woodthe changes in volume precede the acoustic effect which can beclearly observed only after some dead time. This dead time has
been determined to be the time within which Ihe volumetricswelling of Ihe sample has reached 20% of its maximum value.
At that time, Ihe number of AE signals generated per time unit
increases rapidly, and from Ihen the swelling is considerably
delayed relative to the AE effect.

0,4

••

f

0,3
~
~

~

r0.--

0,1

~

§l

°° 1

The AE measurements in the swelling wood were accompaniedby those of scrain at failure of wood samples scretehed in theradial direction for different wood moiswre contenL The valueof moisture caused deformation, which is equal to the scrain at
failure of wood sample in tension across Ihe grain, can be
determined as critical scrain. The connection between a relative
number of AE signals and Ihe relative wood moisture testifies
to an unambiguous relationship between Ihe critical scrains inwood and the appropriate moisture caused scrains (Fig. 3),
which may become the sources of acoustic emission. The above
results can be interpreted assuming Ihat in nonstationary conditions for investigating the acoustic emission induced byswelling, the water penecration of wood occurs in such a way
Ihat a sharp line may be drawn between the fully penecrated andI

345£R(max) [!oJ

Fig. 3 The relative number of AE total event counts (lJlmu)
versus the degree (Cl r ) of wood swelling <a> and the relative
moisture content (MCIMCmu) as a function of critical scrains(Emu) in radial direction (b).
unpenecrated regions (Helinska-Raczkowska et al., 1988). The
swelling of the peneb'ated regions brings about Ihe deformation in Ihe neigbouring unpeneb'ated regions. This may produce Ihe sb'ess which can locally exceed the strenglh of material and become a source of AE signals recorded on the woodswelling.

108h = 3 em

RMS
h = 2 emh = 1 emTime, tFig. 4 DislribulioD of energy of AE signals (relative units) generated by swelling in water of wood samples of differeot height (h) andthe same area of square cross-section (3 x 3) cur..

- e~~~

B
7'""'-j

6
5

eh=l
oh=2
xh=3
oh=4
6h=5

o

1

2

3

em
em
em
em
em

~~

4

5

Length of sample side , a [em]Fig. S Diagram of dependenc:e of the total AE event counts per unit area of cross-section of samples on the length of the sample
edges.

1D order to give a quantitative description of acoustic emission in the sweBiDg wood, the AE measurements were performed for wood samples of different size. It was intetesting to fmetthat there is no significant relationship between the AE total
event counts per a unit area of the sample cross-section and theheight of the samples (Fig. 5). However. as follows from Fig.
4. the average energy of the generated AE signals increases
proportionally to their heighL With the increasing height ofthe samples. the area of cracks. proportional to the AE signal
energy, increases. This may indicate that the cracks spread allover the height of the samples. This conclusion is consistent
with an earlier relationship between the impact resistance andthe frequency of the emitted waves per unit area destroyed(Suzuki and SChniewiDd, 1987). In the light of the above andtaking into account the anatomical wood structure, we may
assume a model in which a net of mutually crossing lines ofcracks covers the cross-section area of the swollen wood. The
crossing points of the lines determine the width of elementary
microcracks, which are the elementary sources of acoustic
emission in swelling wood.
According to the model. the number dN of elementary
microcracks in dS (c dXPXJ area of the cross-sectional (XrXJ
surface can be dcsc:ribed by:109

dN~2~

The appearance of a crack in the macerial is a random effectof thermo-fluctuational dissociation of the bonds initiated by
the stress (Regel et at.. 1974). Thus. formation of a creak of !hearea (h 1) requires a certain number of bonds. proportional tothe area of the crack. to dissociate. The probability (P) of appearance of such a crack on the cross-sectional surface of thesample of the area (h a) is determined by a ratio of the numberof the dissociated bonds to the number of all bonds; i.e., theratio of (h I) to (h a). Therefore. p = lIa and ac:cordiDg to theBoltzmann's law:

(1)

1,]1

where lr and It are the microcracks mean dimensions in theradial and taDgential directions.
Among all AE signals emitted by microcracks. the transducer records only the signals of the intensities exceeding thethreshold of the measuriDg equipmenL This means that the AE
signals from small mic:rocracks and those whose intesity falls
below the threshold due to the atleDuation are not recorded. The
decrease in the AE signal amplitude for a distance X from thesource is described by an exponential relation (SkudrzYk.
1971) of the type:s = So exp ( - ~ X ),p = II a= exp (-AElRT]

where I = (I,1Jll2 is the mean length of a miCJ"0Q3clc on the
XrXt-plane, AE is the activation energy of dissociation of one
mole of bonds, R is gas constant and T is the absolute tempera-(2)where the coeffICient ~ is a measure of losses of acoustic eneqy
in the medium. Therefore. taking (1) and (2) into account, theture.

total number of recorded AE signals registered due to swellingof wood is descn'bed by the equation:
II ;;

I

2k exp [- 3 (X1 +xl)tn] dX.ciX&
51,1&

Ac:cording to our considerations the temperature dependence of the total number of AE signals generated by swellingwood should be as follows:(38)

It =2 Ie exp [2AElRT] exp ( - ~ 812)

where k is a coefficient related to the sensitivity of the measuring system. For samples of the square cross-sectioo. when 8r =It = a and the transducer is located in the center of the samples,
integration of equation (3a) leads to the relation:II;;2

2ka exp[-~aI2]
lr ~

(3b)

70

lnl t

60

50

40

·

Above 55°C. a rapid decrease is observed in this dependence. It is probably a consequence of the fact that the glasstransition temperature of some amoIphous components of

30

20

10

r-

9

~

10

.~

_. ~

I

,

8

,,1
,
j

7
6

5
4

2.3

3.0

(5)

Consistent with theoretical predictions. the results oftemperature measurements presented in Fig. 6. reveal a linear
character of m(lJ dependence on lIf. for temperatures ranging
from O°C to about SO"C. The activation energy ~E of bond dissociation within microcracks determined from the line slope
assumes the value of the order of 12.5 kl/mot.

The diagram of the dependence of m(lt/r) on the length ofthe edges of the tested samples, given in Fig. 5, indicates agood agreement between the experimental data and relation
(3b).

11

(4)

14

3.2

16

18

1/ T- 1 0 ) . [I(~Fig. 6 TemperatUre dependence of the total number of AE signals recorded during swelling of wood in water.
110

wood cell walls has been reached (DawidsoD, 1962; Irvine.
1984; Iida, 1986). This, iD turD, would imply a rapid increaseiD acoustic wave damping coefficient (~ in the medium as well
as a decrease of the probability of mUle cracks formation.

W. MOJgDer, P. Niemz and K. Theis (1980), "Anwcodung cler
Schallemissionsanalyse zur UDtcrsuchung von Bruch- undKriecbvoIgangen in Werkstoffen aus Holz", Holzlecbnologie,
22(2), 77-82.

4.

P. Niemz and A. Hansel (1987), "Zur Anwendung der Schallemissionsanlage in der Holzfonchung", Holztechnologie, 28(6), 293-297. .

Conclusions

1. The investigations of AE in swelliDg wood indicate the existence of less recognized adsorptive cracks besides the commonly known desorptive microcracks.

M. Noguchi. Y. Kagawa aDd J. Katagiri (1980), "Detection of
acoustic emissions during hardwoods drying", Mokuzai Gatt.,
26(9), 631-638.

2. The values of wood swelliDg in water that generares AE siguals are comparable with mcc:haDica1 deformations of wood tissue upon its failure.

M. Noguchi, Y. Kagawa and J. Ka1agiri (1983), "Acoustic emission generation in the process of drying hardwoods", Mokuzai
Galck., 29(1), 20-23.

3. The iDteDsity of AE effects in the swelliDg wood is proportional to the microcrack area on the cross-sectional surface ofwood.

M. Noguchi. S. Okumura and S. Kawamoto (1985), "Characteristics of acoustic emissions duriDg wood drying", Mokuzai
Galck., 31(3), 171-175.

4. The activation energy AE of bond dissociation within the
microcracks takes the values comparable to the energy of hydrogen bonds that is of about 12.5 kJ/moL

M. Noguchi, K. Nishimoto, Y. Imamura, Y. Fujii, S. 0ku1llW'a
and T. Miyauchi (1986), "Detection of very early stages of
decay in western hemlock wood using acoustic emissions",
Forest Prod. J., 36(4), 35-36.

5. A temperature dependence of AE total event counts exhibits a
rapid decrease above 55°C, which may be taken as the glasstraDsitioD temperature of amorphous components of wood.

A.W. Porter, ML El.osta and OJ. Kusec (1972), "Pledicitionof failure of fmger joints using acoustic emissions", Forest
Prod. J.. 22(9), 14-82.

Acknowledgments
The authors would like to express their thanks to Prof. J.
Ranachowski and Mr. Z. Ranachowski from the [PPT of thePolish Academy of Sciences for their valuable help in our investigations.

J. Raczkowski (1969), "Der Einfluss von Feucbtigkeitsanderungen auf KriechverhalteD des Holzes", Holz als Roh- u. Weststoff, 27(6), 232-237.

References

W.R. Regel, A.I. Slucker and EJ. Tomaszewskij (1974), Kinelicukajll prirodo. prOCMSli l11erdych leI, Nauta, Moskva.

F.e. Beall (1986), "Effect of moisture conditioning on acoustic
emission from particleboard", J. Acoustic Emiss., 5(2), 71-76.

K. Sato, N. K.amei, M. Fusbitani and M. Noguchi (1984), "Discussion of tensile fracture of wood using acoustic emissions. A
statistical analysis of the relationships between the characteristics of AE and fracture stress", Molcuzai Gakk., 30(8), 653659.

H.F. Becker (1982), "Scballemissionen wabrend der H01zlrocknUDg", Holz als Roh- u. Werkstoff, 40, 345-350.
R.W. Davidson.(1962), "The influence of temperature on creep

in wood", Forest Prod. J., 12, 377-381.

Eo Skudrzyk (1971), The /oundQl;ons 0/ tzCoustics, SpringerVerlag, Wien-New York.

L Helinska-Raczkowska, S. Poliszko and J. Raczkowski(1988), "StleSS relaxation in wood under unsteady penetrationof physically active liquids", Progress and Trends in Riteology
II. SIIpL 10 Rlleologicll Aetll, cds. H. Gieskus and P. Mitscbka,
26, 236-238.

M. Suzuki and A.P. Sclmiewind (1987), "Relationship between
fracture toughness and acoustic emission during cleavage failure in adhesive joiDts", Wood SCi. TecImol, 21(2), 121-130.
F. Wassipaul, M. Vanek and A. Mayerboffer.(1986), "Klima
und Schallemissionco bei Holztroeknung", Holzfonch. u.
Holzverwert., 38(4), 73-79.

R.M. Honeycuu,Ch. Skaar aDd W.T. Simpson (1985), "Use of
acoustic emission to control drying rate of red oalc", Forest
Prod. J., 35(1), 48-50.
I. lida (1986), "The thermal softening of green wood evaluatedby its Young's modulus in bending", Mokuzai Ga1clc., 32(6),472-477.
G.M. Irvine (1984), "The glass transition of lignin and hemicellulose and their measurement by differential thermal analysis",. Tappi J., 67(5), 118-121.
111

Characterization of the ASL Parameter

J. W. Whittaker
Abstract
Electron beam (EB) welding of uranium produces large
amounts of acoustic emission (AE) having a broad range of
amplitudes. The Average Signal Level (ASL) parameter has
been used to summarize and quantify such emission. The parameter's ability to average the effects of numerous signals and its
large dynamic range make it particularly useful. We performedexperiments to provide a better understanding of the responseof the ASL circuitry to AE produced under various conditions.
The effects of emission type (contmuous, burst and mixed),
signal amplitude and frequency, and recording inslIUment were
evaluated.

Y4U'41t
IllD.....------------------.;..-;..;,

80

&0

20

1. Introduction
EleclroD beam welding of uranium (WhiUaker and Murphy,1991) produces continuous AE interspmed with large Dumbersof high amplitude bursts. Continuous emission is representative of weld formation. Large amplitude bursts (many as high as
95 dB in reference to 80 dB .. I V) m produced by weld expulsion (and possibly other sources) and smaller amplitude ones
«60 dB) are related to some, as yet unidentified. cooling pheno1DCD8. Since emission is produced by welding processes, itbas the potential to provide some indication of the quality of
the weld that is being formed. Meuwement of weld penetration, for example, in real time would be beneficial in that a
post-weld ultrasonic examination could be eliminated and thecost of the weldment reduced. An experimental program isunderway to establish AE monitoring as a viable technique for
real·time penetration measurement of a variety of materials.
Early experiments were designed to evaluate the ability ofvarious signal conditioning techniques to provide an adequate
representation of the welding process from an AE viewpoinL
The most commonly used averaging measurement, root-mean·squared voltage, was discanled because it is typically limited 10a linear dynamic range of ooly 20 - 2S dB. The ASL parameter,
as measured by a Physical Acoustics Corporation (pAC) Model
3102 AE analyzer (100 kHz high-pass flltered), was found 10
adequately average the time-varying weld AE signals and had auseful dynamic range of - 70 dB. The ASL has been successfully
used to measure AE signatures of welds with a broad range of
pene&ration. (See Fig. 1 for a typical signatUre.) A search of thelileratUre revealed that the ASL parameter is very seldom utilized. Only one paper mentioning the ASL (Falchi, 1986) ismown 10 the author. Since little was known about how the ASL
Received S May 1992. The awhor is atr'illated with Deve10pIDnt
Divisiou. Oat Ridge Y-12 Plaut, PO Box 2009, Oat Ridge, 1N 37831.
Oak Ridge Y-12 PIa1It is mauged by ManiD Marietta Euergy Systems.
IDC. tor the U. S. DepanmeDt ot Energy uDder ContJaet DE·ACOSBAOR21400.
JClUnIII of Acoustic Emission
113
Volume 10. Number 314

Oc=::==--.....L..

o4......_ _.....L..

•12........_ _.....J"20

Fig. 1 Typical acoustic emission signature from electron beam
welding.
circuitry would respond UDder different AE conditions, experiments were performed to elucidate the e1fec::ts of emission type(i.e., continuous, burst and mixed), frequency response. burst
parameters (rate, amplitude and duration), and recording iDswmeaL

2. Experimental Work
2.1 Apparatus
According to the manufacturer, the quantity bOWD as ASL
results when the logarithmic amplitude of the preamplified andfiltered AE waveform is continuously measured and the resu1lant
voltage passed tbrough a resistance/capacitance circuit for
averaging. It is generally utilized to measure the average levelof continuous emission sucb as that resulting from fluid leaks
and is relatively insensitive to burst-type emission. However,
if bursts occur at a very rapid rate or have very long duration,
they can affect ASL measuremeoL

The apparatus in Fig. 2 was used to subject the ASL circuitry of a PAC 3104 AE analyzer to electronically-generated
signals designed to simulate continuous, burst and mixed (c0ntinuous and burst) emission. (A 3104 was used because of its
availability. The ASL circuitry of the 3104 is identical to thatof the 3102.) Continuous emissioD was simulated by theosdllator (Krohn-Hite Model 4200A) which generated steadystate sine waves of selected frequency and amplitude. The AE
simularor (Acoustic Emission Associates Model AES-l) wasused 10 simulate either burst-type emission or (through mixing
its burst and carrier wave signals) mixed coDtinuoUSlburst emis~CV92II0113-116

CopyriptCl992 Ac:ousaic PmisliCll GroupOSCILLATOR
1CH42OClAh-

AEMONITOR
PAr. 3llllllnlCM

Y.QAI1~"100

ASL
AESlMULATOR

A£S.t,.

80

OUT

MIXER

OUTPUT

OSCILLOSCCPE
NICOLET 40948 WI""
4&70 PLUG-IN

80

i...

DVM
FWICE

:l

lI060A

Fig. 2 Experimental apparatus.
sion. A digital oscilloscope (Nicolet 4094B with a 4S70 plugin amplifier) was used to observe the signals and make peat
voltage measurements. The digital voltmeter (Fluke 8060A)
was used to measure the dc voltage resulting from ASL measmement of continuous signals. The PAC Model 3000 computer attached to the 3104 recorded data.

20

oL-

-.JL-..__.................._ _. - . - _......

0.01

As the project progressed. a new AE analyzer (pAC Model
LOCAN-AT) was introduced. It utilizecl a 100 - 300 kHz passband. The signal conditioning features (including the ASL circuitry) of the instrument are essentially the same as a 3102 or
3104. It differs, though, in that it internally samples the ASL
signal and records it. The 3102 and the 3104 measure the ASL
clim:tly but have no provision to digitally sample it as they doother more standard AE parameters measured (i.e., AE counts.
amplitude, etc.). Instead, the ASL signal is routed to an external
PlIJ'lUIIetric input where it passes through a low-pass fJIter and isthen digitized and recorded. To enable comparison of the ASL
recording capability of the LOCAN-AT and the 300013102, theinput signal generated during a typical weld was simultaneously
recorded by both instruments.

0.10
UIO
Input IIgnaIIIqIlitude (Vpp)

10.00

rag. 3 Behavior of the ASL parameter as functions of signal
amplitnde md fIequency.
3. Results
In Fig. 3, the recorded ASL value is plotted as a fimction of
f'n:quency and amplitude. The ASL iac:reased approximately linearly as a function of the logarithmic signal level. Unearity
was best for 300 kHz and SOO kHz. Additionally, for a given
signal amplitude, the ASL value decreased with higher frequency. Due to the linear ASL behavior at 300 kHz, the expectation is that the ASL value of signals derived from welding (mwhich a 300 kHz resonant sensor is used) should vary linearlywith the logarithm of the input signal ampliblde, as desiJed.

2.2 ProcedllTe

The ASL values for various simulator output conditions are
summarized in Table 1. The normal noise level of the ASL circuitry is 1 - 2 dB. SiDe wave input is sigoificantly greater (38 39 dB). Short duration pulses added only 2 dB wbile long pulses
added 7 dB to the overall ASL leveL Thus, if weld expulsion 00curs simultaneously with the continuous emission produced byvolumetric weld processes, the expulsion-related AE will cootribure only a modest amouot to the peak ASL value ra1ber thancoostiture the maiD portioo of it. Consequently, even in expulsiou-prone weld portions, such as at initiation, the largest majority of the ASL is due to welding. This clarifies some of the
AE behavior of weld observed previously.

Oscillator frequency was varied over the 100 kHz to 1 MHz
range and signal amplitude varied from 0.01 to 8.0 V peak-topeak. The pulsed output of the AE shm~18tor was mix~ with alow level (60 mV) sine wave from the sunulalor's camer output
to produce a signal which simulated continuous emission mixed
with high amplitude bursts. Pulses with a 9S dB peak amplltude
were produced at a rate of 2401s. Pulses of this amplitude andrate have been observed during high expulsion portions ofsome welds. Pulse durations (above an arbitrary 0.1 V threshold) of 100 and 1000 JlS were also used to assess potential pulse
duration effects 00 ASL measurement.
The signal output generated during a welding run wasrouted simultaneously to the 3102 and lDCAN-AT. The ASL
signal was sampled by each instrument at its maximum rate,lOOIs for the 300013102 and lOis for the LOCAN-AT.

Table 1. ASL value as a function of simulator output conditions.

ASL(dB)

114

Sine Wave Oo1y

+ Short Pulses+ Long Pulses

40

42

47

8Or-------------------------------,Y-GA92·1258!Weldend
60

20

0

0

4

12

8

16

20

Time (sec)80

rid . .

60

iii"

:s.
... 40
~

20

O'-

o

.L...-.L...-

4

8.L...-......

12

16

---J

20

Time (SIC)

FIg. 4 Comparison of ASL signatures RCOrded by different AE analyzers during welding of a step joint in uranium.
a. PAC 3000J3102 (top); b. PAC LOCAN·AT (bottom).
The AE signatures of the weld recorded simultaneously by
the 300013102 and LOCAN·AT are compared in Fig. 4. AU features pn:sent in the 300013102 signature are also present in the
LOCAN-AT signature. The LOCAN-AT signature is smootherdue to the lower sampling rate. The key difference between thesignatures is that the features of the LOCAN-AT signature arehigher in amplitude (- 9 dB). Since both instruments processed
the same signal input with essentially the same circuitly (the115

only difference is the 100 to 300 kHz filtering used in the
LOCAN-AT which should attenuate the signal rather than amplify it) the difference arises elsewhere. Investigation revealed
that fIltering of the parametric input of the 300013102 is responsible for the difference. The increased ASL value of the
LOCAN-AT signature should not in any way affect measurements required to correlate ASL value with weld penetration,
similar to those made with the 300013102 data.

4. Conclusion
The ASL parameter, as implemented in PAC instrumentation, exhibits essentially linear response with logarithmic
amplitude over frequencies typically used in AE experiments. It
has a large dynamic range, which makes its use in certain applications more attractive than the more traditional root-meansquared voltage measuremenL It is relatively insensitive topulsed emission. Care must be taken in selection of the methodof recording the ASL since low-pass fIltering can attenuate its
dc-voltage representation.
References
J.W. Whittaker and J.L. Mwphy (1991), "Real-Time Measurement of Electron Beam Weld Penetration in Uranium by Acoustic Emission Monitoring," Proceedings of the 4th World Meeting on Acoustic Emission and 1st International Conference on
Acoustic Emission in Manufacturing, 16-19 September, Boston. MAo ASNT, Columbus, OH. pp. 447-452.

116

Acoustic Emission from Bubbles in a Water Column

Mark A. Friese) and Jack F. Dawson
Abstrac:t

AE at higher frequencies. These authors reported successful use
of a pattern recognition technique with data vectors composedof spectra in various frequency bands along with other parameters related to the spectral and signal amplitude distributions.
Beattie (1982) developed a model to describe the response at asensor to bolling-related soun:es in a solar receiver. The modelis based on the integral of a distributed source function and an
attenuation function similar to the method used below.

The unique remote monitoring capability of acoustic emission methods shows a potential for quantitatively monitoring
multi-phase flow in pipes. Using a simple experimental apparatus where gas bubbles are injected into a tube filled withstanding water, an ability to obtain a quantitative relationshipbetween acoustic emission and void fractioo during a vCIY simple two-phase flow simulation is demonstrated. Models for thesource and the atteDuatioDisensor sensitivity function are proposed. and are shown to ac:c:urately describe experimental results.

2. Experimental Apparatus and Setup
A simple apparatus was designed and built to allow a simulated two-phase condition to be created in a vertical fluid column. Materials used in the experiment are as follows:
Glass tube 91.5 x 4.7 em (0.0.) with 0.46 em wall
thickness,
Compressed He gas with regUlator.
0.43 mm 1.0. syringe needle,
Cylindrical rod waveguide sensor,
Broadband amplifiers (nominally 20 dB and 40 dB.
respectively),
AE data acquisition instrument (30 dB internal gain),Rubber plug, ring stand, miscellaneous hardware.
The apparatus consists of a water-filled tube (Fig. 1), open at
the top and with a rubber stopper in the bottom. Pressurized
helium was allowed to flow through a rubber hose connected toa needle. which was inserted either through the rubber stopperor down through the top of the glass tube. The insertion point
could be changed by positioning the needle tip at different
depths in the tube. producing a slight change in quiet fluid
level The void fraction is related to the gas flow rate, and wasvaried by increasing the gas pressure. The steel waveguide AE
sensor was spring-mounted to the tube about 36 em below thewater surface level using the apparatus illustrated in Fig. 2.
Sensor response was fed through the amplifiers into the dataacquisition instrument. yielding nominally 90 dB of gain fromthe sensor outpuL A Dunegan 1032 AE instrument and amachine designed and built at Battelle for the Nuclear Regulatory Commission were used at different times during the tesL
These instruments derived various AE parameters such as signal
amplitude, risetime, event time, etc.. and stored the data for
analysis.

1. Introduction
Acoustic emission (AE) is a passive NDT method commoDly used for process control and to identify and monitor
damage in structural materials. AD ultrasonic receiver coupled
to the surface of a structural component can identify remote
microscopic damage by detecting acoustic radiation produced as
the damage occurs. Acoustic emissions may also be produced
by sources unrelated to material damage, some of which are or
may be useful for process control; i. e., to diagnose operationof mechanical. hydraulic. or electrical systems. One suchapplication is monitoring reactor coolant to determine the void
fraction during two-phase flow. In recent reactor models, suchcoolant may be liquid metal, where "void" fraction is a measureof the amount of coolant in vapor form. This application may
involve temperatures in excess of 1200°C, making standard
methods of flow monitoring unfeasible (Lahey and Moody,1977). Acoustic emission technology has been successfully
adapted to extreme environments, including high ambient radiation levels and temperatures (Hutton et at., 1981). Although
AE sensor components can be made fairly radiation resistant, awaveguide sensor must be used to protect sensor components
from the heat (Friese!, 1986).
Wode reported here is a demonstration of the potential of
AE techniques to monitor and quantify two-phase liquid coolant
flow, which buUds on and complements earlier wode. It has
been shown that cavitation in coolant channels and boilingproduces detectable acoustic emission. Examination of cavitation noise was performed by Saxe and Lau (1968) using primarUy spectral analysis at frequencies below 30 kHz. Relevant tocurrent research was an apparent linear relationship between
small bubble population in the flow, and measured acoustic
signal amplitudes. An experimental loop facility was used by
Leemans et al. (1981) to study flow quality and flow rate withDuring setup and calibration, a significant number ofemissions were detected from bubbles bursting at the upper surface of the water column. Since a high background rate could interfere with measurements, an acoustic barrier was constructedusing a 2-e:m wide band of putty on the outside of the tube about
10 cm below the qUiet water level. supplemented by a rolled
thin plastic sheet inserted inside the water tube, extending fromthe putty level to above the tube top.

Received 1 Man:b 1991. The llU1hors aRl afJUiated with Automation I1Jld
Measurement Sciences Department. Pacific Northwest Laboratol)'.
Ricb1aDd, WA 99352.
Joumal of Acoustic Emission

Volumo 10. Number 314

117~100117-121

Copyriglttelm AcousIic Emissioo GroupGAS CYLINDER\

1
H

AE SENSOR/AMPLIFIERS~

L..Er:E£:l..!!!
~~

Fig. 1 Schematic of the test apparatus. H is approximately 36 em.
of the height of the fluid in the tube in the presence of large
bubbles was rendered somewhat difficult by fluctuating levels.
The behavior of the bubbles suggests that void fraction inthese experiments is depth depeDdeDt, hence the void fraction
estimat.es are values averaged over the entire fluid volume.

/

o

E

Fig. 2 Waveguide aad mounting apparatus. A: spring, B: bolt, C:
metal strap, D: waveguide, E: glass tube.
3. Experimental Results

3.1.1 Experiment 1: With an acoustic barrier of putty and plastic sheet installed near the quiet fluid level, the needle wasinserted through the rubber stopper and the gas flow increased
stepWise. Each flow rate was maintained for 20 minutes and AE
events recorded, while the maximum and minimum fluid levels
were determined to obtain void fraction estimates. Void fraction was defined as f = Ab/(h + ~), where h is the quiet fluid
level, and ~ is the change in level caused by gas injection.
The AE event rate is plotted against estimated void fraction in
Fig. 3.

3J General ObservQtions

The fluid level at a given gas pressure would vary depending upon how close large bubbles were to the surface. The bubbles also appeared to expand with decreasing depth; hence inthis case, void fraction calculated by volume ratio is Dot a
depth-independent quantity. Because of these conditions, therelation between AE rate and void fraction bas some additional
inherent uncertainty, but the general treDd suggests that the AE
rate aad void fraction are closely correlated over the range examined.

Observable bubble activity was in the form of expansion
and oscillation of individual bubbles, collisions among bubbles, collisions between bubbles and the tube wall, and bubble
coalescence. At higher gas pressures and as the bubbles rose,
iDcreasingly large bubbles formed through coalescence andexpausion, culminating in a form of plug flow. Determination

3.1.2 Experimeot 2: For this experiment, the gas pressure washeld constant while the insertion point was raised from an initial position just above the rubber stopper. Again emissions
were monitored for 20-miDute intervals at each needle location
with the acoustic barrier in place. The results shown in Fig. 4
illuslrate a rising event rale as the needle approacbes the sensor

Experiments were performed to examine the effect of void
fraction and relative injection point/sensor distaace on themeaswed acoustic emission. The contribution of aurface-breaking bubbles on the AE and the effectiveness of putty and plastic:
sheeting in teducing emissions from Ibis source were examined.

118

3.1.3 Experiment 3: In oIder to verify the results of experiment 2 and the effectiveness of the acoustic barrier, AE wasmonitored a) with or without the banier with the needle tip
placed 3 em below the water slUface, and b) with the bmier set
up to block emissions except in the vicinity of the sensor.

2.5

g

2.0

a

In part a), the putty ring and plastic were placed as forExperiment 1 to insulate the tube from the bubbles. Ilesu1ts ofthis experiment are shown in Table I, where the emission rates
with no bmier, putty only, and both putty and plastic are
shown along with the background rate obtained without gas
flow. The AE event rate of O.SS evenls/s is seen to be reduced
85CJJ by the putty and plastic, to a value of about three times the
background rate. In part b), the needle was inserted through thebottom plug, ancJ gas flow held CODStant for all measurements.
Table 2 shows the effects of putty and plastic placed so as toblock emission from outside a region extending from about S
em below to 5 em above the sensor location. Two plastic
sheets were employed, one extending from the upper putty ringto the end of the tube and the other from the lower ring to therubber stopper. As shown. the use of putty and plastic reduces
emission only by about 20CJJ, or 0.7 events/so

0

•

~

•
C
•
>

a

1.5

•

a

~ 1.0
II:
III

c

0.5

0.0~--"""---r------T""'"---'---i3o2Void FracUon 1l.

Fig. 3. AE rate vs. mean void fraction.
3

Table 1 Acoustic barrier effecls on near-surface AE
Condition~l:0Bare Glass
Putty
Putty + Plastic
Background

2

co

•
•

.

I II

Events/Second

0.53

0.12
0.08
0.03

Go

e•

•

>

Table 2 Acoustic barrier effects on AE

III

ConditionoBare Glass
Putty (rop)Putty (rop &. Bottom)
Putty + Plastic
Background+-.. . . . . . . -....-r.....---......"""T---......-..-,r-"-..........~15.

.5

0

5

10

Scaled Inlectlon Polnt·Sensor DIstance

Events/Second

3.15
3.06
2.72

2.45
0.03

Fig. 4 Calculated and experimental event rates vs. needle tipsensor distance. Distance scale factor is about 4.45:1.
The greatest drop in event tate in b) was obtained when thelower putty strip was applied, indicating that most of thedetectable emission was produced below the sensor position
where the bubble size is smaller, and the density of bubbles
greater. If the result from a) yields a good estimate of the barrier
effectiveness (e.g. about 85CJJ), then over half of the emissiODS
detected in b) originate within S em of the sensor, and most ofthese originate below the sensor position.

position from below (positive r value), a sharp drop in ~ ~before the needle tip reaches the sensor, and a low eDUSS10n
rate dropping towards background level as the injection point
passes the sensor position and moves upward. Although the
emergence of bubbles from the needle tip was originally considemJ a potential AE source, from the results of this experiment, such cannot be the case if the sensor response is symmetric. The drop in AE rate as the needle tip passes the sensor
position also suggests that the seosor sensitivitr drops off
rather sharply with distance. The observed behaVlOr ean thenbe qualitalively explained if a) only emissions from bubbles inthe vicinity of the sensor are detected, and b) if smaller, mon:densely packed bubbles occurriDg closer to the injection point
produce more emissions per unit volume than luger bubbles atgreater distance from the needle tip.

4.

AnalysIs
The results of Experiment 1 illustrate the potential of AE

to monitor void fraction. To analyze the uncertainties in theordata of Fig. 3, lID estimate for the variancein the void f'rac.
tion may be obtained in the usual way by averaging over a
Taylor expansion and neglecting higher oIder terms, giving

119

0.8

1.2

-----

.Iph.:

1.0

iii

0.6
D

0.8-

•

';j

i

•

'a

II

II:

iQ. 0.4

0.6

~

0.2
0.4
0.6
0.8
1.0

e

•:.

C

W

a
0.4'
0.2

iii

0.2

.

0.0
·10

~

-8

-6

-4

-2

.
0

.

2

4

6

0.0
-10

10

8

·8

-6

-4

-2

0

,

2

4

6

8

10

Fig. 6 a) Effect of a on dEldt with cr co 1.0.

Fig. S AE event rates for a delta function sensor response andexpouentially decaying source function.

0.4

cr?- ... (dfldh)201? + (dflMb>2 04h2

(1)

where the fluid level h and the change in fluid level 4h aretreated as independent variables. Values for 4h ranged fromabout 0.5 to 10 em, with uncertainties ranging from near zero
to -3 cm. Unc:ertaiDty in the void fractioo is therefore about
one to two percent at larger void fraction.

o.~

D.3

1.!l

dt

Jo

iii

0.1

'_-\'""'"'1"'"'i--J.:"'~:;"r-....,.--.."';:~~iii;:::-iifi~iiiili~

o.o~

(2)

-10

(f{2;

10

·8

·6

-4

-2

0

2

4

6

8

10

Fig. 6 b) Calculated dFJdt for various a vaIues with a .. 1.0.

By completing the square, equation (3) becomes

g = Do [exp(~)212 - ar] xdt

It seems reasonable to assume that the sensor respoose issymmetric and decreases at greater soun:elsensor distances. ID\he extreme, p(x - r) ~ 5(x - f), which can be substituted into
equatioo (2). By defmiDg sex) as an exponentially decaying
function with an appropriate constant, the curve obtained fromequatioo (2) bears qualitative resemblance to the experimental
data (see Fig. 5). A reasooable startiog point for the systemmodel thus appears to be

dt

0.2

"0

Here x '" 0 corresponds to the positioo of the needle tip where
the gas is injeeced. H is the fluid surface level, and r is the position of the sensor. Since the seasor was fiXed, the distaDc:ebetween it and the fluid surface level was approximately coostaoth .. H - r.

da =...J!L.. (" exp(-ax)exp[-(x - r)2I2cr)dx + Eo

2.0
2.!

;

To describe the results of Experiment 2. it is oecessary to
posbllate both a source model sex) and a sensor !eSpouse andauenuatioo model p(x). The sensor !eSpOuse is defmed as the
frac:tioo of events detected from a constant rate point source
located a distance x from the sensor. The event rate can then be
written as the sum of the cootributions from each volume
increment in the fluid tube, plus the background rate:
dE = (" s(x)p(x - r)dx + Eo

1.0

[Q(a.cr - rio) - Q(aa + blo)] + Eo

where
dE =

dt

-1-fn;;.

exp(-zzl2)dz

(4)

(5)

Figures 6a and 6b illustrate the behavior of the family of cu!yesobtained by varying a and (J in equation (4). using Do c I, Eo '"0, with r on the interval {-lO,IO}. The f values can be linearly
scaled to the experimental data. Letting r' = la, and othCJ
quantities in the Dew scale denoted by the primes,

(3)

and

120

"-~=aa-~~~(o.'o')lt2 - 0.'( =(o.o'lt2 - cxr(6b)

The form of the event rate equation is similar to that usedby Beattie (1982) although in the present case the rate of occurrence of discrete events rather than the sigual amplitude is measured, and only one sensor is used. A Gaussian function wasused here for the sensitivity function since it provided die Decessary symmeay. An advantage offered by the present experiment over most acoustic emissioD experiments was Ihat the
behavior of the sources could be observed, and predictions of
the model verified directly.

from which
0.'= fJ.Ik(6c)0'''' ko.

Comparing the behavior of these curves indicates that it
may be necessary to provide an ordinate transformation. Sincer is the position of the sensor relative to the needle tip, thezero source-seusor distance from experiment should coIreSpODdto r '" 0 in the theoretical curve. One cannot, therefore. simply
shift the axis by redefining r as (r + CODStaDt) in equation (4) inorder to improve the fiL However, it is easy enough to showthat adding a term to the argument of Q is equivalent to shifting
the origin of the scale on the tube, and therefore the event rate
disbibution. In equation (4), r - ~ is substituted for r in theargument of Q and by working backwards, equation (3) isrecovered if x is redefmed as x + ~. By again representing p(x r) as a delta function and integrating equation (3), the position
which produces the highest event rate along the gas bubble
train is foUDd to be b above the needle tip.

The sources of detected emission appear to be the interaction of the bubbles with the blbe waIl or turbulence caused bypassage of the bubbles, and bubble rupture on the free surface of
the water. Some process associated with the production of bubbles, initially considered the prime candidate for the emission
source, was not detectable to any appreciable degree with thepresent experimental seblp. The degree of localization of thesensor response was somewhat surprising, since s in Ihe scaleof the experiment is only about 3.5 em. This result was supported by Experiment 3, however. The results of Experiment 3
also indicate that detected AE is traDsmiUed via the tube walls
rather than through the fluid.

An iterative estimation procedure was wriucn to solve
equation (4) by comparing experimental data wiIh values calculated using incremented values of 0.. 0 and ~. Values for Q wete
calculated using a slight modification of a series expansion forP(x) given in Abramowitz and Stegun (1965). The experimental
data was linearly scaled to fit in the interval {-10,10} using afactor of 4.45. Au approximate background was subtracted fromthe data, and no estimated by dividing the largest experimental
data point by the calculated value at the same position. Aftervertically scaling the remaining data, the difference between
calculated and experimental values was found, and the sum of
the absolute differences compared to fmd the lowest value. A
comparison of the experimental data and the curve calculated
for 0. '" 0.29, 0 '" 0.68, ~ = 1.76,
0.60, and 110 = 5.02 isshown in Fig. 4. It should be noted that good fits were notobtained for values too far from the ones used above. The
source and sensitivity functions can now be estimated:

Acknowledgment
The authors would like to thank: Zen Autoniat of PNL andthe U.S. Department of Energy for providing funding to obtain
the experimental data. Pacific Northwest Laboratory is operated
for the U.S. Department of Energy under Contract DE-AC0676RLO 1830.
References
M. Abramowitz and I.A. Stegun, cds. (1965), Htmdbewlc lor
Mallu!matit:lJl Functions. Dover Press, New York.

Eo '"s(x) .. 5.02exp[- 0.058x)(7a)p(x) '" exp[- (x - r'l/18.31)(7b)

A.G. Beattie (1982), "An Acoustic Measurement of Boiling
Instabilities in a Solar Receiver," J. Acoustic Emission, 1(1),21-28.
M.A. Friesel (1986), "Acoustic Emission Source Identification
Using Long-Waveguide Sensors," NOT 1nL, 3(19), 203-206.

In the experiment, position was measured from the needle
tip where the gas was injected. By rescaling ~, the analytic
result indicates that the rate of maximum event production isroughly 8 em above the needle tip, where from observation the
bubbles begin to interact directly with the blbe wall. In Experiment 3, it was noted that the acoustic barrier reduced surface AE
to about 0.8 events/I, only slightly more than the backgroundused above. There is a tacit assumption that the upper surface
conlribution is roughly constaDt despite insertion depth.
5.

P.H. Hutton, M.A. Friesel, and J.P. Dawson (1987), "Detectionof Cracking in Vitrified Nuclear Waste Using Acoustic
Emission Methods." Report prepared for Kemfoncbungszentrum Karlsruhe GmbH Karlsruhe, FDR. Pacific Northwest
Laboratory, Richland. WashiDgtoD.

R.T. Lahey, Jr. and SJ. Moody (1977), The Thermo-Hydraulics
of a Boiling Water Reactor, American Nuclear Society. La
Grange Park, llliDois.
D.V. Leemans. J.A. Baron and a.A. Kupics (1981), Advancu
in Acoustic Emission, ed. H.L Dunegan and W.F, Hartman,
Dunhart Publishers. Knoxville, pp. 323-329.

Discussion

It is encouraging that the analytic results provide a good
fit to the data, since the initial choices for s(z) and p(z) weremade on the basis of fairly limited observations. Steps which
would enable Ihe model to be verified or expanded upon are toobtain a dense data set, and to obtain or write a least-squares
routine for finding the best f11 of the variables.

R.F. Saxe and L.W. Lau (1968), "Cavitation Noise in Nuclear
Reactors," Nuc. Eng. and Des., 8. 229-240.

121

In Memoriam

Dr. RAYMOND W. B. STEPHENS(1902-1990)
would rummage in the pUes and unerringly extract something relevant to the discussion in band. Meanwhile, outside the offices. the other students and overseas visitors
were always tapping on the door. "I'm a little pressed
today," he would gently excuse himself when one of his
students was trying to get his thesis read. "Perhaps we
could do it on Sunday at, say. three o'clock?"

We are sorry to report the death in London of Dr. R. W. B.
Stephens at the age of 88. Longtime Reader in Acoustics at
Imperial College. London. Dr. Stephens was the leading
light of acoustics in England and was well known all overtbeworld.
In his long and distinguished career Dr. Stephens trained a
prodigious number (>110 Pb.Ds) of postgraduate sbldents

While he left us to our own devices as far as the technicalities of our projects were concerned, Steve always pushed us
to attend technical society meetings. to speak at them andto publish. All his students had to give a presentation totheir peers quite early in their reseaJCh programs, a nervewracking prospect for an inexperienced student but aninvaluable icebreaker for later public speaking. Steve wasvery active in the Instiblte of Physics and pushed me into
organizing. with the Instiblte, the first conference in Europe
on acoustic emission which was held in 1972. It was theirmost successful topical conference of the year. attended byover 180 people. Out of that conference the European
Working Group on Acoustic Emission was born. Long
after he retired, Steve was still asking people to write
papers. give presentations or organize sessions for the cooferences and journals and professional associations he wasinvolved with - Acustica, Ultrasonics. the International
Congress of Acoustics, the Instiblte of Acoustics and many
more.

from many countries, who have made their JDaJk in acoustics all over the world The full scope of these activities has
been reported more completely by Dr. E. A. Shaw in the
Journal of the Acoustical Society of America.
In acoustic emission. Dr. Stephens' Ph.D. SbldenlS included
Adrian Pollock (1964-68). Roger Hill (1968-72). and R. S.

Geng. He also supervised the PhD. project of Brian
Woodward (1965-69) who later undertook the first acoustic
emission work in Australia. The fIrSt project in acoustic
emission was sponsored by Roy Sharpe's nondestructive
testing group at the Atomic Energy Research Establishment, Harwell stimulated by Roy's contacts with Battelle
NortbwesL Dr. Stephens secured the grant and Adrian
Pollock, newly graduated from Cambridge, joined the
Acoustics Group to perform the work. Graham Curtis.
another of Dr. Stephens' sbldents, took the knowledge ofthis project back to Harwell and initiated there the work in
AE that was evenblally taken up by Wadley. Scruby et at.
Meanwhile at Imperial College, Roger Hill moved in afcer
Adrian Pollock's graduation, and started under Dr. Stephens
the train of work that he later carried to Robert Gordon'sInstitute of Technology and Nottingham PolyteChnic.

Steve did not hesitate to seek publicity for the work of hisgroup, and cheerfully blmed a blind eye to the proteslS thatthe publicity aroused. The major disaster of 1968 was"Ronan Point". the collapse of a large apartment block in
the London subUl'bs. The newspapms made passing mentionof the fact that residents of the block had been -bearing
strange noises" for weeks before the collapse. "We should
call The Times," said Steve, and we did An article duly
appeared extolling the potential of AE to prevent such failures. Of course, this article brought in several valuable
enquiries for more information. However. we soon received
a stiff reprimand from our government sponsor. AERE
Harwell, who took the brunt of complaints from other ministries who were embarrassed by the implications of ourreporL I was summoned to appear before George Brodie, ourcontract administrator. who dutifully rapped me over the
knuckles but also let me know that personally. he had nocomplainlS about our action.

-From the beginning. Dr. Stephens was intrigued withacoustic emission as he was with all aspects of acoustics;
in particular he recognized from the start the relationshipbetween AE and earthquakes. and the possibility of locating
AE sources based on time difference measuremenlS. (AAP)

Adrian Pollock recalls:
Students from all over the world were crammed into a large
upstairs laboratory and an even larger, very tall basementlaboratory in which he had his resident technician build amezzanine floor out of nexion scaffolding to double theworking space. His workload was enormous and his desk
was legendary. About twelve feet long. it was completely
covered in papers to a depth of about twelve inches. He

AIl of his students and many others wUl remember Stevewith affection for his warm and gentle nature, the tremen122dous breadth of his knowledge, his total commib1lent toacoustics and its people, and the special influences he hadon their lives.

Robert Chivers writes:

The sheer statistics of his work are extraordinary. Raymond
had a relatively modest publication list - some nine boob
(the last was published when he was 84 years old) and 60 70 papers which belies the reality. Much of the work of his
early postgraduate students did not bear his name (save in
the acknowledgments). Indeed, it is these students thatbegin to reveal the nature of the man. He supervised almost
100 postgraduate theses at the University of London, andexamined over 120 doctoral theses from all over the world.
Not only have many of these students held eminent positions in a wide variety of acoustics, but it is reputed to be
hard to find a COlDltry of the world where at least one of thisex-students does not reside.

Raymond Stephens was a classical physicist in every sense.
Apart from a brief period in India during his pre-schooldays. all of his life was lived in South West London - avantage point which enabled him to maintain an extraordinary level of activity in professional organisations and in
hospitality to dozens of overseas visitors. After auending
Emmanuel Schoo~ he studied Physics at Imperial College
from 1921 - 24. before Heisenberg's uncertainty principle
was enunciated! 'Ibis was followed by 45 years' service toImperial College as Demonstrator, Lecturer, Reader andSenior Turer. Retiring at the age of 68, he worked foranother 15 years at Chelsea College as a Visiting Research
Fellow. Raymond's concern for his students - and indeed for
all with whom he came into contact - was well recognised
in many quarters. not least by the students of Imperial
College who elected him to Honorary Membership of theUnion (only the second staff member to be honoured in thisway).

The growth in activity in acoustics in the UK after theSecond World War gave Raymond the opportunities which
only someone of his breadth of interest, depth of humanunderstanding, and clear vision, could fully exploiL A former member of the Acoustics Group of the PhysicalSociety. Raymond became Committee member in October1950 and Joint Honorary Secretary in March 1952, a post
which he filled actively (to jUdge from the Minutes) for anumber of years. He was a prime mover in the founding of
the British Acoustical Society and the British Society of
Audiology in the 1960's. In the early 1970's he was one ofthe principal architects of the forging of the Institute of
Acoustics out of the conjunction of the British AcousticalSociety and the Acoustics Group of the Institute of Physics. This was achieved just in time to host the ICA in
London in 1974, Raymond serving as Chairman of theOrganising Committee (as well as being Founding President of the Institute).

The first few scientific papers published (mainly in the
Philosophical Magazine in the 1930's) give some clue tothe career that was to flower later. While concerned mainlywith the measurement of thermal properties of materials,
they range widely. and have a key element - that of a dedication to the teaching of experimental physics. For 19 years
Raymond was Chief Examiner in the Practical Physics
Examinations for London University. In addition to being
Chief Examiner in A level Physics for the University of
London, he was also me Physics Examiner for the Instituteof Plastics. the Royal Aeronautical Society, the British
Optical Association, and External examiner at the Universities of Ibadan (Nigeria). Malawi, Kumasi and Khartoum(Sudan). In his 'spare' time he held part-time evening
lectureships in a number of colleges and polyteehnics in the
London area. However, Raymond clearly saw a broader
range of professional involvement as essential. He servedon the Council. the Education Committee, the TechnicalPapers Committee, the Library Committee and the Nondestructive Testing Group of the Physical Society. He wasa Fellow of the Iustitute of Physics, and sometime Secretary and Chairman of the Acoustics Group of the Institute.

Raymond enthusiastically supported the formation of theFederation of the Acoustical Societies of Europe, and wasclosely involved with the formation of Espanola deAcustica and the Grupo de Acusticos Latino-Americanos.
For thirty years he served as English Editor of 'Ultrasonics'
and one the Advisory Board of the Journal of Sound andVibration. He was a consultant to the Admiralty Underwater
Establishment for eighteen years prior to his retirementfrom Imperial College, and for fourteen years was an
Honorary Physicist at the Institute of Laryngology andOtology at London.

Sadly we report also the death, a little earlier. of Dr.
Stephens' wife Ellie, a lady of exceptional intelligence andcharm to whom he was totally devoted. Many workers in
the field of acoustics will remember the warm hospitality of
their home in East Putney. Their son survives them.

With this intense activity with organisations in different
countries, it is perhaps surprising that Raymond's research
activities still flourished. In addition to working in most of
the main areas of acoustics (architectural, noise control,etC.) Raymond's major work was in Physical Acoustics andultrasonics - but even so, the list of areas researched under
his guidance is exttaordinaIy. It includes: sound propagationin tubes, rods, plates, sheets, anisotropic solids, liquid metals. porous media and liquid aystals; finite amplitude waves
in gases, liquids and solids, sonoluminescences, hypersonicwaves in liquids. infrasonics, ultrasonic imaging, surface
waves. acoustic emission in stressed solids, modal regeneration, vibration isolation. viscoelasticity and photoaeoustics.

Renwtably, Raymond's interest in Acoustics, for which heis pre-eminently well-known, was a relatively late developmenL If dates can be identified for such evolutionary processes, it would probably be 1947 which marked the start ofhis keen activity in Acoustics, (a date which also marh thefoonation of the Acoustics Group of the Physical Society).

Recognition ofRaymond's achievements was wide. He waselected a Fellow of the Acoustical Society of America in1967 and in 1977 was the rust British scientist to receive123potential of AE for testing a wide range of materials.

their Gold Medal. The British Acoustical Society awarded
him the Rayleigh Gold Meclal in 1971, the French
Acoustical Society (Gala) their Silver Medal in 1973, be
was made a Doctor Honoris Causa of the University ofCordoba in Argentina in 1972, and was elected a Honorary
member of the British Society of Rheology, the British
Society of Audiology. the Institute of Acoustics (UK), theSociedad Espanola de Acustica, the Grupo de Acusticos
Latino-Americanos. the Polish Acoustical Society, and the
Ultrasonics Society of India. One of the most surprisingfeatures of Raymond's career was the relative lack of recognition accorded him by the British establishment This may
have been due in part to his modesty and gentility, but it issad that we in England did not better honour the man. who
was almost certainly the most eminent British acoustician
since LoJd Rayleigh.

Raymond Stephens was a quiet man who succeeded by thefriendships he made with all he met He was not one tooffend others and took offense when faced with what heregarded as bad manners in others. His quiet manner was, nodoubt, not well regarded in some quarters, and this accounts
for his lack of recognitiOD is his own COURtly. Ironically.
for those friends from abroad who expected an EDglishman
to be a gentleman he was always that. For those interested
in acoustics rather than science politics his contribution
was reganled with JeSPecL
He made an impact on generations of students and his
impact in the field of acoustic emission is evident in that
his research students in AE are all still active in the technology over twenty years on. His longevity meant that bebrought to the latter part of the twentieth century scientificvalues from before the rust world war and an earlier age.
His personality bas marked the lives of many and in a worldof increasing materialism and aggression we can perhaps
remember the importance of a milder and more gentlemanly
way of conducting our affairs.

No tribute to Ray Stephens would be complete without
mention of the activity of which he was as passionately
fond as his physics, namely Association Football. In addition to being a lifelong supponer of Fulham FootballClub, Ray was a keen participant at different times and indifferent ways in the Association Football Club of Imperial
College, of which he was President and a life VicePresident He was also a Vu:e-President of the University of
London Football Club, and of Ulysses (the University of
London Old Students' Football Club).

Footnote by Roger Hill
I recently obtained a copy of Imperial College "Calendar"for 1935-36, which gives information on forthcoming
activities at the college during the following academic year.
At this time Physics courses were still dominated byClassical Physics and listed are the courses to be taught by
RWBS. just at the beginning of his long academic career.
The courses to be taught were, magnetism and cunent electricity and a problem solving class, but no indication of thewide ranging acoustic work to follow after the second world
war. However, within the pages of the Calendar we do see
foreshadowed, an indication of what was to come. The
Physics department had an academic staff complement oftwenty, with four professors (UK style), of whom the most
famous was perhaps G. P. Thomson, who was responsible
for confirming the wave nature of electrons and shared a
Nobel Prize with Davisson in 1937. Perhaps more relevantto the later work. of RWBS was Emeritus Professor within
the department, The Lord Rayleigh M.A., SeD.. FRS.
This man was the son (1) of the 3rd Baron Sttutt, Lord
Rayleigh whose book published in 1878 did so much toprovide the theoretical foundations of acoustics. Rayleigh(the son) had written a biography of his father and DO doubt
visited the college from time to time and had an influenceon the future direction and work of RWBS. but it was not
until after the war that his interest began to take shape, inwhat was no doubt a restructure college.

In all his endeavours. Raymond was fully supported by hischarming wife, Ellie. who predeceased him by a few
months. Raymond was in very sense one of that rare breed,a gentleman and a scholar. but rust and last a friend.

Roger Hill, Associate Editor Europe. Journal of
Acoustic Emission writes:

Raymond Stephens was one of the pioneers in acoustic
emission in the world and one of the first to study the phenomenon in the UK -but this was only one aspect of a wide
interest in the field of acoustics. As Dr. Chivers states, be
was a classical physicist: he studied acoustic phenomena of
which AE was just one and the projects within his laboratory at Imperial College covered topics such as low frequency vibration work, acousto-optic phenomena, room
acoustics, underwater ultrasound, acoustic emission andattenuation studies of high frequency ultrasound. His pr0jects spanned the frequency spectrum and crossed the bowdaries into other areas ofphysics.
He was not essentially an NDT physicist but he was one of
the earliest to recognise the potential of AE, before theready availability of commercial instrumentation and saw
its potential as a method of materials evaluation and NDT.
He was possibly the rust to use amplitude analysis of AE
signals, the first to see the need to model AE sources andthe first to detect the Felicity Effect. He predicted the acoustic equivalent of magnetic Barkhausen noise before it wasreported in the literature although his idea was not tested.
presumably due to lack of funding. He also realised the

Cover photograph: courtesy of Roger Hili.

124

INDEX to Volume 10, 1991/92
Author Index
D. L. ArmentroutFrank C. Beall
Andrew R. Blysn
S.J. Bowles
Franklin R. Breckenridge
Nancy Brown
S. H. Carpenter
Tunothy G. Crowther
C. E. D'Attellis
Jack F. Dawson
V. G. Ruiz de Argandofia
L. M. 5uUez del Rio
F.Dusek
R.M.Esben
T.Flynn

Michael J. Friedel
Marlc A. Friese!
T.Fujiwara
JuosGereb
5teven D. Glaser
C.M.Grossi
H. R. Hanly, Jr.
R. W.Hanis
F. P. Hassani
K..Hata
C. R. Heiple
H.HickHiroshi Hikosaka
KyojiHomma
Takaka Inaba
T. Ishida
KoJoMusa K.. Jouaneh
5hahla Keyvan

Ron King
N. Kinoshita
T. Kishishita
TatsuoKita
K.Kitano
F.M.Kustas
E.LandisMarcel F. Leach
Richard Lemaster
J. F. Mccardle
K.. Michihiro
Kazuya Miyano
Hiroyuki Miyalake
Waldemar Molinski
M.Momayez
M.Montoto

Yuji Murakami
5. Naemura
M.Nakamura
5yoozou Nakamura
Priscilla P. Nelson
S. Nishikawa
Yoshitugu Ohigasbi
Masayasu Ohtsu

97,103
83

549
49
43

71
97.10371
13

Hisanori Otsuka
c.Ouyang
Peter Pellionisz
L.V.Perez
S. Pilecki
Stefan PoliszkoThomas M. Proctor, Jr.
Jan Raczkowski
G. L. Rigby

117

535
535
1

535
559

LRoman

S77
117

Gottfried A. Rubin

D.RubioJ. E. Ruzzante
Koji Sakai
C.M.Scala
1. 5. 5chwartzberg
S.P.ShahHisatoshi Shimada
Noboru Sbinke
J. Siedlaczek
F.Simacek
M. Tanaka
Richard E. Thill

563
19
51
535
61

529,55961
563

97,103
67

513
3S
590
542
55S

AdrianP.Wade
N. Wakabayashi
J. W. Whittaker

B. J. S. Wilkins

83
91
91

H. W'iller
E. W'mter
B.R.A. Wood
H. Yoshioka
Hironobu Yuki
F.Zeides

542
555
590
542
97
597
518
83

49
S63
S90

S13
107
61

S35
1-1

590

SSS
555
513
SI

555
25
i (112)

S13
S97
19

13
1

107
43

107
S22

31
S18
13
13
SI04

49
97
597

5104
25
1

67
SSS
577
71

542
113
S22

67
67
S29,SS9
S63

35
31

Contents
Number 1/2
Current Research and Future Trend of AE Applications to Civil Engineering and Geological Technology
Masayasu Ohtsu, Topical Editori
Variety of Acoustic Emission Waveforms Produced by Discrete Crack Growth in Rock
SteVen D. Glaser and PriscillaP. Nelson
SI-SI2Estimation of Maximum Stress in Old Railway Riveted I-GiJder Bridges using Acoustic Emission Signals
SI3-S17
Hisanori Otsuka, Hiroshi Hikosaka, Hiroyuki Miyatake and Syouzou Nakamura
Another Look at Booming Sand
Marcel F. Leach and Gottfried A. Rubin
S18-S21
Expected Acoustic Emission from Around a Shaft in Intact Rock
BJ.S. Wilkins and GL. Rigby
S22-S28
Acoustic Emission Monitoring during Microseismic Activity Caused by Mine Subsidence
529-S34
Brian R. A. Wood and Robert W. Hmis
Acoustic EmissioDlMicroseismic Activity Monitoring of Salt Crystallization for Stone Conservation
M. Montoto, RM. Esbert, LM. S1W'ez del Rio, V.G. Ruiz de Argandofta and CM. Grossi
S3S-S41
Acoustic Emission Monitoring during In-sibl Heater Test ofGranite
S42-S48
T. Ishida, K. Kitano, N. Kinoshita and N. Wakabayashi
Using Acoustic Emission Testing in Seepage Investigations
Andrew R. Blyscra
S49-SS4
Acoustic Emission ofPeneuation Experiments to Judge Soil Condition
SSS-SS8
S. Naemura, M. Tanaka, S. Nishikawa, M. Nakamura, K. Jo and T. Kishishita
A Laboratory Investigation of AE from CoalR. w. Harris, B. R. A. Wood and T. Flynn
S59-S62
Determination of the Initial Stresses on Rock Mass using Acoustic Emission Method
K. Michihiro, K. Hata, H. Yoshioka and T. Fujiwara
S63-576
U.s. Bureau of Mines Research on the Kaiser Effect for Determining Stress in Rock
Michael J. Friedel and Richard E. 1bill
877-S89
Evaluation ofJoint Properties of Anti-washout Underwater Concrete by Acoustic Emission Measurement
Kazuya Miyano, Tatsuo Kita, Yuji Murakami and Takako lnaba
S90-S96
Automated Determination ofFirst P-Wave Anival and Acoustic Emission SOurce Location
S97-S103
E. Landis, C. Ouyang and S. P. ShahApplication of Acoustic Emission Techniques in the Evaluation ofFrost Damage in Mortar
S100-S109
Hisatoshi Shimada and Koji Sakai

Number 3/4
Acoustic Emission of the 4SHNMFA Scruetural Steel during Low-Cycle Fatigue
J. Siedlacz.ek, S. Pilecki and F. Dusek
1-11
Parameter Estimation in Acoustic Emission Signals
C. E. D'Attellis, L. V. Perez, D. Rubio and J. E. Ruzzante
13-17
Acoustic Emission Technique at ProofTests of Nuclear Pressure Vessels in Hungary
PeterPellionisz andJ4nos Ger4b
19-23
Effects of Wave Velocity Change on Magnetomechanical AE in Sintered Iron
Noboru Shinke and YOShiblgu Ohigashi
25-29
Origin of Acoustic Emission in Naturally Aged Aluminum-Uthium Alloys
P. Zeides and L Roman
31-33
Analysis of Artificial Acoustic Emission Waveforms Using a Neural Network
35-41
Hironobu Yuki and Kyoji Homma
Source Force Waveforms: The Use of a Calibrated Transducer in Obtaining an Accurale Waveform of a SOmceThomas M. Proctor, Jr. and Franklin R. Breckenridge
43-48

1- 2

C. M. Scala, J. F. McCardle and S. J. Bowles
Maximum Curvature Method: A Teclmique to Estimate Kaiser-Effect Load from Acoustic Emission Data
M. Momayez. F. P. Hassani and H. R. Hardy, Jr.
Acoustic Emission Measurements on Bridges
H. Hick, H. Willer, E. Winter and F. Simacek
Acoustic Emission Monitoring of the Sensitivity of Chemicals to Impact
TunothyG. Crowther, AdrianP. Wade and Nancy Brown
Study of Acoustic Emission Generation in Sliding Motion
Musa K.. Jouaneh, Richard Lemaster and Frank C. BeaD
The Role ofAcoustic Monitoring as a Diagnostic Tools in Nuclear ReaclOJ'S
Sbabla Keyvan and Ron King
Acoustic Emission Produced by Sliding Friction and its Relationship to AE from Machining
S. H. Carpenter, C. R. Heiple, D. L. ArmenbOUt, F. M. Kustas and J. S. Schwartzberg
Comments on the Origin of Acoustic Emission in Fatigue Testing of Aluminum Alloys
C. R. Heiple, S. H. Carpenter and D. L. Annentrout
Acoustic Emission ofWood during Swelling in Water
StefanPoliszko, WaldemarMolinski and Jan Raczkowski
Characterization of the ASL Parameter
J. W. Whittaker
Acoustic Emission from Bubbles in a Wau:r Column
MaIk A. Friesel andJackF.Dawson

49-60

In Memoriam, Dr. Raymond W. B. Stephens (1902-1990)

122-124

61-65
67-70
71-82
83-89
91-95
97-101103-106
107-111113-116
117-121

CoDl'erences and Symposia
Progress in Acoustic Emission VI, Proceedmgs of The 11th International AE Symposium

36th Meeting of Acoustic Emission Working Group

12, 18,24,30,34
42, 66, 90, 96, 102

1993 SUBSCRIPTION RATE for Volume 11
Base rate for one year (four issues, Mar. - Dec.)
S 96.00
Add Postage of
U.s. - Book rate
S 8.00
Allothm - Bookrate
S 11.00
U.s., Canada - First class$ 17.00
Western Europe - Air mail rate
$ 25.00
All others - Air mail rate
$ 30.00
Add SS without payment upon order. Payment must be iD U.S.
dollars drawn on a U.S. bank. Bank traDsfer accepted to the A.B.
Group account (Account No. 080-03(16470) at
SumiJomo Baok of California
San Fernando Valley Office15250 Ventura Blvd
Sherman Oaks, CA 91403-3262
Notify us of such a traDsfer as the baok caanot sometimes give us
the Dame of the paya'. One volume of four issues per year.1Ddex
iDcludcd ill the Oct.-Dec. issue. Back issues available at $SO pervolume for Vols. 1-3. $90 per volume for Vols. 4-8, $96 pervolume for Vols. 9-10. US surface postages $8 per volume. For
Canada and overseas surface delivery, add $11 and for air mail,add $30 per volume (adjusted for multi-volume order). All orders

Fourth International Symposium on Acoustic Emission(rom Composite Materials, July 27·31, 1992, Seattle,
WA

Fourth Inremational Symposium on Acoustic Emission
from Composite Materials (AECM-4) was beld at Holiday
Inn Crowne Plaza in downtown Seattle with about 80
participants from 13 countries and areas. AECM-4 wasorganized by CARP with Dr. Burke Dykes of Boeing
Commercial Airplanes as Chairman, Dr. D.R. Smith asPaper and Program Chairman. A one-day, pre-symposium
educational seminar was also conducted. The Symposium
opened with a sad news that Dr. Daniel Valentine of Ecoledes Mines de Paris, who served as the Paper and Program
Chairman for AECM-3 in Paris, France, in 1989 passed
away earlier in the year. He has actively studied AE
behavior in connection with the failure mechanisms/criteria
of fibeNeinforced composite materials. Two keynote
addresses by Prof. T. Kishi (Microfracture analysis withAE) and by Dr. P. Fleischmann (AE and rheologicalbehavior) as well as nearly SO papers presented contributed
to further advances of this field. Proceedings volume isavailable from ASNT (Catalogue No. 1335).

should be sent to

Acoustlc EmIssion Group308 Westwood Blvd., Box 364
Los Angeles, CA 90024-1647 USA
Editor-Publisher
Editorial AssistantCover Design
Produc:tion

Kanji Ono (310) 825-5233
Barbara Brooks
RobiDWeis%
Pace Publications

PublicalioD Date ofThis Issue: 15 March 1993.
I- 3

AVAILABLE BOOKS ON ACOUSTIC EMISSION
Progress in Acoustic Emission IV (1988) The Proceedings of the 9th International AE Symposium.
Progress in Acoustic Emission V (1990) The Proceedings of the 10th International AE Symposium.
Progress in Acoustic Emission VI (1992) The Proceedings of the 11th International AE Symposium.
These volumes are $ 150 plus shipping. Earlier Proceedings volumes also available. Inquire below.
Proc. Second International Conference on AE (1985) October 1985. about 350 pages $ 50
Proc. World Meeting on AE (1989) March 1989. about 350 pages $ 50
Acoustic Emission - A Bibliograpby with Abstracts (1979) T.F. Drouillard 806 pages. Published by
Plenum Press. $ 135 post paid (surface).
Fundamentals or Acoustic Emission (1979) Ed. K. 000 - microfIche version only 360 pages. $ 10.
Journal or Acoustic Emission, Vol. 1 - 3 (1982 - 84)$ 50 per volume (plus shipping)
Journal or Acoustic Emission, Vol. 4 - 8 (1985 - 89)$ 90 per volume (plus shipping)
Journal of Acoustic Emission, Vol. 9 - 11 (1990, 91/92, 93)$ 96 per volume (plus shipping)

Above are available fromAcoustic Emission Group308 Westwood Blvd, Box 364
Los Angeles, CA 90024-1647
FAX 1-818-368-8309

NDT HANDBOOK (2nd Edition), Vol. 5, Acoustic Emission Testing (1988)
Eds. R.K. Miller and P. McIntire. 6SO pages. $110.25
Tbird International Symposium on AE from Composite Materials (1989) Cal No. 770.
Fourtb International Symposium on AE from Composite Materials (1992) Cat. No. 1335.
Proc:. Fourth World Meeting on AE (1991) Sep. 1991. Cat. No. 1332.
Advances in Acoustic Emission (1981) eds. Dunegan & Hartman 385 pages - $ 68.
Available from Book Depl, ASNT, P.O.Box 28518, Columbus, OH 43228. Also call (800) 222-2768.
First International Symposium on AE from Reinforced Plastics (1983)
Proceedings of the San Francisco Symposium. July 1983. about 400 pages - $ 50 + shipping.
Second International Symposium on AE rrom Reinforced Plastics (1986)
Proceedings of the Monb'ea1 Symposium, July 1986. about 22S pages - $ 6S + shipping.
Available from Publication Sales, SPI, 355 Lexington Ave., New York, NY.
Acoustic Emission (1983) JoR. Mathews 167 pages.
Basic Acoustic Emission (1991) Ian G. Scott, 246 pages. Available from Gordon & Breach, New York.
Tbird Cont. on AElMicroseismic Activity in Geologic Structures and Materials (1982)
Fourth Conf. on AElMicroseismic Activity in Geologic Structures and Materials (1989)
Eds. Hardy & Leighton. Available from Trans Tech Publications, 16 Bearskin Neck, MA 01966.
Acoustic Emission (1980) tr. A.R. Nicoll 385 pages - $ 63.
Available from DGM Metallurgy Information, 150 E. 271h St., New York, NY 10016
Acoustic Emission (1980) R.V. Williams 140 pages - $ 29.
Available from A. Hilger Ltd, Bristol, UK or Heyden &. Sons, Philadelphia.
ASTM-STP's: Acoustic Emission (1972), STP-50S.
Monitoring Structural Integrity by Acoustic Emission (1975), STP-571.
Acoustic Emission Monitoring or Pressurized Systems (1979), STP-697.
Acoustic Emission in Geotecbnical Engineering Practice (1982), STP-750.
Acoustic Emission, Current Practices and Future Directions (1991), STP-I077.
Acoustic Emission Testing or Aerial Devices and Associated Equipment used in tbe
Utility Industries (1992), STP-1139.
Available from ASl'M, 1916 Race St, Philadelphia, PA 19103.
I· 4

Notes for Contributors
I. General
The Journal will publish contributions from all parts of the world
and manuscripts for publication should be submitted to the Editor.
Send to:Professor Kanji Ono. Editor - JAE
5731 Boelter Hall. MSE Dept.
University of California
Los Angeles, California 90024-1595 USA
FAX (1) 818-368-8309
e-mail: ono@seas.ucla.edu
European authors may submit manuscripts directly to:

Dr. Roger Hill. Associate Editor· Europe
Deparlment of Chemistry and Physics
The Nottingham Trent University
Clifton Lane
NOllingham NOll 8NS UK
FAX (44) 602-45-6002
Authors of any AE related publications are encouraged to send a
copy for inclusion in the AE Literature section to:
Mr. T.F. Drouillard. Associate Editor - JAE
EG&G Rocky Flats
P.O. Box 464
Golden. Colorado 80402 USA

The title should be brief. Except for short communications. descriptive heading should be used to divide the paper into its component parts. Use lhe International System of Units (SI).
References to published literature should be quoted in the text citing authors and the year of publication. These are to be grouped
together at the end of the paper in alphabetical and chronologicalorder.
Journal references should be arranged as below. Titles for journal
or book articles are helpful for readers. but may be omitted.
H.L. Dunegan. D.O. Harris and C.A. Tatro (1968). Eng. FracL
Mach.• 1. 105-122.

Y. Krampfner. A. Kawamoto. K. Ono and A.T. Green (1975).
"Acoustic Emission Characteristics of Cu Alloys under LowCycle Fatigue Conditions." NASA CR-134766. University ofCalifornia, Los Angeles and Acoustic Emission Tech. Corp.•
Sacramento. April.
A.E. Lord, Jr. (1975), Physical Acoustics: Principles and Methods,
vol. II, eds. W. P. Mason and R. N. Thurston. Academic Press,
New York. pp. 289·353.

Note some changes in the addresses.

Abbreviations of journal titles should follow those used in the
ASM Metals Abstracts. In every case, authors' initials, appropriate volume and page numbers should be included.

All the manuscripts will be reviewed upon submission to the
Editor. Only papers not previously published will be accepted.
Authors must agrce to transfer the copyright to the Journal and not
to publish elsewhere. a paper submitted to and accepted by the
Journal.

Illustrations and tables should be planned to fit a single column
width (87 mm or 3.3") or a double width (178 mm or 7"). For thereviewing processes, these need not be of high quality. but submit
glossy prints with the fmal manuscript. Lines and letters shouldbe legible.

A paper is acceptable if it is a revision of a governmental or organizational report. or if it is based on a paper published in a conference proceedings volume of limited distribution.

4. RevIew

An abstract of about 200 words is needed for Research andApplications articles. while it should be shorter than 100 words
for other articles.
The language of the Journal is English. All papers should be
wriuen concisely and clearly.

2. Page Charges

No page charge is levied. Fifty copies of off-prints will be sup·plied to the authors free of charge.
J. Manuscript for Review
Manuscripts for review need only to be typed legibly; preferably,
double-spaced on only one side of the page with wide margins andsubmiued in duplicate.

All manuscripts will be judged by qualified reviewer(s). Eachpaper is reviewed by one of the editors and typically sent forreview by members of the Editorial Board. The Board member
may seek another independent review. In case of disputes. theauthor may request other reviewers.

5. Electronic MediaIn order to reduce any chance of typographical error. the authors
are encouraged to submit a floppy disk copy of the text. We canread Macintosh and IBM PC formats. Those who can connect tocomputer networks, such as BITNET. ARPANET. ASIANET,
EARN. NETWORTH. UUCP. CSNET. JANET. OZ. etc. can sendto "ono@seas.uc1a.edu".
6. Color PhotographWe can print color photographs needed to enhance the technicalcontent of an article. Because of the cost. the author is asked to
pay $350 per page.

Contents
B·l1
19-23
25-29

31·33
35-41

43-48
49·60

61·65
67·70
71·82
83·89
91-95
97-101103·106
107·111113·116117·121122·124

Acoustic Emission of the 45HNMFA Structural Steel during Low-Cycle Fatigue
J. SiedlDad, S. Pilecki and F. Dim!
Parameter Estimation in Acoustic Emission Signalsc. E. D'Attd/is, L. V. Pun, D. Rubioond J. E.Ruzzanle
Acoustic Emission Technique at Proof Tests of Nuclear Pressure Vessels in Hungary
Perer Pellwnisz and JfJnM Guib
Effects of Wave Velocity Change on Magnetomcchanical AE in Sintered Iron
Noboru ShinJce and Yoshitugu Ohigashi
Origin of Acoustic Emission in Naturally Aged Aluminum-Lithium Alloys
F. Zejdes and I. Roman
Analysis of Artificial Acoustic Emission Waveforms Using a Neural Nelwork:
Hironobu Yuki and KJoji JlomI/14
Source Force Waveforms: The Use of a Calibrated Transducer in Obtaining an Accurate
Waveform of a Source
Thomas M. PrOClor, Jr. and Franllin R. BreckelVidge
Acoustic Emission Monitoring of a Fatigue Test of an F/A·I8 Bulkhead
C. M. Scala, J. F. McCardk and S.J. Bowks
Maximum Curvature Method: A Technique 10 Estimate Kaiser-Effect Load from Acoustic
Emission Data
M. Momayn, F. P. Jlassani and H. R. Hardy, Jr.
Acoustic Emission Measurements on Bridges
H.lIick, H. Willu, E. Winter and F. Simacd:
Acoustic Emission Monitoring of the Sensitivity of Chemicals to Impact
Timothy G. Crowther, Adrian P. WadI!: and Nancy Brown
Study of Acoustic Emission Generation in Sliding Motion
MllSa K. JoullIIi!h, Richard umasur and Fr(JJlk C. Beall
The Role of Acoustic Monitoring as a Diagnostic Tools in Nuclear Reactors
Shahla Keyv(JJl and Ron King
Acoustic Emission Produced by Sliding Friction and its Relationship to AE from Machining
S. H. Carpemer, C. R.lleiple, D. L. ArmentrouJ, F. M. Kustas and J. S. Schwartzberg
Comments on the Origin of Acoustic Emission in Fatigue Testing of Aluminum Alloys
C. R. Heiple, S.Il. Carpenter and D. L. Armentrout
Acoustic Emission of Wood during Swelling in Water
Stefan Polinko, Waldemar Molinski and Jan Raczkowski
Characterization of the ASL Parnmcter
J. W. Whittaker
Acoustic Emission from Bubbles in a Water Column
MarkA. Friesel and Jack F. Dawson
10 Memoriam. Dr. Raymond W. B. Stephens (1902-1990)
Conferences and Symposia

12 34
42 102

Progress in Acoustic Emission VI, Proceedings of the 11th International AE Symposium
36th Meeting of Acoustic Emission Working Group
Available Books on AE

1-1 - 1-3

Index to Volume 10, 1991192'4