two research and development team s have already expressed
interest in applying and extending the system. The technology
could be extended to other rotary drilling applications, such as
drilling holes for blasting in mining and construction, since
rock strength is an important consideration in efficient
blasting. The application of neural network technology to
strength classification of the material being drilled is new, as
is estimating the strength index class in near-real-time.

References

1, Teale, R. (1995), The Concept of Specific Energy In Rock
Drilling. Int. J. Rock Mech. Min. Sci. & Geo. Abs., Vol. 2, pp. 57-
73.

2. Pryor Knowledge Systems, Inc, (19957). EZ-1 Neural Network
System-Basic. Bloomfield Hills, MI. 74 pp.

3. Specht, Donald F. (1988). Probabilistic Neural Networks for
Classification, Mapping, or Associative Memory. In Proceedings
IEEE Second International Conference on Neural Networks (San
Diego, CA). Vol. 1, pp. 525-532.

4. Reilly, D. L., L. N. Cooper, and C. Elbaum (1982). A Neural
Model for Category Learning. Biol. Cybernetics, Vol. 45, pp. 35-41.

5. MIT GmbH (1996). Data Engine V.i. Data Mining by Intelligent
Data Analysis. Part I: Basics, 81 pp.; Part II: User Manual,
Function Reference, and Tutorials, 262 pp. Aachen, Germany.

6. Kohonen, T. (1995). Self-Organizing Maps. Springer-Verlag,
New York, NY, 362 pp.

7. Tsao, E.C-K., J. C. Bezdek, and N. R. Pal (1982). Fuzzy Kohonen
Clustering Networks. Pattern Recognition, Vol. 27, No. 5, pp. 757-
764,

8. Carmichael, R. S. (1982). Handbook of Physical Properties of
Rocks, Vol. 2. CRC Press, Boca Raton, FL, 306 pp.

9. King, Roger L., and Stephen P. Signer (1994). Using Artificial
Neural Networks for Feature Detection in Coal Mine Roofs. In
Computer Methods and Advances in Geomechanics. Proceedings of
the Eighth International Conference on Computer Methods and
Advances in Geomechanics, ed. by H. J. Siriwardane and M. M.
Zaman (Morgantown, WV, May 22-28, 1994). Vol. 3, Balkema,
Rotterdam, pp. 1853-1857.

10. Masters, Timothy (1993). Practical Neural Network Recipes in
C++. Academic Press, San Dicgo, CA, 493 pp.


Neural Network Technology for Strata Strength Characterization

Walter K. Utt, Spokane Research Laboratory,
National Institute for Occupational Safety and Health, Spokane, WA
wau0@cdc.gov

Abstract

The process of drilling and bolting the roof is currently one of
the most dangerous jobs in underground mining, resulting in
about 1,000 accidents with injuries each year in the United
States. To increase the safety of underground miners,
researchers from the Spokane Research Laboratory of the
National Institute for Occupational Safety and Health are
applying neural network technology to the classification of
mine roof strata in terms of. relative strength, In this project,
the feasibility of using a monitoring system on a roof drill to
assess the integrity of a mine roof and warn a roof drill
operator when a weak layer is encountered is being studied.
Using measurements taken while a layer is being drilled, one
can convert the data to suitably scaled features and classify
the strength of the layer with a neural network. The
feasibility of using a drill monitoring system to estimate the
strength of successive layers of rock was demonstrated in the
laboratory.

Introduction

The Spokane Research Laboratory (SRL) of the National
Institute for Occupational Safety and Health (NIOSH)
conducts research to improve the safety of miners. Roof falls
in underground mines have caused many fatalities in the past.
To reduce the risk of deaths and injuries from roof falls, 1- to
3-m-long bolts are uscd to reinforce the rock. However, the
process of drilling and bolting the roof is currently one of the
most dangerous jobs in underground mining and according to
data compiled by the Mine Safety and Health Administration,
resulted in about 1,000 accidents with injuries a year between
1984 and 1994 in the United States. By using a monitoring
system on a roof drill to assess the integrity of a mine roof, a
roof drill operator could be warned when a weak layer is
encountered. Such a warning could make the difference
between life and death for the operator.

A cross section of a typical mine roof and various types of
roof support, including bolts, are shown in figure 1. Neural

‘Laminated
#mudshale

Figure 1_—Cross se

network technology is being applied to the classification of
mine roof strata in terms of relative strength. That is, meas-
urements taken while a layer is being drilled can be used to
compute the specific energy input and convert these data to
suitably scaled features. A neural network can then be used to
classify the strength of the layer.

Method

A functional strata characterization program has been
developed. The program is designed to interface with an
instrumented rock drill. Torque, rotation ratc, thrust, penetra-
tion rate, and depth of the drill tip are measured and converted
to electrical signals by transducers. This information flows
through interface boards to a computer with a custom data
acquisition program that includes a graphics display (figure 2).

Sensor Le
Drill
Sensor —

L MUX Computer Conversion

ADC | gp| controlled t
LPF data | scatures

acquisition atur
Classifier

Figure 2.—Drilling data flow


The data are smoothed by averaging, and the specific energy
of drilling (SED) is computed. SED is the drilling energy
input or work done per unit volume of rock excavated {.

The SED includes both rotational and translational energy.
Rotational energy is usually much larger than translational
energy. However, if thrust is zero, there will be no significant
penetration, even if the rotational energy input is high. SED
usually ranges from equivalence to about twice the
compressive strength of the material being drilled and is a
useful feature for strength classification if drilling parameters
are within the normal operating range. Consequently, it is
advisable to monitor initial measurements to be certain they
are within the normal range of operation. The SED can be
used in combination with penetration rate to provide a
minimum set of features for the classifier. The other
measurements can be used as supplementary features, if
desired. The computer program block shown in figure 2
consists of three major parts: data acquisition, conversion to
features, and the classifier.

Since strength is to be evaluated while drilling is still
underway, it is necessary to process a subset of data
corresponding to each layer. A subarray that cofresponds to
the layer of material being drilled is converted to suitably
scaled features for a neural network classifier. A pipeline
processing system is an appropriate concept for processing the
data while drilling through successive layers. The laboratory
prototype was designed to be consistent with pipelinc
processing. However, the graphic display results in a delay,
which will require attention in the design of a prototype
suitable for field use.

Two commercial neural network packages (EZ-1 and Data
Engine) were obtained and evaluated. The EZ-1 [2] is a
package of supervised neural network techniques with an
accelerator board. The package contains three alternative
software programs. These are—

1. A probabilistic neural network [3],
The RCE system (Reilly, Cooper, Elbaum), patented as
the Sclf Organizing General Pattern Class Separator and
Identifier [4], and

3. PRCE, which is a combination of the probabilistic and the
RCE programs.

The Data Engine [5] is a package of unsupervised neural
network techniques that contains two alternative software
programs.

4. Kohonen’s self-organizing feature mapping algorithm [6]
and
5. Fuzzy cluster means combined with Kohonen’s algorithm

(7).

All five alternatives appeared to be satisfactory, which is an
indication of the significant advances in neural network
technology in recent years. Due primarily to compatibility
considerations, the learning algorithm of Kohonen
(alternative 4) [5] was selected for the crisp classification of
layer strength. Naturally occurring rock varies considerably
in both composition and strength. Rock strength is often
classified in 32 classes [8], which is adequate for our
proposes. The neural network was trained with data of known
classifications prior to using it to classify new measurements,
The conceptual network is shown in figure 3. The actual
network would, of course, have many more neurons.
Classification output was monitored on a computer graphics
display. For signalling a warning, the classifications were
grouped into three color categories, red for weak, yellow for
medium, and green for strong.

Input — Neural network Output
Drilling Classifier Class of rock
measurements ‘strength (0-31)
Penetration Q) — Strong rock (8-31)
rate Medium strength
tock (4-7)
Specific Cy — Weak rock (1-3)
energy Very weak seams,

fractures, voids (0)

Figure 3.—Mine roof strata characterization

A brief investigation of alternate feature vectors was
conducted early in the project using data from prior research
at NIOSH/SRL and geological classes in the manner of King
and Signer [9]. After a neural network was trained on some of
the existing data files, it was used to classify data from another
file and was found to be successful in discriminating layers.
The two features, SED and penetration rate, were found to be
satisfactory for classifying different layers into the proper
geological classes. The full set of five features (SED, torque,
rotation rate, thrust, and penetration rate) gave comparable
performance at discriminating layers.

The fuzzy clustering algorithm (alternative 5) automatically
identificd a start-in class, which corresponds to observations
made of the drill entering the rock. When the drill first enters
the rock, there is a lot of chatter, and the data are very noisy.
When the drill tip is at a depth sufficient to quell the chatter,
it is said to have established a collar. In fact, the data obtained
prior to reaching the collar depth should not be used in the
strength classification, since it would be misleading.


Results

A prototype drill monitoring system with a strata strength
classifier was developed in the laboratory. — Drilling
measurements for each roof bolt hole can be processed and the
essential information displayed for the operator to monitor in
near-real-time. A file of selected information for each
borehole can be stored for later retrieval. Prior experience
told us that the drilling data are likely to be noisy.
Consequently, measurements will be processed in subsets,
which will allow the use of statistics, smoothing, and
conversion into features for each layer. ‘The features must be
scaled properly for use in the neural network classifier, so it
was necessary to write a program to convert raw data into
suitably scaled features. That program was written in C
language. Classifying each layer drilled according to estimat-
ed strength is a new capability.

Typical drilling data from a borehole were processed. SED is
presented as a function of the depth of the drill tip in figure 4.
The spurious peak would not be used in estimating rock
strength. The data collected before the collar depth is reached
(10 cm) would not be used in the classifier to estimate rock.
strength. There is a linear upward trend in the SED that is
probably caused by friction. The steel drill shaft bends under
thrust and rubs in the borehole. It is recommended that such
trends should be removed from the data before classification
[10]. Penetration rate is presented as a function of depth in
figure 5. The penetration rate indicates the results of the
drilling process, while SED represents work put into the rock.
Neither feature is without shortcomings, but together they
provide a reliable representation of the strength of the rock.

The network was trained on data for which the strength was
known and labeled accordingly. Data from a typical borehole
were placed into one of 32 classes of compressive strength.
The strength index class is presented as a function of depth in
figure 6. There are three layers where the strength index
drops below 4, indicating that those layers are weak and not
suitable for anchoring. The deeper layers have a strength
index greater than 8, which means they are strong cnough to
provide a good anchor. If an estimate of compressive strength
is required, it can be obtained. For example, the index value
of 4 corresponds to 31,030 +3,447 kPa. However, the
strength index class is adequate for our purpose. The strength
classification is both feasible and useful.

Conclusions

The feasibility of using a drill monitoring system to estimate
the strength of successive layers of roof rock while drilling is
still underway was demonstrated in the laboratory. This
system should be applicable in all underground mines. There
is considerable interest in developing a field prototype, and

SPECIFIC ENERGY OF

STRENGTH INDEX CLASS

PENETRATION RATE, cm/s

DRILLING, MPa
8388s
83836és

ro}
is)

0 | 1 1 1 1 L L

0 20 40 60 80 100 120 140
DEPTH, cm

Figure 4.—Specific energy versus depth

4.0

3.5
3.0
2.5
2.0
1.5
1.0
0.5

0

0 20 40 60 80 100 120 140
DEPTH, cm

Figure 5.—Penetration rate versus depth

0 20 40 60 80 100 120 140 160
DEPTH, cm
Figure 6.—Strength index versus depth