論文 / 著書情報
Article / Book Information
題目(和文)

鋼橋を対象としたロボティック点検手法と腐食損傷評価に関する研究

Title(English)

A study on a robotic inspection method and corrosion damage
evaluation of steel bridges

著者(和文)

阿久津絢子

Author(English)

Ayako Akutsu

出典(和文)

学位:博士(工学),学位授与機関:東京工業大学,
報告番号:甲第10804号,
授与年月日:2018年3月26日,
学位の種別:課程博士,
審査員:佐々木 栄一,廣瀬 壮一,WIJEYEWICKREMA ANIL,岩波 光保,千々和 伸浩,小林 裕介

Citation(English)

Degree:Doctor (Engineering),
Conferring organization: Tokyo Institute of Technology,
Report number:甲第10804号,
Conferred date:2018/3/26,
Degree Type:Course doctor,
Examiner:,,,,,

学位種別(和文)

博士論文

Type(English)

Doctoral Thesis

Powered by T2R2 (Tokyo Institute Research Repository)

Doctoral Dissertation

A Study on a Robotic Inspection Method andCorrosion Damage Evaluation of Steel Bridges

Tokyo Institute of Technology
Department of Civil Engineering
Supervised by Eiichi SASAKI

Ayako AKUTSU

Contents
List of Figures .............................................................................................................................. iv
List of Tables ................................................................................................................................ ix
List of symbols .............................................................................................................................. x
Acknowledgements ..................................................................................................................... xii
Abstract ...................................................................................................................................... xiii
Chapter 1: Introduction ................................................................................................................. 2
1.1. Background ..................................................................................................................... 3
1.2. Related Research ............................................................................................................. 5
1.2.1.

Inspection System of Structures using Wall Climbing Robots................................ 6

1.2.2.

Inspection System of Structures using Unmanned Aerial Vehicles ......................... 6

1.2.3.

Method to Inspect Corrosion Damage on Steel Members....................................... 8

1.3. Purpose and Objectives ................................................................................................... 8
1.4. Outline of this Dissertation ............................................................................................. 9
Chapter 2: Development of a Self-Propelled Inspection Robot for Bridges ............................... 16
2.1. Overview ....................................................................................................................... 172.2. Concepts of the Inspection Robot ................................................................................. 17
2.3. Development of a Movement System, a Surface Attachment System and a Prototype
Inspection Robot ........................................................................................................... 18
2.3.1.

Movement System of the Inspection Robot .......................................................... 20

2.3.2.

Investigation of Attachment Method using Air pads and Improvement Process of
the Suction Condition ............................................................................................ 21

2.3.3.

Definition and Quantitative Evaluation Method for Evaluating the Attachment
Condition of the Proposed Robot .......................................................................... 23

2.4. Performance Tests of Robotic Movement on Target Surfaces and an Existing Bridge . 27
2.5. Summary ....................................................................................................................... 28
Chapter 3: Capability of Robotic Inspection System .................................................................. 48
3.1. Overview ....................................................................................................................... 49
3.2. Functions for the Proposed Robotic Inspection System ................................................ 49
3.2.1.

Visual Inspection Function .................................................................................... 49

3.2.2.

Location Identification Function to Determine the Inspection Location .............. 51

3.2.3.

Crack Detection Function for Concrete Component in Steel Bridges................... 55

3.2.4.

Vibration Measurement Function .......................................................................... 56
ii

3.2.4.2. Performance Test of the Vibration Measurement Function ................................... 58
3.2.5.

Hammering Test for Concrete Components in Steel Bridges ................................ 59

3.2.6.

Inspection Simulation based on the Currently Proposed Robot ............................ 60

3.3. Summary ....................................................................................................................... 61
Chapter 4: Proposal of Corrosion Damage Evaluation Method .................................................. 79
4.1. Overview ....................................................................................................................... 80
4.2. Basic Concept of Eddy Current Testing Method ........................................................... 81
4.3. Numerical Simulation on Characteristics on Low Frequency Eddy Current ................ 83
4.3.1.

Analysis Conditions .............................................................................................. 83

4.3.2.

Fundamental Characteristics of Detected Voltage in Eddy Current Testing.......... 85

4.3.3.

Fundamental Characteristics of Phase in Eddy Current Testing ........................... 86

4.4. Proposal of Corrosion Damage Evaluation Method Focusing on Difference of Dynamic
Magnetic Field Characteristics due to Damage Condition ............................................ 88
4.4.1.

Investigation of Input Waveforms and Evaluation Indexes used in Proposed
Method .................................................................................................................. 88

4.4.2.

Corrosion Damage Evaluation Method Focused on Detected Voltage
Characteristics ....................................................................................................... 89

4.4.3.

Corrosion Damage Evaluation Method Focused on Phase Characteristics ........... 92

4.5. Experimental Evaluation of Proposed Corrosion Damage Evaluation Method ............ 93
4.5.1.

Measurement System ............................................................................................ 94

4.5.2.

Measurement to Obtain Database.......................................................................... 94

4.5.3.

Measurement Experiment using a Corrosion Specimen ....................................... 97

4.6. Application of Proposed Corrosion Damage Evaluation Method to Robotic Inspection ..
..................................................................................................................................... 99
4.6.1.

Measurement to Obtain Database........................................................................ 100

4.6.2.

Measurement Experiment using a Corrosion Specimen ..................................... 101

4.6.3.

Investigation of Installing Method of Small Eddy Current Probe on the Proposed
Inspection Robot ................................................................................................. 101

4.6.4.

Confirmation of the Robotic Movement while Installing Small Eddy Current
Probe ................................................................................................................... 103

4.6.5.

Application on a Corrosion Specimen using Proposed Robot ............................ 103

4.7. Summary ..................................................................................................................... 104
Chapter 5: Conclusions and Recommendations ........................................................................ 134
5.1. Conclusions ................................................................................................................. 135
5.2. Recommendations ....................................................................................................... 137
References ................................................................................................................................. 138
iii

List of Figures
Chapter 1: Introduction
Figure 1.1: Examples of inspection using large-scale facilities .................................................. 11
Figure 1.2: Relationship between basic inspection processes and potential robotic inspection .. 12
Figure 1.3: Inspection robot with a magnet wheel ...................................................................... 12
Figure 1.4: Inspection robot with suction cups ........................................................................... 13
Figure 1.5 :Application of UAV for inspection ........................................................................... 13
Figure 1.6 :Conceptual image of robotic inspection in this study ............................................... 14
Figure 1.7 :Flow of this dissertation............................................................................................ 14
Chapter 2: Development of a Self-Propelled Inspection Robot for Bridges
Figure 2.1: Appearance of several types of air pads.................................................................... 31
Figure 2.2: Plan view of proposed inspection robot .................................................................... 31
Figure 2.3: Appearance of proposed inspection robot ................................................................. 32
Figure 2.4: Composition of proposed inspection robot, (a) Air supply part, (b) Control part,(c) Inspection part ..................................................................................................... 32
Figure 2.5: Movement part of air cylinder .................................................................................. 37
Figure 2.6: Condition of air pads when attached and not attached to surfaces ........................... 37
Figure 2.7: Electronic equipment installed on the proposed inspection robot, (a) Solenoid valve
for compressive air, (b) Solenoid valve for vacuum air, (c) Air cylinder ................. 37
Figure 2.8: Movement system for forward motion in one cycle ................................................. 38
Figure 2.9: Movement system for rotation motion in one cycle ................................................. 38
Figure 2.10: Appearance of elastodur air pads and nitrile rubber air pads .................................. 38
Figure 2.11: Condition of air pads while edge is rolling up by friction force ............................. 39
Figure 2.12: Proposed sponge bellows air pads .......................................................................... 39
Figure 2.13: Attachment condition, (a) Chloroprene sponge when a horizontal force is applied,(b) Sponge bellows air pads when a horizontal force is applied............................. 39
Figure 2.14: Appearance of proposed silicone bellows air pads ................................................. 40
Figure 2.15: Attachment condition and robotic movement on a corroded steel surface ............. 40
Figure 2.16: Attachment condition and robotic movement on an exterior concrete surface ....... 41
Figure 2.17: Attachment condition and robotic movement with upside down condition............ 41iv

Figure 2.18: Target surfaces on which the proposed robot is assumed to work on ..................... 42
Figure 2.19: Schematic image of determining the gap coefficient .............................................. 42
Figure 2.20: Results of calculating gap coefficient ..................................................................... 43
Figure 2.21: Results of calculating suction force ........................................................................ 43
Figure 2.22: Inspection robot during a performance test of robotic movement on an outside
concrete wall ........................................................................................................... 44
Figure 2.23: Appearance of the target bridge .............................................................................. 44
Figure 2.24: Performance test of robotic movement at the target bridge, (a) Lower chord,(b)Vertical member of the truss ................................................................................... 45
Figure 2.25: Performance test of robotic movement on the trial bridge ..................................... 46
Chapter 3: Capability of Robotic Inspection System
Figure 3.1: Original image taken by the visual inspection camera and image after applying
distortion correction .................................................................................................. 63
Figure 3.2: Filming range and color discrimination of the visual inspection camera ................. 63
Figure 3.3: Image taken by the visual inspection camera at vertical member of truss in existingbridge ........................................................................................................................ 63
Figure 3.4: Images from both the visual inspection camera and surrounding camera ................ 64
Figure 3.5: Flow of the POC method .......................................................................................... 64
Figure 3.6: Example of applying the location identification and mapping function, (a) Images
taken at the initial and after one movement of the inspection robot, (b) Initial image
attached on the base map, (c) Result from applying the location identification
function, (d) Image from applying the mapping function......................................... 65
Figure 3.7: Schematic image of the location identification method using the inspection robot . 66
Figure 3.8: Initial and end position of the robot in the performance test .................................... 66
Figure 3.9: Image from applying the mapping function ............................................................. 67
Figure 3.10: Initial and end position of the robot in the second performance test ...................... 68
Figure 3.11: Mapping result of the robot from the second performance test .............................. 69
Figure 3.12: Method to detect cracks in concrete components of steel bridges, (a) Original
image and edge emphasis process, (b) Process of applying Gabor wavelet
transformation, (c) Crack assessment process, (d) Noise reduction process .......... 70
Figure 3.13: Sensor arrangements for the laboratory experiments to evaluate vibration
characteristics of the inspection robot .................................................................... 70
Figure 3.14: Input and output signal of the experiments, (a) Input triangular wave of 2 Hz,(b)

Output acceleration wave, (c) Acceleration Fourier Spectrum from obtained
acceleration data ..................................................................................................... 71v

Figure 3.15: Gain, phase lag, and coherence as evaluation indexes, (a) Gain, (b) Phase lag,(c)

Coherence ............................................................................................................... 73
Figure 3.16: Measurement conditions when obtaining bridge vibration ..................................... 73
Figure 3.17: Experimental results of measuring bridge vibration, (a) Full Acceleration Fourier
Spectrum, (b) Magnification ................................................................................... 74
Figure 3.18: Condition of the robot during the hammering test .................................................. 75
Figure 3.19: Hammering test results using the robot acceleration sensor, (a) Surface with aninternal defect, (b) Healthy surface ........................................................................ 76
Figure 3.20: Hammering test results using the microphone ........................................................ 77
Chapter 4: Proposal of Corrosion Damage Evaluation Method
Figure 4.1: Corrosion damage pattern and an inspection method ............................................. 107
Figure 4.2: Eddy current mechanism ........................................................................................ 107
Figure 4.3: Conceptual image of eddy current testing, (a) Conceptual image, (b) Circuit
diagram ................................................................................................................... 107
Figure 4.4: Analysis model........................................................................................................ 108
Figure 4.5: Influence of mesh division on eddy current loss .................................................... 109
Figure 4.6: Detailed mesh division of analysis model .............................................................. 109
Figure 4.7: Relationship between detected voltage and frequency ........................................... 110
Figure 4.8: Relationship between detected voltage and plate thickness, (a) 1Hz, (b) 10Hz,(c)

100Hz, (d) 1000Hz ................................................................................................. 110
Figure 4.9: Eddy current density distributions, (a) Plate thickness of 6 mm and 9 mm with

0mm lift-off, (b) Lift-off of 0 mm and 0.3 mm for 9 mm plate thickness .................111
Figure 4.10: Ratio of eddy current loss to total energy consumption ....................................... 112
Figure 4.11: Relationship between detected voltage and lift-off, (a) 1Hz, (b) 10Hz, (c) 100Hz,(d) 1000Hz .............................................................................................................. 112
Figure 4.12: Relationship between phase and frequency .......................................................... 113
Figure 4.13: Relationship between phase and lift-off, (a) 1Hz, (b) 10Hz, (c) 100Hz,(d)1000Hz.................................................................................................................... 113
Figure 4.14: Relationship between phase and plate thickness, (a) 1Hz, (b) 10Hz, (c) 100Hz, (d)1000Hz.................................................................................................................... 114
Figure 4.15: Conceptual image of the effect on lift-off and thickness by the detected signal .. 114
Figure 4.16: Example results applying wavelet transformation, (a) Plate thickness 9mm andlift-off 0 mm, (b) Plate thickness signal strength.................................................. 115
Figure 4.17: Relationship between wavelet coefficient and plate thickness, (a) 1Hz, (b) 10Hz,(c) 100Hz, (d) 1000Hz .......................................................................................... 116
viFigure 4.18: Signal strength, (a) Plate thickness signal strength, (b) Lift-off signal strength,(c) TL signal ratio ................................................................................................. 117
Figure 4.19: Corrosion damage estimation curve, (a) Lift-off estimation curve (VV),(b)

Thickness estimation curve (VV) ......................................................................... 118
Figure 4.20: Flow of estimating lift-off and plate thickness (VV) ............................................ 118
Figure 4.21: Lift-off estimation curve (VP) .............................................................................. 119
Figure 4.22: Plate thickness estimation curve (VP), (a) Lift-off 0 mm, (b) Lift-off 0.1 mm,(c) Lift-off 0.2 mm, (d) Lift-off 0.3 mm ............................................................... 119
Figure 4.23: Flow of estimating lift-off and plate thickness (VP) ............................................ 120
Figure 4.24: Measurement system ............................................................................................ 120
Figure 4.25: Calibration specimen ............................................................................................ 121
Figure 4.26: Thickness signal strength ...................................................................................... 121
Figure 4.27: Lift-off signal strength .......................................................................................... 122
Figure 4.28: TL signal ratio ....................................................................................................... 122
Figure 4.29: Lift-off estimation curve (VV) ............................................................................. 123
Figure 4.30: Plate thickness estimation curve (VV).................................................................. 123
Figure 4.31: Lift-off estimation curve (VV) using thicker steel plate with thickness of

16

mm to 19 mm ........................................................................................................ 124
Figure 4.32: Plate thickness estimation curve (VV) using thicker steel plates with thicknessof

16 mm to 19 mm ................................................................................................... 124
Figure 4.33: Relationship between phase and frequency as experimental example ................. 125
Figure 4.34: Lift-off estimation curve (VP) .............................................................................. 125
Figure 4.35: Plate thickness estimation curve (VP) .................................................................. 126
Figure 4.36: Corrosion specimen .............................................................................................. 126
Figure 4.37: Back surface shape of corrosion specimen ........................................................... 126
Figure 4.38: Corrosion damage estimation results, (a) Estimation result of rust layer thickness,(b) Estimation result of residual plate thickness ................................................... 127
Figure 4.39: Lift-off signal strength (φ10 probe) ...................................................................... 128
Figure 4.40: TL signal ratio (φ10 probe) ................................................................................... 128
Figure 4.41: Lift-off estimation curve (VP_φ10) ...................................................................... 128
Figure 4.42: Relationship between phase and frequency .......................................................... 129
Figure 4.43: Plate thickness estimation curve (VP_φ10) .......................................................... 129
Figure 4.44: Corrosion damage estimation results (VP_φ10), (a) Estimation result of

rust

layer thickness, (b) Estimation result of residual thickness .................................. 130
Figure 4.45: Appearance of probe holder and inspection robot with a φ10 probe .................... 131

vii

Figure 4.46: Movement performance on a corroded metal surface and concrete wall surface,(a) Corroded metal surface, (b) Mortar wall surface ............................................ 131
Figure 4.47: Corrosion damage estimation results (VP_robot), (a) Estimation result of

rust

layer thickness, (b) Estimation result of residual thickness .................................. 132

viii

List of Tables
Chapter 2: Development of a Self-Propelled Inspection Robot for Bridges
Table 2.1: Requirements of inspection robot............................................................................... 30
Table 2.2: Developed five prototype robots ................................................................................ 30
Table 2.3: Specification of devices in robot body ....................................................................... 33
Table 2.4: Specification of devices in control part ...................................................................... 34
Table 2.5: Specification of devices in air supply part ................................................................. 35
Table 2.6: Specification of devices in inspection part ................................................................. 36
Chapter 3: Capability of Robotic Inspection System
Table 3.1: Result of location identification after modification.....................................................67
Table 3.2: Specification of microphone ...................................................................................... 75
Table 3.3: Inspection condition and information of movement and inspection duration of theinspection robot ......................................................................................................... 77
Table 3.4: Calculation results of inspection duration .................................................................. 77
Chapter 4: Proposal of Corrosion Damage Evaluation Method
Table 4.1: Physical property values of material using in analysis ............................................. 108
Table 4.2: Specification of coil.................................................................................................. 108
Table 4.3: Variables used in analysis ......................................................................................... 108
Table 4.4: Features of proposed CDE methods ......................................................................... 115
Table 4.5: Specification of measurement probe ........................................................................ 121ix

List of symbols
S’, S’0Q, Q0
Pp, Pp0, Pa, Pbase

Effective sectional area between an air pump and an air pad [mm2]Cairflow

Coefficient of air flow calculation [l/min∙N]

α’gN

Effective sectional area between an air pad and surfaces [mm2]D

Diameter of air pads [mm]Fv, FhSuction force [N]Ra

Average surface roughness [μm]Rz

Maximum surface height [μm]WWind load [N]ρa

Air density [kg/m3]

Ud

𝑊ind speed [m/s]AnProjected area [m2]Ga

Gust response factor

CD

Drag coefficient

N1, N2, n1, n2, k1, k2,
n10, n20

Pixelf(n1,n2), g(n1,n2)

Image

F(k1,k2), G(k1,k2)

2D DFTs of the image

RFG(k1,k2)

Normalized mutual power spectrum of the image

rfg(k1,k2)

POC function of the imageeNapier's constanttTime [s]𝛹𝜃𝑘 (𝑛10 , 𝑛20 )Wavelet coefficient or two dimensional gabor wavelet

𝜓𝑔Mother wavelet: gabor wavelet

𝛼𝑘Scaling parameter of two dimensional wavelet transformation.

u

Input signalv

Output signalsComplex number

Flow of vacuumed air [l/min]
Pressure of vacuumed air [MPa]Gap coefficient [μm]Number of air pads

x

U(s)

Laplace transforms of input signal

V(s)Laplace transforms of output signal

G(s)Transfer function

γ2

Coherence

Wuu, Wvv, Wuv

Power spectrum and the cross spectrum of the input signal and the output

δ

signal .
Penetration depth [m]f, fL, ft, fc

Frequency [Hz]μRelative permeability

σElectro conductivity [S/m]vv, Vm, V

Voltage [V]I, Im

Current [A]R

Resistance [Ω]LInductance [H]Z

ImpedanceωAngular velocity [rad/s]

𝜙Phase transition

B

Magnetic flux density [T]E

Electric field [N/C]J

Current density [A/m²]

We

Eddy current loss [W/m3]ρ𝑊𝜓 [𝑢(𝑡)]Resistivity𝜓Mother wavelet: Mexican hataScale parameter of Wavelet transformationb

Shift parameterd, d0Plate thickness [mm]l, l0Lift-off [mm]WCL, WCt

Wavelet Coefficient

Pxx(f), Pyx(f)Power spectral density and Cross power spectral density of the input
and output waveform.

ΦPhase [degree]

Wavelet coefficient: Mexican hatxiAcknowledgements
I wish to express my deepest gratitude to my supervisor, Associate Professor Eiichi Sasaki for hisvery kind supports, advices and encouragements. I am thankful for his aspiring guidance,
invaluably constructive criticisms and friendly advices during the research work.
I wish to express my gratitude to Associate Professor Yusuke Kobayashi, Dr. Atsushi Tanabe
and Dr. Hisatada Suganuma for their accurate advice and kind encouragements. I am sincerely
grateful to them for their truthful and constructive views on a number of issues related to thisresearch. I would like also to thank sincerely Assistant Professor Hiroshi Tamura for his kind
support, very friendly advices and encouragements. A special thanks goes to Ms. Ishihara and Ms.
Hamada for her support in non-research field. They helped many office works.
I would like to offer my special thanks to Dr. Takeya. He gave me fundamental knowledge
about developing robots. I learned many things thorough his advice and discussion. Dr. Natdanai
and Dr. Cao gave me advices on my research works, and experiments. I wish to thank ourlaboratory members, PhD. student Ms. Sanjeema, Mr. Ando, Mr. Iizuka and Mr. Kurishiba. It
was good experience for me to help their research works. It was good experience for me to discuss
on our research works and spending great time with PhD. student Ms. Tuttipongsawat, PhD.
student Ms. Mohamed, PhD. student Mr. Piseth, Mr. Mishima, Ms. Irie, Ms. Rodthong and Ms.
Shigeta.
I wish to special big thank Ms. Scorupski for her advice about my research work and English.
She gave me useful view and remarks of my research works. Furthermore, I would like to thankour graduated laboratory members, Mr. Tajima, Mr. Navicus, Mr. Ueda, Mr. Umekawa, Mr.
Kanamori, Mr. Ebisawa, Mr. Itami, Mr. Tominaga. Mr. Ota for their kind help and friendship.
Especially, I learned many things from assisting their research work and discussion with them.
Finally, I would like to thank to my mother Ms. Tomiko Akutsu for her long support andunderstanding.

xii

Abstract
Bridge maintenance is a topic that is gaining more and more significance in the field of civilengineering and so, performing inspection periodically has become an important theme.
Particularly in the case of steel bridges, there are many areas that are difficult to access and see
directly, hence it is difficult to acquire key information such as steel corrosion or the deteriorationof decks. In this research, robotic inspection has been considered as a means to acquire thisaforementioned, and a method that is simple, safe, low cost, and does not require scaffolding has
been investigated and proposed. Moreover, a corrosion damage evaluation method that can easily
obtain the rust thickness and residual thickness of corroded steel members based on eddy current
testing has been newly developed. The proposed self-propelled robot uses vacuum suction for
attachment and movement, and both a visual inspection and detailed inspection system using
small sensors has been implemented as inspection functions. Furthermore, the capacity of robotic
movement and the inspection functions were confirmed through experiments on actual bridges.
The proposed corrosion damage evaluation method has been investigated as both an independent
inspection method as well as one of the robotic inspection functions.

Keywords: Bridge maintenance, robotic inspection, self-propelled robot,
corrosion damage evaluation

xiii

1

Chapter 1
Introduction————————————————— Abstract ————————————————In this chapter, the background, related research, and purpose and objectivesof this research have been discussed. Related research has focused on several
types of existing robots that are used for inspection or to evaluate corrosion
damage using eddy current testing, to understand difficulties or needs during
inspection and taking measurements. Taking into account the background andrelated research, pertinent purpose and objectives were determined for thisresearch. To achieve an effective and efficient inspection of steel bridges,
inspection by use of a robot, which is called robotic inspection in this study,
and corrosion damage evaluation have been considered.
——————————————————————————————————————

2

1.1.

Background

Infrastructure including steel bridges constitutes an important transportation network that
contributes to the national economy and transportation system [1]. In Japan, the construction ofbridges rapidly increased, especially in the period of high economic growth during the 1960s [2,3].
Currently, there are about 700,000 road bridges exceeding two meters in length and approximately
30% of those are steel bridges [4]. At present, the construction of steel bridges has slowed down.
However, 20% of all bridges are 50 years or older, and moreover, it is expected that this number
is increasing swiftly [5-9]. In addition, steel bridges in particular are prone to deterioration due tocorrosion. These facts clearly indicate that bridges in Japan are facing deterioration problems. If
this deterioration continues to progress and rebuilding or large-scale repair is consequently
required, an enormous cost will be incurred, resulting in economic losses.
Based on the abovementioned situation, bridge maintenance is a topic that is gaining more
and more significance in the field of civil engineering [10,11]. Presently bridges cannot simply
meet the demands of traffic loadings, but they must also ensure security and reliability undervarious natural environment conditions. It is therefore important to improve the safety of bridges,
maintain their service life, and reduce their life-cycle cost [12-16]. A bridge inspection process
normally involves three steps; first, regular inspection to recognize the damage conditions ofvarious structures predominantly using visual inspection, second, detailed inspection to check the
structural behavior of the bridge using various inspection devices for cause identification, andfinally, consideration of countermeasures that can be performed for the repair and reinforcementof the bridge structure [4,6,7,11,17]. For steel bridges, in particular, corrosion is the main reason
for damage, followed by damage on deck [18-20]. Steel bridges using weathering steel materials
are being constructed, yet accumulated rust layer accompanied by delamination due to corrosion
has been discovered in coastal areas [19,21-23]. For these reasons, it is considered preferable toconduct corrosion specific inspections, as well as regular inspections of steel bridges, to acquire
corrosion damage information such as the presence of rust and reduction in steel plate thickness[24-27]. To maintain bridges effectively and efficiently within a limited budget, developing
technologies that can perform close visual inspection without requiring large-scale inspection
devices and many inspectors is essential [28,29]. These technologies are considered extremely
useful for making periodic inspection more feasible.
There are many bridges that are approaching the time for the need of rebuilding due todeterioration, yet in recent economic conditions, rebuilding is proving difficult. Therefore, it isimportant to predict serious damage in advance through maintenance, and promote long service
life by applying countermeasures to minor damages. However, there is a great lack in the numberof inspectors required for the number of existing bridges, the time required for inspection is3lengthy, inspection cost is high, and hence it is currently difficult to conduct inspection
periodically as required. In addition, it is considered the issue of current inspection is that it isdifficult to keep quantitative records because inspection results depend on the inspector's skills.
Furthermore, steel bridges comprise many sections that are invisible location, or either narrow ordifficult to access by inspectors. These factors also contribute to making inspection difficult as it
becomes necessary to have large facilities such as scaffolding for inspection to be carried out. For
example, in a steel bridge, sections that are difficult to inspect include a narrow part in a truss
component or the inside of a closed cross-section of a steel box girder. It is required to perform
simple inspections frequently without requiring large-scale facilities such as scaffolding andbridge inspection vehicles (Figure 1.1) and many inspectors [28,29].
An inspection using a robot can be considered as one means to realize aforementioned
inspection. Recently, inspection technologies and monitoring techniques using robots (referred toas robotic inspection in this research) have gained much attention as a means to accomplish thenecessary aforementioned inspections. The relationship between basic inspection processes andpotential robotic inspection is shown in Figure 1.2. During bridge monitoring, it is expected thatdata such as deformation is obtained for long term use [7,30,31]. Therefore, in this research,robotic inspection has been focused as inspection technologies. Through robotic inspection, it isexpected that inspection functions that can be used for bridge inspection can be established, in
addition to developing robots equipped with various attachments and movement functions.
The advantages of performing robotic inspection is that large equipment such as scaffoldingor special vehicles is not necessary and inspection cost can be reduced. The need of skilledinspectors is also reduced, moreover, the inspection results could be evaluated later by skilledinspectors since the results obtained by performing the inspection using a robot is not greatly
affected by the skill of the inspector carrying out the actual inspection. Therefore, it is expectedthat it will be easier to conduct inspection periodically [32-36] and to record inspection results
quantitatively. However, steel bridges not only have steel components but also concrete
components such as decks or piers, and so the robot must have the ability to move along thesecomponents also. In addition, since there are also many parts that become blind spots forinspectors, it is considered important to acquire information regarding the inspection site, that is,
to acquire the inspection position of the robot. In current robotic inspection, the application ofrobots which have supplementary features such as rails [37-41], wall climbing type robots [4252], and flight type robots (UAV: Unmanned Aerial Vehicle) [32-36,53-68] are mainstream. In
addition, research and development of inspection functions is currently underway, mainly
focusing on visual inspection and hammering tests that are conducted when performing regularinspection. In this research, in addition to visual inspection, specific inspection methods thatenable the acquisition of information such as vibration or corrosion of steel bridges have been4

investigated, and a method to implement these functions into a robot for robotic inspection has
been proposed.
On the other hand, according to a report that investigated the reason for the renewal of steel
bridges which underwent rebuilding in the past [68], the ratio of rebuilding due to damage toupper structures was confirmed to be five times higher than that of damage to lower structures. In
addition, corrosion was found to be a major factor for rebuilding steel bridges, as corrosion ofsteel materials in upper structures accounted for more than half, 51.7%, of all cases. Regarding
local corrosion, it is possible to repair corroded parts and consequently recover and improve
performance. However, this is only temporary as corrosion progresses, especially when the
corrosion of main structures is significant, and hence complete repair eventually becomes
necessary. As the area for repair increases, so does the economic burden. Therefore, it is important
to detect the existence and degree of local corrosion during periodic inspection and to carry out
repairs while the damage is still minimal. Examples of local and minor corrosion include thegeneration of a rust layer, thinning of plate wall thickness and occurrence of holes in the various
steel sections
Moreover, it has been made clear that corrosion damage lowers the load-carrying capacity ofsteel members [69-77]. Hence much research has been carried out by using the ratio of a designed
thickness to a residual thickness to develop evaluation methods of the remaining load-carrying
capacity [71-77]. Therefore, if it becomes possible to obtain the residual plate thickness of steel
members during inspection, it will enable evaluation of the remaining load-carrying capacity, andthus be an effective inspection method for steel bridges.

1.2.

Related Research

The robotic inspection proposed in this research is targeted for narrow areas such as truss panels
and inside of box members, and locations that are difficult to access by inspectors. Therefore, theproposed robot must satisfy certain conditions, namely, it moves by self-propulsion, it can movealong surfaces made of various materials, surface conditions and angles, and it can make turningsof small radii. The proposed robotic inspection intends to cover not only simple visual inspection,
but also be able to determine structural performance and evaluate corrosion damage. Therefore,
in addition to the wall-climbing robots and inspection systems using UAVs that are currently
attracting much research attention, inspection methods for corrosion damage which causes serious
damage to steel bridges were investigated through following related research. The applicabilityof these methods to robotic inspection is discussed.

51.2.1.

Inspection System of Structures using Wall Climbing Robots

Wall climbing robots need to attach and move on surfaces to access target sections. There arevarious surface attachment systems of robots. For example, robots that are capable of attaching
to only steel surfaces use magnet [43], permanent magnets [44,45] and electromagnets [46]. For
application to other materials, there are mechanisms such as using electrostatic film [47,48],
passive suction cups [49], negative suction cups [50,51] and suction cups with decompressionfans [52]. These mechanisms can attach to surfaces irrespective of the material, and the suction
force depends on the surface roughness. There are also a variety of movement methods, such as
robots that move with tires, are crawler type or are piston type. Except for the piston type robots,
a motor is necessary and so it likely that the robot body becomes heavy. Also, the crawler type
and the piston type robots only allow forward and backward motion, and so it is sometimes
difficult to change the direction of the robot.
Currently, although there are not many robots with inspection functions, Wang et al. [43]developed a robot that attaches to and inspects steel surfaces using a magnet wheel (Figure 1.3).
For attaching to surfaces, electricity is sent to the magnet wheels to magnetize them and themagnet wheels are then moved by motors. The magnet wheel shown in Figure 1.3 not onlyattaches the robot to the steel surface but also magnetizes the steel surface. Therefore, as the robot
attaches to the surface, cracks located near the surface can be detected by the magnetic flux
leakage method using a GMR sensor, which is a thin element that works in a similar manner tothe coil in eddy current testing. This method can identify surface layer flaws, which is one typeof corrosion damage that seriously damages steel bridges, thus it can be considered as a usefulrobotic inspection function.
Iwamoto et al. [52] developed a robot that can inspect concrete slabs or vertical concrete
walls using the impact echo method. The developed robot is capable of exploring vertical andceiling surfaces by wireless control. The robot attaches to surfaces by bringing the inside of thesuction cup close to a vacuum state using a decompression fan (Figure 1.4). For movement, it
adopts a mechanism that uses a belt and a DC motor. Furthermore, a CCD camera is installed forvisual inspection, an impact hammer and a microphone is installed for hammer sound inspection
and measurement data is then recorded and stored in a PC by a wireless LAN.
1.2.2.

Inspection System of Structures using Unmanned Aerial Vehicles

To perform inspection of highway bridges, after lane regulation, visual inspection is carried out
by an inspector using a special inspection vehicle. The time period for lane regulation is limited,and so the inspection range is limited to about 300 meters per day [29]. Under such circumstances,inspection of bridges using UAV, which can acquire a lot of data simultaneously, gained particularattention, and so research and development is actively underway in this domain (Figure 1.5) [60].
6Inspection by UAV is mainly performed by taking a photograph of an entire bridge and obtaining
information such as surface cracks, and then flying to target areas and carrying out visual checks
using an attached camera [55-57,59-61].
Photchara et al. [54] attached electromagnets to parts corresponding to the legs of the UAV,and developed a robot that could both fly and attach. This robot was developed assuming that thehammering test is performed once the robot has reached the target area for inspection by flying
and then attached to the target area using electromagnets. However, the body of the UAV is large
hence it is difficult to approach locations with complicated structures. In addition, being able toresist the reaction force in hammering tests by the suction force of UAV is another challenge.
Nonetheless, this robot, which is capable of both flight and attachment, can achieve large changes
in inspection position by flying.
NEXCO East Japan conducted a demonstration flight of a "fully automated robotic aerial
birds-eye view photographing system" using a UAV. A small UAV, originally developed formilitary use that takes off vertically with four rotors was used for the demonstration. This UAV
was produced to quickly confirm the location of disaster occurrence following the lessons learnt
from roads that suffered damage due to the Great East Japan Earthquake and due to the occurrenceof landslides. The demonstration flight was carried out as a verification experiment based on theidea of installed system was carried out based on the idea that it can be used daily for bridge
inspection [20]. With this UAV, it is also possible to confirm the condition of bridges by using themovies or images taken by the attached camera, and to create a map using the obtained images.
For map creation, the UAV automatically takes shots of a specified range. Since location
information is also stored at the time of photographing, UAV is also applicable to photogrammetry.
Even in other organizations such as NTT Facilities Research Institute [51], the development ofinspection technology using UAV is underway.
As a summary of inspection robots, current inspection robots tend to equip camera for visualinspection but not many inspection robots have several inspection devices. In addition, UAVs
need to consider about flight control, and the influence on hovering due to wind. In this research,aiming to achieve effective and efficient inspection for steel bridges, in addition to visualinspection other inspection methods such as vibration measurement or corrosion damage
evaluation are also considered investigating. To achieve the inspection, wall climbing type robot
was selected considering wind influence, performing vibration measurement and corrosion
damage evaluation. Hence, it is preferable to choose attachment methods which can be applied tosurfaces irrespective of materials, and to have good contact with surfaces. It is also important totake measures against potential damage to the inspection site surroundings. To perform visualinspection, vibration measurement, and corrosion damage evaluation without influenced by7

surface conditions, robots own vibration, and wind, in this research, especially attachment system
has focused.
1.2.3.

Method to Inspect Corrosion Damage on Steel Members

For corrosion damage inspection, application of ultrasonic testing [78-81] and eddy current
testing [79,82] has been progressed since measurement methods which are able to obtain residual
thickness by measuring from only one side of steel members are gained high demand. Ultrasonic
testing requires preliminary surface treatment due to the need for measurements taken in directcontact with the target surfaces. Furthermore, the measurement time for ultrasonic testing, which
can last anywhere between several minutes to just under one hour is another problem [81]. On theother hand, for residual thickness measurement of steel members, it has been reported eddy
current base methods that remote field methods [83-85] and Pulsed Eddy Current (PEC) testing[81,86-96] can eliminate preliminary and post-treatment of target surfaces. In remote field
methods, the penetration depth of eddy current is increased by using a relatively low frequency
with an eddy current testing of about 1000 Hz, and the interval between excitation and thedetection coil is increased to efficiently detect changes in the eddy current produced by theferromagnetic material. Additionally, surface or near-surface cracks in steel materials can bedetected using PEC testing [86-88] which includes a wide frequency range and caninstantaneously create a large magnetic field [89]. Furthermore, it has been found that the PEC
testing method can be applied for the measurement of steel thickness [90-93], and the detectionof piping corrosion under insulation material [94]. However, in the aforementioned methods using
eddy current, relatively large power consumption and the need of large equipment may be adifficulty during field measurements. Although the use of small probes has also been investigated,
there are still problems that it is difficult to detect a detailed damage conditions such as the actual
thickness range that can be measured being small, and a thickness reduction due to local corrosion.
In addition to establishing an independent inspection method, a corrosion damage evaluation
method has been considered in this research as one of the inspection functions of the developed
robotic inspection method. Hence the aim is to establish a method with a simple measurement
system using a small probe that can acquire local information such as thickness reduction, andthat can easily be installed onto a robot.

1.3.

Purpose and Objectives

Inspection of steel bridges can be very challenging since there are many sections that are narrowor difficult to access by inspectors. One option which can be considered to solve these difficulties8

is inspection by using a robot. The purpose of this research has thus been to develop a robotic
inspection method where a self-propelled robot is used to reach target sections of steel bridges
and successfully perform inspection. Firstly, a self-propelled inspection robot that can freely
attach and move on various types of surfaces has been developed to perform simple and effective
inspections. Next, inspection functions for robotic inspection system have been proposed, and theperformance of robotic movement and all the inspection functions have been experimentally
verified. In addition to robotic inspection, a corrosion damage evaluation method especially foruse on steel bridges has been proposed and its performance has also been investigated. The
proposed corrosion damage evaluation method can perform as both an independent measurement
method as well as one of the inspection functions of the developed robotic inspection system.
Since obtaining corrosion damage information is important for steel bridges, the proposed
corrosion damage evaluation method is considered to be an effective and efficient inspection
method that can be used independent of the inspection robot. The conceptual image of the
proposed robotic inspection for steel bridges in this study is show in Figure 1.6. In the proposed
robotic inspection, “Access” and “Measurement” are key words where “Access” includes
movement and observation of various surfaces and “Measurement” indicates measurement using
various sensors. The robotic inspection is expected to complement regular inspection and detailed
inspection and it is also expected to expand locations where inspection can be performed. To
achieve the purpose of this research, the three main research objectives considered are as follows:I.

Development of a mobile robotic inspection device which can move on various surfaces andaccess locations difficult for inspectors.

II. Investigation of robotic inspection functions for observation and various measurements.
III. Proposal of an efficient corrosion damage evaluation method.
 An independent inspection method (General inspection)
 One of the robotic inspection functions (Robotic inspection)

1.4.

Outline of this Dissertation

The research objectives are discussed in three main chapters, Chapters 2 to 4. Outlines of all
chapters are shown below (Figure 1.7).

Chapter 1: Introduction
Background, purpose and objectives and related research have been discussed. In this9

research, an inspection method applying a robot which can be considered one option toachieve an effective and efficient inspection, and a method to acquire and to evaluate
corrosion information are investigated.
Chapter 2: Development of a Self-Propelled Inspection Robot for Bridges
In order to develop a self-propelled inspection robot that is applicable on several types of
surfaces and surface conditions, an attachment system using air pads for the robot was
thoroughly investigated. Moreover, a gap coefficient, which is the average gap height
between the air pads and target inspection surface, was defined to evaluate the attachment
condition of the robot. A new type of air pad was also proposed in this chapter to improve
the attachment condition, namely, to reduce the gap between the air pads and target
inspection surface.
Chapter 3: Capabilities of a Robotic Inspection System
The proposed robotic inspection was attempted to cover both visual inspection and othermeasurements such as vibration measurement. Moreover, when the inspection robot works
at sections invisible such as closed box section, it is important to understand robot location.
To acquire the location of the inspection robot, identifying the location of the inspection robot
and making a map of the robotic movement was also proposed using the images taken byattached camera used for visual inspection.
Chapter 4: Proposal of Corrosion Damage Evaluation Method
In order to detect the thickness of rust layer and the residual thickness of corroded steel
members, a corrosion damage evaluation method applying eddy current testing techniques
has been newly proposed and developed. This corrosion damage evaluation method aims toact as both an independent measurement system and as one of the robotic inspection
functions. Therefore, a method to install an eddy current probe to the inspection robot wasalso investigated, and the performance of the corrosion damage evaluation method wasclarified using a corroded steel specimen.
Chapter 5: Conclusions and Recommendations
The results of each chapter are summarized in this chapter. Based on the findings obtainedin this study, recommendations for versatile robotic inspection and practical use of thecorrosion damage evaluation method are proposed.

10Figure 1.1

Examples of inspection using large-scale facilities [28]11Figure 1.2

Relationship between basic inspection processes and potential robotic inspection

Figure 1.3

Inspection robot with a magnet wheel [43]12Figure 1.4

Inspection robot with suction cups [52]Figure 1.5

Application of UAV for inspection [59]13Figure 1.6

Conceptual image of robotic inspection in this study

Figure 1.7

Flow of this dissertation

14

15

Chapter 2

Development of a Self-Propelled Inspection Robot for
Bridges————————————————— Abstract ————————————————In this research, an inspection method using a robot has been investigated.
The aim is for the robot to effectively access target sections of bridges, suchas areas that are either narrow or difficult to reach by inspectors, and thenperform inspection using various attached devices. A self-propelled robot has
initially been developed in this chapter. Steel bridges are the target of thisresearch, however, they are not only made of steel components but also
comprise concrete or other materials. Therefore, the robot must be able tomove on numerous surfaces under various conditions. In this chapter, theattachment system of the robot has been the main focus to enable movement
on various surfaces under a range of conditions.
——————————————————————————————————————

16

2.1.

Overview

Bridge maintenance has gained much attention in the field of civil engineering. Especially forsteel bridges, which consist of steel and concrete sections, acquiring information such as steel
deck corrosion, concrete deck cracks, and changes in vibration characteristics due to deterioration
is particularly important. However, the complicated structure of bridges makes inspection difficult.
One option to achieve aforementioned inspection can be considered robotic inspection.
Consequently, robotic inspection has recently gained much interest as an increasingly importantpart of bridge maintenance procedures. However, at narrow sections such as truss components orinternal closed sections of steel box girders incur significant inspection cost and effort wheninspection must be performed continuously. These factors make inspection complicated, and there
are further problems including the difficulty to secure enough personnel or inspection equipment.
Considering these factors, if a robot could move across both steel and concrete parts and inspectsections that are narrow, difficult to access or even invisible for inspectors, performing inspection
would become easier and more cost effective than conventional inspection methods.
In this chapter, a self-propelled inspection robot hereafter, referred to as inspection robot withthe ability to move on various surfaces and angles was initially built. It was then developed further
with the aim to achieve effective bridges maintenance within a limited budget, and perform
inspection without the need of scaffolding or large inspection passages for inspectors. It isconsidered to be able to apply also for concrete bridges. Moreover, the attachment system of theinspection robot has been thoroughly investigated. In this research, air pads which can attach tosurfaces by their inside being made into a vacuum state were selected for the robot so that it can
attach to several surface conditions irrespective of the material. Furthermore, an evaluation
method for the suction condition has been newly developed, by considering that the vacuumed
air flow can estimate the attachment condition of the inspection robot. Finally, after numerous
improvement processes, performance tests and robotic movement tests were carried out at various
locations.

2.2.

Concepts of the Inspection Robot

To begin with, there are several expectations of robotic inspection. Examples include that the
robot can perform inspection at sections that are difficult for inspectors to access, as well as
perform low cost and easy to schedule inspections without scaffolding.
Sections need of inspection are made of various materials, mainly steel and concrete in steel
bridges, and so the inspection robot needed to be able to attach and move on such surfaces also.
17

The proposed robotic inspection is namely, to approach a target section by robot and to thenperform several inspections. Moreover, establishing a method to obtain damage information suchas the corrosion or deterioration condition of the bridge through visual inspection, vibration
measurement and corrosion damage evaluation has been considered. In addition, most of theaforementioned narrow sections are invisible for inspectors and it is therefore important to obtainlocation information of the robot. It is also important to obtain such location information wheninspectors find cracks or corrosion to accurately determine damage locations which can then also
be used for consequent inspections.
To perform not only visual inspection, but also vibration measurement and corrosion damage
evaluation, the inspection robot needs to have good contact with surfaces so that it does not
influence its own vibration. Therefore, the most important point to apply wall climbing type robot
to inspection can be considered attachment condition. The inspection robot is also not intended toperform inspection of an entire bridge but of locations where damage is expected or locations
where it is difficult to approach by inspectors. Concretely, since robotic inspection is able toperform measurement in addition to visual inspection, it is expected to complement of regularinspection and detailed inspection. Furthermore, it is expected to make it possible to inspect
locations such as high places, closed sections or narrow sections where inspections have been
considered difficult (that is, expansion of the inspection place). In addition, the inspection robot
is considered able to determine the degree of damage progress by performing inspection
periodically at the same location. Considering these facts, the requirements of the inspection robot
were determined as shown in Table 2.1. Functions of the robot to satisfy the requirements will be
explained in the following chapters.

2.3.

Development of a Movement System, a Surface Attachment
System and a Prototype Inspection Robot

In this research, five prototype robots as shown in Table 2.2 were initially built. Each robot
applied improvements such as changing materials of the inspection robot, shape of the inspection
robot or method of making vacuumed state. Finally, the fifth version was selected as the inspection
robot to satisfy the requirements as shown in Table 2.1. Main reasons that the inspection robot
can achieve good contact and self-propelled movement are considered improvement of
attachment method, enough suction force, and its weight. Air pads were selected to attach theinspection robot to surfaces irrespective of surface materials (Figure 2.1). The air pads have asimilar surface attachment system to vacuum suction cups, however in this research, an air pumpwhich can provide vacuumed air directly to the air pads has been used instead of a fan to create a18

vacuumed state. By using air pads and an air pump, there is no need for additional parts such asa fan to be attached to the air pads, hence the leg part of the robot can be made simple and light,
and a more stable vacuum state can be sustained. After the fifth prototype robot was built andselected, several improvements were made to the robot, and inspection devices were then installed
to create the proposed inspection robot for this research. The body of the inspection robot is madeof polycarbonate, which is a useful plastic for applications that require high impact resistance,
and its use enabled the robot to become lightweight and durable. To consider the narrow sections,finally the size of the robot body without inspection devices is (W) 153 mm × (L) 148 mm × (H)
60 mm and its weight is 327.5 g. It is considered to be able to apply at narrow sections such as
truss components or hand hole of closed box girders. The plan view and appearance of theinspection robot are shown in Figures 2.2 and Figure 2.3 respectively.
The proposed inspection robot was built considering simple control system, therefore, amicro sequencer which can provide “on” and “off” signal to control all movements is selected.
Moreover, air pump is selected to use to make vacuumed state of air pad thus, air tubes are
required on the inspection robot. Consideration of simple control system and required air tube,
the robot is considered to be regulated by wired control. It is considered important to guarantee
the safety of the roads under the bridges that are being inspected. In case of the proposed
inspection robot, the attached cables would keep the robot suspended from the bridge if it robot
accidentally fell down while performing inspection. Hence using wired control robot with
attached cables can prevent damage to the area under the inspection site. Then finally, theinspection robot was proposed with four components; robot body, air supply part, control part andan inspection part. The air supply part consists of an air compressor and an air pump to supply
compressed and vacuumed air to the robot body, as shown in Figure 2.4(a). The control partconsists of a micro sequencer and a controller, to control the robotic movement and devices forinspection as shown in Figure 2.4(b). The inspection part consists of a Charged Couple Device(CCD) camera to perform visual inspection, plus other cameras to view the surrounding areas,
and an accelerometer to determine structural performance as shown in Figure 2.4(c).
Specifications of the devices used in the robot body, control part, air supply part and inspection
part are defined in Table 2.3 to Table 2.6 respectively. The inspection devices were selected withconsideration of applicability and their size. If the inspection devices are small, other inspection
devices could be additionally installed onto the robot, or used to replace existing inspection
devices to enable other types of inspection. The robot body and inspection parts are connected tothe control part by cables, which can currently extend to 10 meters. It was decided as the lengththat it can be inspected members such as stringer or cross beam at one time. Therefore, inspectors
are able to control the robot from a safe distance, without putting themselves in any danger, while
viewing the images from the inspection cameras to examine specific areas in real-time. An
19additional inspection function, namely a corrosion damage evaluation method using an eddy
current probe, has been investigated in Chapter 4.
The main unique feature of the proposed inspection robot is that it is able to attach to various
surfaces. It does so by using six air pads, and it moves by the expansion and contraction of the air
cylinder piston rods. It is therefore possible for the inspection robot to move along surfaces withvarious materials, angles, and roughness, without the need of special rails, as required byinspectors. In addition, the inspection robot is relatively small and lightweight and is also tightly
attached to the surface by air pads, thus it is difficult for the inspection robot to be affected bywind.
2.3.1. Movement System of the Inspection Robot
The proposed robot is made of two separate parts hereafter called the upper part and the lower
part as shown in Figure 2.2. In this system, the bodies of the air cylinders are fixed onto the upper
part and the piston rods are fixed onto the lower part. The lower part advances when the pistonrods expand. On the other hand, the upper part advances when the piston rods contract. Moreover,
the robot has a movement mechanism that moves up and down (Figure 2.5) according to whether
the air pads are or are not attached to the surfaces as shown in Figure 2.6. This mechanism allows
the inspection robot smooth movement regardless of friction force which influences to the air
pads movement and will be explained following section. When an air pad is said to be “attached”,
it means that the inside of the air pad is made into a vacuum state by the air pump. Solenoid valves
are used for the robot to control the air cylinders and air pads by sending “on” and “off” signals
from a micro sequencer. This technique allows the movement system to become simple. The air
pads attach to a surface and the air cylinders cause the piston rods to contract when the solenoid
valve sends them an “on” signal. The electronic equipment used, such as the solenoid valves andair cylinders are shown in Figure 2.7. There are two types of solenoid valves in this research. One
is for compressed air to send signals to the air cylinders and the other is for vacuumed air to send
signals to the air pads. The piston rod of the air cylinders is 20 millimeters thus in theory, therobot should be able to move 20 millimeters in one movement and whether this is the case or nothas been examined.
The designed movement system allows the inspection robot to move forward, retreat andalso rotate. Figure 2.8 shows one cycle of the movement system for forward motion. The colored
air pads indicate that they are attached, and the uncolored air pads indicate that they are notattached. Forward movement composes of three steps; standby, upper part advances, and lower
part advances. First, air pads of both the upper and lower parts are attached to the surface, and thepiston rods of the air cylinder are expanded (Step 1). Next, the air pads of the upper part detachfrom the surface by sending the air pads of the upper part an “off”-signal to release them from the20

vacuum state. The upper part then advances by contracting the air cylinder piston rods (Step 2).
The lower part advances by attaching the air pads of the upper part but not lower part to thesurface, by expanding the air cylinder piston rods. Finally, the robot returns to standby byattaching the air pads of the lower part to the surface (Step 3). When the advance button of thecontroller is pressed continuously, the motion of Step 1 to Step 3 is repeated. For retreat motion,
the steps of forward motion are simply reversed.
Figure 2.9 shows one cycle of the rotation motion system in the right-hand direction.
Rotation motion composes of five steps; standby, lower part retreats, lower part rotates, upper part
rotates, and lower part advances. First, air pads of both the upper and lower parts are attached tothe surface by suction, and the air cylinder piston rods contract (Step 1). The lower part thenretreats by removing the suction of the air pads of the lower part and contracting the air cylinder
piston rods (Step 2). The lower part can then rotate approximately 10°, by expanding the left hand
air cylinder piston rod (Step 3). The upper part can then rotate approximately 10° by attaching the
air pads of the lower part but not upper part to the surface by suction, and by contracting the left
hand air cylinder piston rod. (Step 4). The lower part advances by expanding the air cylinder
piston rods, and finally, the robot returns to standby by suction of the lower part air pads (Step 5).
When the right rotation button of the controller is pressed continuously, the motion of Step 1 to
Step 6 is repeated. During left rotation motion, right and left are reversed in the aforementioned
steps.
2.3.2. Investigation of Attachment Method using Air pads and Improvement Process of theSuction Condition
In this research, a method to enable a self-propelled inspection robot to attach to surfaces ofvarious angles or roughness has been developed initially. The proposed inspection robot attaches
to surfaces using air pads, hence the suction force of the air pads is the most influential factor for
inspection such as vibration measurement when dealing with surface angles or conditions. In
general, using larger diameter air pads increases the suction force. However, if there is a gap, airleakage increases, which lowers the suction force. The balance between suction force and air
leakage, and the dead weight of the robot need to be considered to improve the suction force. In
this study, in particular, an air pad having a strong suction force and a small air leakage wasselected. The suction force is a particularly important factor in the use of air pads, so evaluation
method of suction force will be discussed in the following section.
Air pads are mainly used for transporting products and machinery in factories hence there
are many types of air pads with different shapes or materials according to their various purposes.
In this research, bellows type air pads have been selected for the attachment part of the robot.
Bellows type air pads are considered adaptable to surface conditions with little constraint on the21

movement range and can be expanded and contracted vertically according to the required suction
condition. Two types of material with different hardness were selected for the air pads, namely
elastodur and nitrile rubber as shown in Figure 2.10. The specifications for air pads made of bothmaterials are shown in Table 2.3.
Nitrile rubber is harder than elastodur, and so the movement of the robot can be made morestable. However, on rough surfaces such as concrete or corroded steel, nitrile rubber was not able
to have good contact due to the poor surface condition and so the suction force reduced. For thisreason, elastodur air pads, was initially selected. Although the attachment condition of theelastodur air pads to concrete surfaces or other rough surface conditions was better than the nitrile
rubber air pads, the edge of the elastodur air pads rolled up due to the softness of elastodur anddue to the friction force between the air pads and surface as shown in Figure 2.11. It was
confirmed that the suction force reduces due to this phenomenon and so air pads made of nitrile
rubber, which had greater hardness and stability for robotic movement than elastodur wasinvestigated. Attachment to rough surfaces, which was considered to be a problem for nitrile
rubber air pads, was solved by attaching a chloroprene rubber sponge to the suction part of thenitrile rubber air pads as shown in Figure 2.12. A chloroprene rubber sponge was selected tofurther develop the nitrile rubber air pads as it contains bubbles and is thought to be able to change
shape corresponding to the surface conditions. Using a chloroprene rubber sponge alone cannot
support the weight of the robot, however the proposed nitrile air pads with chloroprene sponge,
named hereafter sponge bellows air pads, can support the weight of the robot by using the
expansion and contraction feature of bellows type air pads. As shown in Figure2.12, in order torestrict the movement of the sponges, a support material has been embedded and glued inside of
the sponges. Taking into consideration the potential of air leakage in the sponge part, the minimum
height required for attachment, without compromising the adhesion strength, was determinedthrough experiment. Figure 2.13 shows the attachment condition of the sponge bellows air pad.
By using sponge bellows air pads, an attachment system has been developed to enable therobot to move across various surfaces with many different surface conditions, except damaged
surfaces such as those with large cracks or peeling corrosion. However, although the inspection
robot has a movement mechanism that moves up and down according to whether the air pad is or
is not attached to the surface, friction wear has been confirmed since the sponge is weaker against
friction force than the elstodur air pads. As the friction wear of the sponge progresses, the suction
force decreases and this was considered to influence the movement of the robot Therefore, further
improvement to the air pads was examined.
There are various materials for the sponge as well as the air pads, and so a silicone sponge
which is more resistant to abrasion and has finer bubbles than a chloroprene rubber sponge wasconsidered to improve the attachment condition of the robot. First, silicone rubber has a higher
22hardness than chloroprene rubber and has better durability. It was also confirmed throughexperiment that air leakage at the sponge section was also reduced due to the finer bubbles ofsilicone rubber. In addition to changing the sponge material, the shape of the inner support
attached inside of the sponge was also changed, as shown in Figure 2.14, to make the suction
state more efficient and stable. The height of the inner support shown in Figure 2.14 was also
made higher and the size of the support holes was made bigger to maintain a suction state more
effectively than the previous sponge bellows air pads. While there are many advantages of using
silicone sponge bellows pad that have fine bubbles and high hardness, named hereafter silicone
bellows air pad, especially for use on smooth surfaces it was established that smooth movement
was sometimes prevented due to friction or surface moisture. Sponge bellows air pads using
chloroprene rubber sponges and silicone bellows air pads have different characteristics on
different surface types. Therefore, sponge bellows air pads and silicone bellows air pads can beselected according to the surface condition of the target inspection location.
By selecting the type of air pad accordingly, the robot is able to move on various surfaces
such as corroded steel, outside concrete walls or even ceilings as shown in Figure 2.15 to Figure
2.17 respectively. If sponges are not attached to the air pads, friction wear occurs at the attachment
surface of the air pads directly. Since the cost of air pads is relatively higher than sponges, using
sponges can be assumed to be an advantage as sponges can be replaced immediately with low
cost and ease if friction wear is discovered.
2.3.3. Definition and Quantitative Evaluation Method for Evaluating the Attachment Conditionof the Proposed Robot
The most important thing when using air pads is evaluating the suction state, as this is considered
the attachment condition of the inspection robot. During the performance test of the air pads, thesuction state and robotic movement on various surface conditions such as mortar or exterior
concrete walls have been confirmed. In this research, twelve different types of surface wereselected as target surfaces for the inspection robot. The target surfaces are shown in Figure 2.18.
They are made of either steel or concrete with corrosion, cracks or peeling present.
In this subsection, a quantitative evaluation method of the suction state of the inspection
robot has been examined. Therefore, with reference to formulas for calculating the air flow rateand effective cross sectional area of the air tube used when selecting the vacuum pump, a gap
coefficient, which is the average height between the air pads and surfaces, has been defined.
Furthermore, by using the calculation for the gap coefficient, it is also possible to calculate thesuction force of the air pads. To obtain the gap coefficient for each surface, experiments whichmeasure the negative pressure of the air pump when one air pad is attached to a surface have been
carried out. Schematic image of deterring the gap coefficients are as shown in Figure 2.19. In this23

figure, 𝑆 ′ and 𝑆0′ indicate the effective sectional area between the air pump and the air pad when
the air pad is or is not attached to surfaces respectively , 𝑎′ indicates the effective sectional area
between the air pad and surfaces, and t indicates the gap coefficient. As a result of the experiments,
the suction state could be classified into three levels using the gap coefficient as follows:(1)

g < 20μm (Complete attachment and movement);(2)

20 ≤ g < 60

μm (Attachment and movement: conditional);(3)

60 ≤ gμm (Impossible to attach).

The second level indicates that the robot can move across surfaces, however, the possibility of it
falling down increases as the gap coefficient nears 60 μm. Based on the flow characteristics of air
pumps, the flow of vacuumed air can be expressed as:

𝑄 = 𝑄𝑚𝑎𝑥 ∗𝑃𝑏𝑎𝑠𝑒 − 𝑃𝑝
𝑃𝑏𝑎𝑠𝑒

(2.1a)

𝑄0 = 𝑄𝑚𝑎𝑥 ∗𝑃𝑏𝑎𝑠𝑒 − 𝑃𝑝0
𝑃𝑏𝑎𝑠𝑒(2.1b)

where 𝑄𝑚𝑎𝑥 is the maximum flow of vacuumed air of the air pump. In this research, an air pumpwith 𝑄𝑚𝑎𝑥 of 90 [l/min] has been used. 𝑄 is the flow of vacuumed air when the air pad is
attached to surfaces by suction and 𝑄0 is the flow of vacuumed air when the air pad is not
attached to surfaces, namely opened condition. 𝑃𝑏𝑎𝑠𝑒 is the base pressure of vacuumed air and𝑃𝑏𝑎𝑠𝑒 of 0.1 [MPa] is generally used. The pressure of vacuumed air when the air pad is attachedto surfaces 𝑃𝑝 [MPa] and the pressure of vacuumed air in the open condition 𝑃𝑝0 [MPa] wereobtained from experiment. S′[mm2], which is the effective sectional area between the air pumpand the air pad which is attached to surfaces, and 𝑆0′ [mm2], which is the effective sectional area
between the air pump and the air pad in open condition, are given by:

S′ =
𝑆0′ =

𝑄
𝐶𝑎𝑖𝑟𝑓𝑙𝑜𝑤 ∗ √(𝑃𝑏𝑎𝑠𝑒 − 𝑃𝑝 )𝑃𝑝
𝑄0

𝐶𝑎𝑖𝑟𝑓𝑙𝑜𝑤 ∗ √(𝑃𝑏𝑎𝑠𝑒 − 𝑃𝑝0 )𝑃𝑝0

(2.2a)(2.2b)

Where, 𝐶𝑎𝑖𝑟𝑓𝑙𝑜𝑤 [l/min∙ N] is coefficient of air flow and 𝐶𝑎𝑖𝑟𝑓𝑙𝑜𝑤 of 240 [l/min∙ N] is used.
Moreover, 𝑆′ can also be expressed by following equation:
1
1
1= ′2 + 2
2
𝑆′
𝑎′
𝑆0
24

(2.3)

Using Equations (2.2a), (2.2b) and (2.3), 𝛼′ [mm2] which is the effective sectional area between
the air pad and surfaces can be obtained as:

𝛼′ =

1
1
1
√𝑆′2 − ′2
𝑆0

(2.4)

Finally, the gap coefficient 𝑔 [μm] can be determined as:𝑔=𝛼′
× 1000𝜋𝐷𝑁(2.5)

where 𝑁 is number of air pads and 𝐷 [mm] is the diameter of the air pads. By calculating the
gap coefficient of a target bridge before performing inspection, it is possible to predict the suction
state of the robot before performing inspection. However, it should be noted that the vacuumed
air flow and effective sectional area are theoretical values.
The calculated gap coefficients of five example surfaces from Figure 2.18 are showed in
Figure 2.20. 𝑅𝑎 is known as the average surface roughness and 𝑅𝑍 is known as the maximum
surface height. These values are generally used to characterize surface roughness [98]. In thisresearch, a laser displacement meter was used to obtain 𝑅𝑎 and 𝑅𝑍 values for each surface.
Figure 2.20 shows the level of the suction state based on the gap coefficient. The gap coefficientsof sponge bellows air pads and silicone bellows air pads were decreased compared with elastodur
air pads hence the suction state has been improved. Therefore, using the developed sponge
bellows air pads and silicone bellows air pads has proven to be highly effective.
A method to obtain the suction force 𝐹 [N] using the gap coefficient t obtained by the above
process is as follows. First, 𝛼′(𝐷), which is the effective sectional area between the surface andair pads attached to the surface with a diameter D [mm], and 𝑆 ′ (𝐷) [mm2] which is the effective
sectional area between the air pump and air pads which are attached to surface with a diameter 𝐷[mm] are given by:
𝛼′(𝐷) = 𝜋𝑔𝐷𝑁/1000
1𝑆′(𝐷) =
11√𝛼′2 (𝐷) + ′2
𝑆0

25

(2.6)
(2.7)

Moreover, the flow of vacuumed air when the air pads are attached to the surface 𝑄 can also be
expressed by the following equation according to the theoretical formula.

𝑄 = 𝐶𝑎𝑖𝑟𝑓𝑙𝑜𝑤 ∗ 𝑆′2 (𝐷)√(𝑃𝑏𝑎𝑠𝑒 − 𝑃𝑝 )(𝑃𝑝 − 𝑃𝑎 )(2.8)

From equation (2.8) and (2.9), the pressure of vacuumed air when all air pads are attached to thesurface 𝑃𝑎 can also be represented as follows:

𝑃𝑎 = 𝑃𝑝 +𝑄2
𝐶𝑎𝑖𝑟𝑓𝑙𝑜𝑤 2 ∗ 𝑆0′2 (𝐷)(𝑃𝑝 − 𝑃𝑏𝑎𝑠𝑒 )(2.9)

Finally, the suction force in the vertical direction 𝐹𝑣 can be represented as follows:

𝐹𝑣 = 𝑃𝑎 ∗

𝐷2
𝜋∗𝑁
2

(2.10)

Figure 2.21 shows the results of calculating the suction force of silicone bellows air pad
using same surfaces as Figure 2.20. 𝐹ℎ in Figure 2.21 is the suction force in the horizontal
direction, and it is evaluated with a safety factor of 1/2 to 𝐹𝑣 . The results show that although eachtype of air pad has a similar suction force on the smooth surface, on the rough surface, the suction
force was six times higher using the sponge bellows air pads and twenty-five times higher using
the silicone bellows air pads than using elastodur air pads. Hence using the proposed air pads
enabled a significantly higher suction force on rough surfaces than using elastodur air pads.
Therefore, these results indicate the effectiveness of using sponge bellows air pad and silicone
bellows air pads over elastodur air pads. Namely, it has been confirmed that attaching chloropreneor silicone sponge to the attachment surface of the elastodur air pads is highly effective as itincreases the suction force of the air pads. For example, limited wind speed for inspection is 10
to 15 meter per second and wind load for the inspection robot can be calculated approximately 9
N. From the view point of wind load, the inspection robot can be considered to have enoughsuction force. Moreover, it can be considered the inspectors can select applicable weight ofinspection devices from suction force evaluation process. The wind load W (N) can be calculated
by following equation [99];1
𝑊 = 𝜌𝑎 𝑈𝑑 𝐴𝑛 𝐶𝐷 𝐺𝑎
226(2.11)

Where, 𝜌𝑎 is air density [kg/m3], 𝑈𝑑 is wind speed [m/s], 𝐴𝑛 is projected area [m2], 𝐶𝐷 isdrag coefficient and 𝐺𝑎 is gust response factor. In this calculation, 𝜌𝑎 of 1.2 [kg/m3], 𝐶𝐷 of
1.6 and 𝐺𝑎 of 1.9 have been used.

2.4.

Performance Tests of Robotic Movement on Target Surfaces andan Existing Bridge

The proposed robot is able to move forward, retreat and rotate. Performance tests of robotic
movement were carried out in three locations. First, target surfaces (Figure 2.18), second, at anexisting steel bridge with significant corrosion, and third, at a trial bridge, (which is used forinspection training). These locations are also used for other performance tests in following
chapters. The first performance test on target surfaces was carried out to evaluate robotic
movement on various surface conditions. The performance tests on the existing bridge and thetrial bridge were then carried out to better understand robotic movement on actual surface
conditions. The existing bridge was assumed surface conditions of significant corrosion and thetrial bridge was assumed general concrete deck.
The first performance test was on target surfaces. In this test, all three movements were
performed and evaluated on all target surfaces. Figure 2.22 shows one of the performance tests
carried out on an exterior concrete wall. It was confirmed that the inspection robot could moveon various surfaces such as concrete or corroded steel, and that it can move up to 400 millimeters
per minute. It was also confirmed that the inspection robot is able to advance and retreat well on
various surfaces, and moreover, rotate smoothly on various surfaces. Moreover, the performanceof the robotic movement was barely affected by its weight or the elasticity of the cables.
A performance test was also carried out at an existing bridge with steel corrosion as shownin Figure 2.23. The existing bridge is a twenty-eight span simple through-truss bridge with a total
span of 559.6 m, located in Japan sea side. It is currently limited to five tons of weight due to thepresence of considerable corrosion hence there are plans to rebuild the bridge in a few years time.
To perform live robotic inspection, performance of robotic movement was tested to examine
whether the robot can be used at sections that inspectors cannot reach, such as panel sections oftruss members. In this performance test, the inspection robot was tested on a lower chord and a
vertical member of a truss, as shown in Figure 2.24(a) and (b). The results confirmed that theinspection robot could move in areas where corrosion was relatively minor. However, corrosion
has significantly progressed in most areas of this bridge and some places showed peeling of thecorrosion surface that has more peelings than case 2 or case 3 of Figure 2.18. The surface
condition in these areas was far worse than the concrete walls used in experiment, and the peeling27

of the corrosion surface lowered the performance of the robots movement. The poor surface
condition created a greater gap between the air pads and the surface than when tested on concrete
walls. Moreover, it was confirmed that the sponge section of the sponge bellows air pads became
worn down by the peeling of the corrosion surface, and this promoted gap propagation between
the air pads and the surface. Two cameras and a single accelerometer were attached to the frontof the inspection robot t to perform live robotic inspection, but the weight of these devices wasfound to wear down the front air pads. It was confirmed that the inspection robot could attach andmove even at significant corroded areas, however it required to solve the problem of wearing
down the air pads. Therefore, silicone bellows air pads were proposed and examined.
The last is performance test was at a trial bridge which is used for inspection training. As the
sponge part of the sponge bellows air pads were worn down in the previous performance test,
silicone bellows air pads were proposed for use on rough surfaces such as those with significant
steel corrosion peeling. The use of silicone bellows air pads was examined on the concrete deckof the trial bridge, which is currently no longer in use. This bridge is very old and now only used
for training inspectors. Figure 2.25 shows the inspection robot during the performance test on theconcrete deck of the trial bridge. The surface condition of the concrete deck is also shown in
Figure 2.25. As shown, the concrete deck consists of a rough surface with cracks. It was therefore
considered to be useful for the performance test of silicone bellows air pads. The inspection robot
with silicone bellows air pads was able to attach to the trial bridge surface and move without the
sponges becoming worn down. Hence the results indicate that silicone bellows air pads are goodfor use on rough surfaces with cracks or corrosion peeling. Moreover, since it was raining at thetime of the experiment on the trial bridge, robotic movement of the proposed robot on wet surfaces
could also be examined. When using silicone bellows air pads, smooth movement wasoccasionally prevented as the friction between the air pads and the surfaces was too strong due tosurface moisture. On the other hand, smooth movement was not a problem when using sponge
bellows air pads on the same wet surface. Thus it can be considered that the two proposed air pads
can be selected according to the condition of the surface that requires inspection, such as roughor wet surfaces. The finally, the attachment condition and performance of robotic movement wereclarified through these performance tests.

2.5.

Summary

In this chapter, fundamental investigations were conducted to clarify the attachment and selfpropelled movement system of the inspection robot. An inspection robot which can move acrossvarious surfaces to carry inspection devices for robotic inspection has been developed in this28

chapter. The developed inspection robot uses air pads to attach to various surfaces and air
cylinders to move forward, retreat and rotate. The inspection robot has a movement mechanismthat moves up and down according to whether the air pads are or are not attached to the surface,to enable smooth movement.
In this chapter, focus has been made on the attachment system, as the most important factor
when using air pads is the suction condition. To evaluate the suction condition, a gap coefficient,which is the average height between the air pads and surface has been defined. Moreover, using
this gap coefficient, a quantitative evaluation method for the suction state has been proposed. This
suction state helps inspectors to estimate how well the inspection robot will attach to target bridges
in advance of carrying out inspection.
To improve the attachment condition of the inspection robot, two new air pads were proposed.
The gap coefficient of each air pad was obtained for twelve target surfaces. The results confirmedthat the suction condition was improved by using the proposed air pads compared with usingelastodur air pads, and that smoother movement could be achieved. Furthermore, use of the two
air pads can be selected based on the surface condition such as roughness or moisture. Finally,
performance tests of robotic movement were carried out on target surfaces in laboratory, at anexisting bridge and at the trial bridge. These performance tests confirmed that robotic movement
improved by using the proposed sponge bellows air pads and silicone bellows air pads.
In this research, fundamental function for self-propelled inspection robot was developed,
which can make the inspection robot move on various surface of existing bridges. It is
unsupported to places where is step and is an angle of the face changes so far, yet it is possible tocope with by changing the movement mechanism, for example changing the angle of the leg partof the inspection robot by the encoder.

29

Table 2.1

Requirements of inspection robot

Requirements

Functions for requirement

Ability to attach and move on various surface

Attachment and self-propelled movement

conditions, includes difference in materials,

system with air pads and vacuumed air

roughness, angles, without special rails
Ability to obtain information of surface condition

Visual inspection function, crack detection

for observation in robotic inspection

function

An automatic identification system of robot

Location identification function

location especially at invisible areas
Ability to obtain information of damage

Vibration measurement, Corrosion damage

condition for measurement in robotic inspection

evaluation

Small in size to inspect narrow sections

Miniaturization of robot body

Table 2.2
Ver.

Appearance

Developed five prototype robots
Size

Weight(L) 130 mm
12(W) 183 mm

Floor: Fail
720 g

Ceiling: Fail(L) 130 mm

Floor: Ok(W) 183 mm

760 g(W) 210 mm

Floor: Ok
624 g(H) 66 mm

563 g(H) 60 mm

30

Elastdur air pad
Compressive to
vacuumed

Nitrile air pad

surfaces

Elastdur air pad
Movementmechanism

Wall: Smooth

Polycarbonate

surfaces

Robot shape

Ceiling: Fail(L) 148 mm(W) 153 mm

Aluminum

Floor: Ok(H) 66 mm

5

Wall: Smooth
Ceiling: Fail(L) 170 mm(W) 200 mm

Wall: Fail

Features

Ceiling: Fail(L) 150 mm

4

Wall: Fail(H) 61 mm(H) 51 mm

3Movement

Floor: Ok
328 g

Wall: Ok
Ceiling: Ok

Vacuumed air
directly supply
Number of air
padsFigure 2.1

Appearance of several types of air pads

Figure 2.2

Plan view of proposed inspection robot31

Figure 2.3(a)Appearance of proposed inspection robot

(b)

(c)
Figure 2.4

Composition of proposed inspection robot, (a) Air supply part, (b) Control part,(c) Inspection part32

Table 2.3
Basic information

Air padSolenoid valve

Air cylinder

Speed controller

Specification of devices in robot bodySize(L) 148 mm (W) 153 mm (H) 60 mm

Weight327.5 gMaterial

Polycarbonate

Rubber material

Elastodur / Nitrile rubber

Shore hardness

65 / 70Size

φ 31.4 mm /φ 32 mm

Weight8 g / 5.5 g

Number of positions

2 positions

Number of ports

5 ports

Valve function

Single solenoid

Size(W)10 mm×(L)43.5 mm×(H)14 mm

Weight20 g

Applicable fluid

Air

Actuation technology

Internal pilot

Connection caliber

M3×0.5Oiling-Withstanding pressure

1.05 MPa

Response time

ON: 4 ms, OFF: 8 ms

Applicable voltage range

24±10% DCV

Current value

52 mA

Size

(W)10 mm×(L)24.5 mm×(H)21 mm

Weight15 g

Actuation technology

Double operation type

Applicable fluid

Air

Withstanding pressure

1.05 MPa

Oiling-Connection caliber

M3

Cylinder diameter

8 mm

Piston rod diameter3 mm

Basic stroke

20 mmApplicable fluid

Air

Applicable tube size

φ 1.8 mm

Applicable pressure range

0.1 ~ 0.9 MPa

33

Table 2.4

Specification of devices in control part

Power supply

Single phase 100 ACV

Maximum applicable pressure

0.7 MPa

Air

Discharge air flow rate

15 L/mincompressor

Air tank capacity

5L

Size

(W)350 mm×(L)310 mm×(H)290 mm

Weight10.3 kgPower supply

Single phase 100 ACV

idling speed

1250 /min

Motor output

90 W

Vacuum pressure-98.3 kPa

Exhaust speed

90 L/min

Size(W)125 mm×(L)340.6 mm×(H)181 mm

Weight9.0 kgApplicable fluid

Air

Connection caliber

Rc1/4

Nominal filtration rating

5 μm

Applicable pressure range

1.47 MPa

Drain storing capacity

55 cc

Material

Zinc die-cast

Weight330 g

Drain cockscrew

Applicable fluid

Air

Connection caliber

Rc1/4

Applicable pressure range

1.47 MPa

Oiling-Material

Zinc die-cast

Weight

230 g

Air pump

Air filter

Regulator

34

Table 2.5

Specification of devices in air supply part

Processing and controlling method

Stored program repeated processing method

Input and output controlling method

Batched processing

Program language

Relay symbol and step ladder method

Program memory

8 k step EEPROM
Sequencer command: 27

MicroCommands

Step ladder command: 2
Application command: 89

sequencer

Basic command: 0.55~0.7 μs / Command
Processing speed

Application command: 3.7~999 μs /Command
DC input: 14

Input and output

Solenoid
valve(Vacuum)

Relay input: 10

Weight450 g

Number of positions

2 positions

Number of ports

2, 3 ports

Valve function

Normally closed

Size(W)15 mm×(L)80.7 mm×(H)24 mm

Weight80 g

Applicable fluid

Air

Actuation technology

External pilot

Connection caliber (main)

M5×0.8Connection caliber (pilot)

M5×0.8Oiling-Withstanding pressure

1.05 MPa

Response time

ON: 15 ms, OFF: 25 ms

Applicable voltage range

24±10% DCV

Current value

65 mA

35

Table 2.6

Specification of devices in inspection partLens mountM12×0.5

Image-acquisition method

NTSC

Pixel number

250,000 pixels

Horizontal resolution

360 TV Line

CCD camera

Lowest illuminance

0.1 Lux(Visual inspection)

Angle of view

90 degrees

Power supply

12 VDC±10% / 80 mA

Size(W)32 mm×(L)32 mm

Weight14g / 29.5 g (includes case)Applicable temperature-10~50℃

Image sensor

1/4 CCD

Image-acquisition method

NTSC

Pixel number

400,000 pixels

Horizontal resolution

480 TV Line

CCD camera

Lowest illuminance

0.5 Lux(Surrounding)Angle of view

170 degrees

Power supply

12 VDC±10% / 80 mA

Size(W)18.5 mm×(L)22 mm

Weight27 g

Waterproof

IP68

Axis

3 axisSize(W)4 mm×(L)4 mm×(H)1.45 mm

Size (with circuit board)(W)20 mm×(L)20 mm

Acceleration sensor

Power supply

1.8 ~ 3.6 VDC±10% / 350 μA(ADXL327)

Minimum full scale range±2g

Band width (X axis)

0.5 ~ 1600Hz

Band width (Y axis)

0.5 ~ 1600Hz

Band width (Z axis)

0.5 ~ 550Hz

36

Figure 2.5

Figure 2.6(a)Figure 2.7

Movement part of air cylinder

Condition of air pads when attached and not attached to surfaces

(b)

(c)

Electronic equipment installed on the proposed inspection robot, (a) Solenoid
valve for compressive air, (b) Solenoid valve for vacuum air, (c) Air cylinder
37

Figure 2.8

Movement system for forward motion in one cycle

Figure 2.9

Movement system for rotation motion in one cycle

Figure 2.10

Appearance of elastodur air pads and nitrile rubber air pads
38

Figure 2.11

Condition of air pads while edge is rolling up by friction force

Figure 2.12(a)Figure 2.13

Proposed sponge bellows air pads(b)
Attachment condition, (a) Chloroprene sponge when a horizontal force isapplied, (b) Sponge bellows air pads when a horizontal force is applied

39

Figure 2.14

Figure 2.15

Appearance of proposed silicone bellows air pads

Attachment condition and robotic movement on a corroded steel surface

40

Figure 2.16

Attachment condition and robotic movement on an exterior concrete surface

Figure 2.17

Attachment condition and robotic movement with upside down condition

41

Figure 2.18

Target surfaces on which the proposed robot is assumed to work on

Figure 2.19

Schematic image of determining the gap coefficient42Figure 2.20

Results of calculating gap coefficient

Figure 2.21

Results of calculating suction force

43Figure 2.22

Inspection robot during a performance test of robotic movement on an outside
concrete wall

Figure 2.23

Appearance of the target bridge
44

(a)

(b)
Figure 2.24

Performance test of robotic movement at the target bridge, (a)(b) Vertical member of the truss

45

Lower chord,Figure 2.25

Performance test of robotic movement on the trial bridge

46

47

Chapter 3

Capability of Robotic Inspection System—————————————————

Abstract———————————————For steel bridges, which consist of steel and concrete sections, it is necessary
to acquire damage information such as steel corrosion or deck deterioration.
In the previous chapter, a self-propelled inspection robot with the ability tomove on various surfaces was developed. In this chapter, inspection functions
to be installed onto this robot have been investigated, and the performance ofthe robotic inspection system has been verified through experiments on
several types of wall surfaces and trial bridges. On the other hand, inspectors
need to know the location of the inspection robot when the inspection robot
is used in areas that are blind spot or at locations where corrosion or cracks
exist. Thus, a function to identify the robot location using an attached camera
has also been developed and implemented on the robot.
——————————————————————————————————————

48

3.1. Overview
In a steel bridge inspection, it is required to acquire information such as steel corrosion, cracks ofconcrete decks, or changes in vibration characteristics of bridges or members, in an easy, safe andeconomical manner, without using scaffolding. In this research, robotic inspection has been
selected to realize the inspection. Robotic inspection therefore aims to perform inspections forobservation and measurement in robotic inspection concept. In the previous chapter, theinspection robot which is a self-propelled with the ability to move on various surfaces and angles
was developed. Therefore, in this chapter, capability of robotic inspection system has been
discussed and inspection functions which is implemented in the inspection robot has been
investigated.
Proposed robotic inspection includes visual inspection to observe surface conditions,vibration measurement to obtain structural behavior and corrosion damage evaluation method to
obtain corrosion information of bridges. Visual inspection is considered as part of observation.
Vibration measurement of bridges using acceleration sensors is considered as a part ofmeasurement. A corrosion damage evaluation method has also been developed for measurement
yet this method will be explained in the next chapter. Furthermore, the proposed robot is expected
to perform inspection at locations that are invisible or difficult to approach by inspectors.
Therefore, obtaining information of the robot location is important for the robotic inspection
system. In this research, the image processing applying Phase-Only Correlation method has been
investigated to build a location identification method, as investigated in this chapter.

3.2.

Functions for the Proposed Robotic Inspection System

The proposed robotic inspection system includes functions to observe surface conditions andmeasurement functions. In addition, location identification and mapping function was also
investigated.
3.2.1. Visual Inspection Function
The key advantage of using the proposed visual inspection function is that it can easily inspectareas where visual inspection is normally difficult. Moreover, it can significantly reduce
inspection cost and secure safety by eliminating the scaffolding or inspection passages requiredby inspectors. In this research, the visual inspection function focused on two points; the ability to
distinguish between different colors and to check detailed surface conditions. However, the robot
must remain relatively small in size to be able to inspect narrow areas. Hence, a small and light49

CCD camera maintaining adequate quality to consider filming area, color identification, andimage quality to perform visual inspection was selected. The proposed robot has a CCD camera
for visual inspection, named hereafter visual inspection camera, in front of the robot, plus anothercamera, named hereafter surrounding camera to view the surrounding areas (Figure 2.4(c)).
Since the visual inspection camera is wide angled and the images are taken at a considerablyclose distance to the surface, distortion similar to a fisheye lens occurred. Although the center ofthe image can be taken relatively well, distortion appears towards the edge. In this research,
location identification is performed by using images taken with this camera yet it was found thatthe accuracy of location identification became extremely poor if analysis was performed without
distortion correction. In order to eliminate this distortion, camera calibration was carried out. The
distortion correction was made by using values such as distortion coefficients obtained by thecamera calibration process. For the camera calibration process, the distortion correction was
performed applying the Zhengyou Zhang calibration method [100], and further distortion
coefficient was obtained using a checkerboard. Figure 3.1 shows an original image (left) and an
image after applying distortion correction (right). The right side image in Figure 3.1 indicates the
distortion correction was successful.
The visual inspection camera is attached to the inspection robot with the height of the lenspositioned 50 mm from the structural surface being viewed. In the location identification function
which uses images taken by this camera, securing the filming range of images is important. This
50 mm is the minimum necessary height for securing this filming range. The visual inspection
camera is 250,000 pixels, in which is far lower in resolution than commercially available digital
cameras which are around 20 million pixels. However, for the visual inspection function, thefilming range of images can be reduced to obtain detailed information of surface conditions. For
this reason, it was considered that this CCD camera could be used as the visual inspection camera,
and so the accuracy of the images obtained were then verified. Here, verification of a filming
range 50 mm from the surface, distinguishing between colors using a color chart, and the image
quality using a 6 mm × 6 mm grid was performed to evaluate the applicability of the visualinspection camera (Figure 3.2). By these images, it is possible to view a range of (W) 60 mm ×(L) 45 mm using a 640×480pixel range in detail. Although the image taken by the visualinspection camera is slightly blue and the filming range is small, it is still able to take images
which do not greatly differ in color recognition and image quality to the digital camera images,
so long as a 640×480pixel resolution is used.
Figure 3.3 shows an image taken by the visual inspection camera in the performance test ofvisual inspection at the existing bridge referred to in the previous chapter. The right side of the
figure shows the image attached with the actual scale adjusted to the image from the relationshipbetween the pixel value and the actual size. The image was taken when performing inspection of50

a vertical member of the truss. From Figure 3.3, it is possible to see the corrosion condition of
the steel surface, the uneven surface condition, and furthermore, the color difference using thevisual inspection camera. Based on the results of basic performance tests and the images taken of
the target surfaces, it has been clarified that the visual camera can take high quality images toperform effective visual inspection. Moreover, Figure 3.4 presents two images taken of a lower
chord, one by the visual inspection camera, and one by a forward facing surrounding camera. The
left-hand image was taken by the visual inspection camera, and the right hand image was takenby the forward facing surround camera. As shown, the visual inspection camera can take detailed
images of surface conditions such as peeling, cracks, color and roughness of the corrosion surface.
Furthermore, by using the surrounding cameras, the inspector can check the surface conditions of
not only close surfaces, but also the surface condition one step ahead of the robot. Therefore, theinspectors are able to estimate the sections which need to be inspected in real time, by assessing
the condition of the surfaces ahead of the robot.
3.2.2. Location Identification Function to Determine the Inspection Location
The inspection robot is required to work in areas visually unclear for inspectors, as the target areas
considered are either narrow to perform visual inspection, difficult to access or are invisible. For
this reason, it is important to identify the location of the robot.
There are some methods to identify the location of the robot using GPS, ultrasonic waves orlasers [101,102]. However, these methods have some difficulties to apply to location identification
function for the robotic inspection. The possibility that a GPS may not work properly in the target
parts of this robot due to closed areas is considered. Moreover, it is considered preferable to avoid
installing new equipment since the inspection robot is made relatively small. Hence, location
identification method using pictures taken by the visual inspection camera was built and installed
as one of the inspection function. The Phase-Only Correlation method (POC Method) that uses
an image matching process such as fingerprint authentication program, has been applied to makea function program [103-105]. Location identification method is carried out using numericalcalculation software MATLAB.
3.2.2.1. Phase-Only Correlation Method
The flow of the POC method is shown in Figure 3.5. Firstly, Fourier transformation is performed
for two different images, to obtain amplitude and phase. In this method, analysis is performed
using only phase information to place emphasis on the edges of images and omit information
regarding brightness. Two phase images are then combined to obtain the peak value of the POC
function. This peak value demonstrates how much the position between two images are changed.
Considering two 𝑁1 × 𝑁2 pixel images, 𝑓(𝑛1 , 𝑛2 ) and 𝑔(𝑛1 , 𝑛2 ), where the index ranges are
51

assumed as 𝑛1 = −𝑀1 , … , 𝑀1 and 𝑛2 = −𝑀2 , … , 𝑀2 for mathematical simplicity, 𝑁1 =−2𝑀1 + 1 and 𝑁2 = −2𝑀2 + 1. Letting 𝐹(𝑘1 , 𝑘2 ) and 𝐺(𝑘1 , 𝑘2 ) denote the 2D DFTs of theimages, the cross-phase spectrum 𝑅𝐹𝐺 (𝑘1 , 𝑘2 ) can then be defined as:

𝑅𝐹𝐺 (𝑘1 , 𝑘2 ) =̅̅̅̅̅̅̅̅̅̅̅̅
𝐹(𝑘1 , 𝑘2 )𝐺(𝑘
1 , 𝑘2 )̅̅̅̅̅̅̅̅̅̅̅̅
|𝐹(𝑘1 , 𝑘2 )𝐺(𝑘1 , 𝑘2 )|(3.7)

where 𝑘1 = −𝑀1 , … , 𝑀1 , 𝑘2 = −𝑀2 , … , 𝑀2 and ̅̅̅̅̅̅̅̅̅̅̅̅
𝐺(𝑘1 , 𝑘2 ) denotes the complex conjugate of𝐺(𝑘1 , 𝑘2 ).
The POC function 𝑟𝑓𝑔 (𝑘1 , 𝑘2 ) is the 2D IDFT of 𝑅𝐹𝐺 (𝑘1 , 𝑘2 ) and is given by:

𝑟𝑓𝑔 (𝑘1 , 𝑘2 ) =

1
−𝑘 𝑛
−𝑘 𝑛
∑ 𝑅𝐹𝐺 (𝑘1 , 𝑘2 )𝑊𝑁1 1 1 𝑊𝑁2 2 2
𝑁1 𝑁2

(3.8)

𝑘1 𝑘2

where 𝑊𝑁1 = 𝑒 −𝑗∗2𝜋/𝑁1 and 𝑊𝑁2 = 𝑒 −𝑗∗2𝜋/𝑁2 . When two images are similar, their POC
function 𝑟𝑓𝑔 (𝑘1 , 𝑘2 ) gives one distinct sharp peak. On the other hand, the peak significantly
drops, when two images are not similar to each other. Moreover, when the two images are shifted,
the position of the peak shifts from the center. The shifted value from the central point shows how
much two images change in position. However, what is obtained here is a pixel value, which needs
to be collected to the actual length.
3.2.2.2. Location Identification Method and Mapping Function
A detailed outline of the location identification function is as follows. One picture is taken by theattached camera for each cycle of robotic movement, and then using consecutive two images, thevalues of parallel translations in both horizontal and vertical directions (hereafter named, x and y
direction) and a rotation angle can be obtained. The proposed robot can rotate approximately 10 °in one rotation cycle. Therefore, to determine the rotation angle, the image after rotation is rotatedbetween −15 ° to +15 ° with 0.1 ° pitch. The rotation angle and the values of x and y are obtainedusing the POC method at same time. By summating all obtained values, the total paralleltranslation and rotation angle from the beginning to the end of the robot movement can becalculated.
In the location identification function, the location of the robot could be determined
numerically. However, when the robot is working at sections that are out of visible sight, it isdifficult to identify the real location of the robot from the obtained values. Therefore, another
CCD camera, surrounding camera, has been attached to look 170° in front as well as a mapping
function of the motion track of the inspection robot to help inspectors determine the location of52

the robot visually. It is expected to be an inspection database to record inspection results andrecognize progressing of corrosion or cracks comparing previous inspection maps. An example
of applying location identification and mapping function is presented in Figure 3.6. Figure 3.6(a)shows the obtained images from the inspection robot at the beginning point and after one
movement. The precise method of this function is the implementation of a 1010 × 1010 pixel
basemap and the images taken by visual inspection camera continuously. First, one image is taken
at the starting position of the robot, and this is attached to the center of the basemap (Figure
3.6(b)). Second, using the obtained values from the location identification function after the robot
moves (Figure 3.6(c)), the new image is attached to the aforementioned image. This cycle isrepeated and so that all images can be attached in order (Figure 3.6(d)). Finally, the entire map iscompleted by combining all the basemaps. Moreover, by implementing a shutter to the camera ofthis program, it is possible to retake images when the obtained result is completely different due
to shadow issues. Figure 3.7 shows a schematic image of the location identification function
using the inspection robot. In this function keeping intersection part as shown in Figure 3.7 isimportant. Therefore, lens height was decided as 50 mm from surfaces. During or after inspection,using mapping results, the inspectors can detect cracks or corrosions and their size. Method todetect them is so far using the images or movies from camera however another method which
performing analysis using wavelet transformation to detect cracks automatically in images will
be investigated in following subsection.
3.2.2.3. Accuracy Improvement Process of Location Identification Function and Performance
Test at Concrete Wall
The following test was performed on a concrete with good surface conditions which is case 7 intarget surfaces. The initial position and end position of the robot can be seen in Figure 3.8. The
movement was three forward motions, two clockwise rotations, and then seven motions forward,
hence the total movement was 61 mm in the x direction, 165 mm in the y direction, with a rotation
angle of 19 °. The result of analysis, in the x direction is 54.45 and, 149.76 mm in the y direction
with a rotation angle of 18 °. The relative error was approximately 10 % which is considered high.
The allowable error of this function was considered around 5 %. The location identification
function is summing the all obtained values and so relative error may increase. However, when
the robot moved only forward, the error was significantly smaller.
To decrease the relative errors, make the calculation pitch of rotation angle smaller (0.01 °),remove the distortion parts, insert deblurring step, put scaling factor and use the average of peakvalues of POC function if the difference between the peak and other peaks is less than 0.001. After
applying distortion correction, there are still some parts distorted thus remove the edge. Although
the value is depending on the images, the area of 90 % to 95 % from the center has used for53

calculation. When the results show obvious error, most cause is picture blur or shadow. Therefore,
deblurring step which uses for camera shake correction was applied. The height of the visualinspection camera is not always same due to attachment condition of the inspection robot. Whenthe height of camera changes, the scale of the obtained images also changes. Therefore, to correct
this scaling, similar method to calculating rotation motion was applied. The scaling factor is 0.9
to 1.1 with 0.01 pitch.
The results of this test after modification are shown in Table 3.1. As shown, the total
movement was 63.9 mm in the x direction and, 164.5 mm in the y direction, with a rotation angleof 20.5 °. The maximum relative error becomes half of the previous method and it was consideredappropriate and so, it has been confirmed that the location identification function performs wellwhen used on real structures. The mapping result, a map of the robot tracks, is shown in Figure
3.9. It is able to confirm that the modified analysis is tracking robot’s movement better, hence
demonstrate that the mapping function can track the movement of the robot effectively. It has thus
been confirmed that the location identification function has the capacity to work in real inspection
sites.
However, this performance test is very short movement. Therefore, to confirm if the accuracyof this function increases due to movement distance, another performance test at concrete wall
similar condition to previous test was carried out. The initial position and end position of the robot
are shown in Figure 3.10. In this test, the movement was thirty-two times forward motions, fiveclockwise rotations, and then twelve times forward, hence the total movement was around 223
mm in the x direction, 670 mm in the y direction, with a rotation angle of 46 °. The result ofanalysis, in the x direction is 215.23mm and, 643.80 mm in the y direction with a rotation angleof 44.08. Figure 3.11 shows the mapping result using obtained values. The maximum relative
error is 4.2 % in rotation thus the error became slightly increase. However, in Figure 3.11, there
is not big gap in the connection part of each images so that it needs to consider the way to obtain
more accurate values to change pixel to millimeters. It can also be able to consider the body of
the robot is twisting due to attachment conditions even the rotation angles of images in analyzing
are correct. Currently, the relative error is evaluated as a percentage, however this means that as
the movement distance increases, the error tolerance range is expanded. For this reason, theallowable error has been limited to being within a 150 mm diameter area. This range has been
determined by taking into consideration the size of the inspection robot, which currently has acable length of 10 m, and using this as a reference. Since the error did not increase in proportion
to the distance moved during experiment, the location identification function of the robot can beconsidered to possess sufficient precision. Especially if the inspection range is small, accuracy
becomes higher. These errors are considered to be permissible values yet some improvement
methods can be thought.
54

3.2.3. Crack Detection Function for Concrete Component in Steel Bridges
In the previous subsection, it was shown that the image obtained by the visual inspection camera
was able to observe the surface condition in detail, and the condition of damages such as cracks
and peelings due to corrosion could be confirmed. Moreover, with the mapping function inlocation identification, it is possible to record the inspection track as a map by connecting thecaptured image according to the movement of the robot. In addition to these functions, a crack
detection function was attempted to implement to the robotic inspection function in thissubsection. Since the range of the image of the visual inspection camera is narrow and can beseen detailed surface conditions, it is possible for the inspector to visually judge from the image.
For this reason, this function is supposed to be an optional function aimed at leaving it as aninspection record. The actual scale is known by the location identification function therefore, it
can be considered that it is possible to judge the degree of danger using the crack scale which isfor measuring the crack width used for crack inspection of concrete.
For the crack detection, a method applying Gabor wavelet function [106,107] wasinvestigated. First, edge emphasis of obtained images is performed (Figure 3.12(a)). Next, Gaborwavelet transformation is applied to the images (Figure 3.12(b)). Then, cracks are judged using
threshold (Figure 3.12(c)). Finally, noise is eliminated by crack shape and length (Figure 3.12(d)).
In this method, not only cracks but also other things such as hole, dirt, shadow or spot mark can
be detected if it is over 0.2 mm, it is therefore, necessary to consider a method to separate themfrom cracks. It is considered that the holes or spot marks can be distinguished by shape such as
similar to circle or too short. Therefore, such holds can be eliminated by crack shape and length.
Since only cracks of over 0.2 mm are assumed to be a target in this study, evaluation standard isusing pixel area (2 pixels × 2 pixels: 0.19 mm × 0.17 mm) corresponding to 0.2 mm instead ofeach pixel. From Figure 3.12 (d), which is the result obtained by the above method, it was
confirmed that the part which is not a crack disappeared, only the crack part remained. The visualinspection camera can obtain images with detailed surfaces conditions therefore, this crack
detection function can be used as optional function to emphasis cracks or record conditions. Gaborwavelet transformation can be represented as follows;∞1𝑛1 − 𝑛10 𝑛2 − 𝑛20𝛹𝜃𝑘 (𝑛10 , 𝑛20 ) = 𝑘 ∬ 𝑓(𝑛1 , 𝑛2 )𝜓𝑔 (,) 𝑑𝑛1 𝑑𝑛2
𝛼
𝛼𝑘
𝛼𝑘

(3.9)

−∞

Where, (𝑛10 , 𝑛20 ) is focused point of wavelet transformation, 𝛹𝜃𝑘 (𝑛10 , 𝑛20 ) is wavelet
coefficient of Gabor wavelet, 𝜓𝑔 is Gabor function and 𝛼 𝑘 is scaling factor of wavelet
transformation.
55

3.2.4. Vibration Measurement Function
Not only the visual inspection camera but an acceleration sensor is attached to the robot torecognize structural behavior as detailed inspection. For example, it is able to obtain performancelevel of bridges using acceleration [108]. A natural frequency of healthy bridge shows high anddamaged bridge shows low natural frequency. Therefore, measuring bridge vibration periodically,
the possibility of evaluating if the bridge got some damage in the middle of inspection interval
can be considered. Measuring bridge behavior by acceleration sensor is normally performed using
several acceleration sensors yet using the inspection robot, it can measure acceleration whilemoving. Therefore, it is conceived that it becomes possible to obtain bridge behavior without
disposing of a plurality of acceleration sensors.
In this subsection, to realize aforementioned measurement, focusing on getting response
spectrum, dominant frequency, vibration characteristics by obtaining acceleration data ofstructures and analyzing the data. Advantage of using inspection robot for determining structural
behavior is that it is difficult to replace the acceleration sensor on real structures however using
this robot, it becomes easy to move and replace its position. Furthermore, it is also possible tocheck the surface condition by attaching a camera, while the inspection robot is moving. Thus, it
is easy to measure the acceleration if the inspector judges it necessary. This inspection robot isdesigned for estimating the sections that are needed to inspect before the accelerometer has been
disposed in real case scenarios.
3.2.4.1. Investigation of Vibration Characteristics of Proposed Robot
The proposed inspection robot was confirmed to have enough suction force to attach several typesof surfaces, however considering to install acceleration sensor, robot vibration may be influenced
to obtaining bridge vibration. It is considered that air pads have the possibility to affect robot
vibration due to nonlinearity of rubber behavior. Therefore, it is important to confirm the influenceof air pads to obtained vibration by the inspection robot. A laboratory experiment using vibration
generator was performed to evaluate suction condition and vibration characteristics of theinspection robot. In particular, data from an acceleration sensor attached to the inspection robot,
named hereafter robot acceleration sensor, and direct attached acceleration sensor are compared
to confirm if the robot fixed to the surface enough to obtain bridge vibration. Figure 3.13 shows
sensor arrangements. A base plate was attached to the vibration generator and its natural frequency
was calculated 250Hz by eigenvalue analysis in FEM. Shell model was used for the analysis. Bothacceleration sensors are same as indicated in Table 2.6.
In this test, the robot was attached on a base plate next to the direct attached acceleration
sensor (Figure 3.13) to know the vibration characteristics of air pads. Five waves which aretriangle wave of 1~5 Hz in 1Hz pitch were used. Figure 3.14(a) shows one of the input wave
56

which is triangle wave with 2 Hz. Figure 3.14(b) shows obtained acceleration data from both
robot acceleration sensor and direct attached acceleration sensor. A red line indicates acceleration
data from robot acceleration and a blue line indicates direct attached acceleration sensor. It was
confirmed that although the position and the amplitude of the peak coincided with both
acceleration sensors, the amplitude of the robot acceleration sensor increased with the vibration
after the peak. This phenomenon is considered to be the influence of the vibration characteristicsof the air pad. Figure 3.14(c) shows Acceleration Fourier Spectrum obtained by each acceleration
data. The colors of the lines are same as obtained acceleration data. The figure indicates that bothlines fit well, especially this graph shows the important frequency band in bridge structures. Here,to evaluate vibration characteristics of the inspection robot, a gain, a phase lag and a coherence
were used. Calculation method of a gain, a phase lag and a coherence are shown as follows.
When 𝑢(𝑡) represents an input signal and 𝑣(𝑡) represents an output signal, a function
indicating the relationship between them is generally called a transfer function. In this case, thedifferences in the amplitudes of the two waves and the deviations using transfer function which
is between both acceleration data of the robot acceleration sensor and the direct attachedacceleration sensor, are investigated. Here, focusing on a gain, a phase lag, and a coherence toinvestigate the relationship between two waves. In general, the transfer function is expressed by
the following equation.
𝑈(𝑠)𝐺(𝑠) = 𝑉(𝑠)𝑠 = 𝜎 + 𝑗𝜔

(3.10)

Where, 𝑈(𝑠) and 𝑉(𝑠) are Laplace transforms of input and output signals, respectively, and𝐺(𝑠) is a transfer function. The expression of Laplace transform is represented by complex
regions. However, in the case where the expression is limited to the frequency domain without
considering the transient phenomenon, simplification of the equation of 𝜎 = 0, 𝑠 = 𝑗𝜔 can be
possible. Note that j is conventionally used in imaginary units in control engineering. Therefore,
the transfer function 𝐺(𝑗𝜔) is expressed by the following equation.

𝐺(𝑗𝜔) =

𝑉(𝑗𝜔)
𝑈(𝑗𝜔)

(3.11)A gain indicates the absolute value of the transfer function logarithmically and is expressed by
the following equation. The unit is decibel [dB]. The meaning that if it is 0 at a certain frequency,
the amplitude of the output signal is equal to the input signal.

57

𝐺𝑎𝑖𝑛 [𝑑𝐵] = 20 ∙ log10|𝐺(𝑗𝜔)|(3.12)A phase lag is a phase angle of the transfer function, which means the phase shift of the output
signal with respect to the input signal in a certain frequency component and is expressed by thefollowing equation. In this research unit is indicated in degrees.
𝑃ℎ𝑎𝑠𝑒 𝐿𝑎𝑔 = ∠𝐺(𝑗𝜔)(3.13)A coherence represents the strength of the correlation between the input signal and the output
signal and is used as an index for measuring the reliability level of the transfer function. The
coherence is calculated as follows using the power spectrum 𝑊𝑢𝑢 , 𝑊𝑣𝑣 and the cross spectrum
𝑊𝑢𝑣 of the input signal u and the output signal v.

𝛾2 =|𝑊𝑢𝑣 |2
𝑊𝑢𝑢 ∙ 𝑊𝑣𝑣

(3.14)

2(0 < 𝛾 < 1)
The gain, the phase lag, and the coherence obtained using the acceleration data, obtained by the
direct attached acceleration sensor, as an input signal and the acceleration data, obtained by the
robot acceleration sensor, as an output signal are shown in Figure 3.15. From all graphs in Figure
3.15, it can be considered that disturbance of waveform at 50 Hz has been affected by power
supply noise. Moreover, natural frequency of base plate was calculated 250 Hz. Therefore, thenatural frequency of air pad can be considered 140 Hz. An amplitude of robot acceleration isbigger and phase lag occurs over 140 Hz, it was confirmed through the gain and phase lag graphs.
Finally, from coherence graph, the influence of robot was smaller and shaper than other signals
such as noise of power supply. Therefore, it can be considered the influence of robot can be
distinguished from other signals. It was confirmed all cases indicate similar results. Moreover, inall experimental cases, the influence of the air pad was not confirmed at frequency under 50 Hz.
Therefore, it can be possible to acquire the vibration characteristics of bridges when measuring
the acceleration of bridges using the inspection robot.
3.2.4.2. Performance Test of the Vibration Measurement Function
In this test, the robot was also attached next to an acceleration sensor that was attached directly
to the bottom flange of the existing bridge, as shown in Figure 3.16. The left-hand picture shows
the acceleration sensor attached directly to the bridge, and right-hand picture displays the setting
position of the inspection robot. Measurements were carried out for 5 minutes, with a sampling58

frequency of 1000 Hz. Figure 3.17(a) shows the acceleration Fourier spectrum of both
acceleration sensors. The blue line shows the acceleration sensor that was attached directly to thebridge, and green line shows the data obtained by robot acceleration sensor. Moreover, themagnified region shown in the lower side of Figure 3.17(b) highlights the important frequency
bands, excluding low frequency and high frequency from bridge vibration. As shown in theprevious subsection, the influence of robot vibration by the air pad was not confirmed, and it was
confirmed that the natural frequency of the bridge can be obtained by the acceleration sensor
installed on the inspection robot.
3.2.5. Hammering Test for Concrete Components in Steel Bridges
According to road administrators, if the inspection robot can perform hammering test in theregular inspection process, the inspection robot can reduce burden of inspectors. Moreover, it was
confirmed in the previous investigation, the inspection robot is able to attach surfaces tightly andvibration of robot itself can be distinguished from other signals. However, there is currently nohammer installed on the inspection robot hence, hammering test was simulated manually andvibration and sound generated by inspector’s hammer blow were obtained. Then a method to
obtain information regarding vibration or sound change, has been investigated for future use.
Concretely, the hammering test was conducted manually, and test whether the sensors
installed to the inspection robot can acquire the presence or absence of the internal defect inconcrete parts of steel bridges, was conducted. In addition to the acceleration sensor, a small
microphone was installed to the inspection robot for this test, and the sound data was also analyzed.
This function is also as an optional function, therefore, an investigation was conducted based on
experiments that have been conducted. The hammering test was conducted with the trial bridge
mentioned in Chapter 2. On the trial bridge, there is a wall that made a deliberate defect inside
the concrete made for hammering test exercises, and then experiments were conducted there. A
specification of the microphone is shown in Table 3.2.
For this experiment, the inspection robot was attached close to the hitting points during
hammering tests. The experiments that measuring vibration and sound with the acceleration
sensor and the microphone were conducted (Figure 3.18). In this test, wall surface for hammering
test on an inside of an abutment was used, and measurements were made at places where thepresence or absence of internal defects was known. The results of the vibration data obtained by
the acceleration sensor with respect to the internal defect surface and the healthy surface areshown in Figure 3.19(a) and Figure 3.19(b), respectively. The acceleration sensor installed tothis inspection robot is a MEMS type sensor, and data up to 1500 Hz can be acquired. From
Figure 3.19(a) and Figure 3.19(b), it can be confirmed that the surface with internal defects has
peaks with many frequency components. Moreover, it can also be confirmed that the frequency
59

range showing the peak is low on the surface having the internal defect. It was also confirmed thesound with the internal defect was lower than without defects at the time of hammering test,
however, the sound was assumed to indicate a peak in a higher frequency range compared to theresults of acceleration sensor. For this reason, it is expected that there is another frequency range
shows sharp peak which cannot be acquired by this acceleration sensor yet, it can be considered
the differences in applicable frequency range are able to acquire by the acceleration sensor.
On the other hand, as a results of hammering test using the microphone are shown in Figure
3.20. It was confirmed the sound at the surface with the internal defect mixed with more soundcomponent than the sound at the surface without internal defect similar to the result of theacceleration sensor as shown in Figure 3.19. Furthermore, it was indicated that the sound of thedefect part is low as well as confirmed by the acceleration sensor. The peak shows around 2000
Hz and the relationship between the real pitch and the frequency is also in good agreement.
Finally, the results indicate that it is possible to detect internal defects by both acceleration
sensor and microphone. An advantage of using vibration data instead of sound data for hammering
inspection, since the path through which vibration propagates is not an air but an individual,
attenuation is smaller than that of sound recording and data analysis of three axes is possible. If
data analysis method of hammering test using acceleration is established, more detailed analysis
may be possible by using data of three axes. In addition, if the acceleration sensor is used forhammering test, the inspection robot can install other inspection devices to the space of the
microphone. Moreover, results indicate the possibility of acquiring structural behavior changes
by deterioration of decks using obtained acceleration data.
3.2.6. Inspection Simulation based on the Currently Proposed Robot
In this section, the robotic inspection functions which the proposed inspection robot can performhave been further discussed. The capacity of the proposed inspection robot, such as movement
speed, that was determined in Chapter 2 has been used as reference. In this subsection, theinspection time has been calculated assuming that an inspection is performed within a range of(W) 240 mm × (L) 200 mm after the proposed inspection robot has moved five meters above a
set position. Table 3.3 shows information regarding to the movement and inspection duration of
the proposed inspection robot during the simulated inspection. Table 3.4 shows the calculation
results. Case 1 assumes that location identification is performed during each movement. On theother hand, since the movement distance of forward movement can be calculated by the numberof movements (one forward movement is equal to 20 mm), Case 2 assumes that location
identification is not performed during the movement up to five meters above the initial set position.
Moreover, in Case 2, although pictures of the surface are taken after each movement, location
identification is only performed after the inspection is completed. During the simulated inspection,
60

the time to take pictures is included in the measurement time of 2.5 seconds, as well as the
movement time for both Case 1 and Case 2. Currently, movement takes up most of the total
operation time for inspection.
However, the speed of the proposed inspection robot and the distance it can move in one
movement can still be improved, and this would significantly reduce the total operation time. The
main advantage of the proposed inspection robot is that it can collect information for eachmeasurement point, however many there are. It is considered that the data from this simulated
inspection can be used to optimize maintenance planning.

3.3.

Summary

In this chapter, functions of the robotic inspection system implemented on the inspection robot
developed in Chapter 2 was examined. Small inspection equipment applicable to the proposed
inspection function was implemented and its performance was also verified. Summaries of eachinspection function is shown as follows.
 Visual inspection function
Visual inspection was carried out using images taken by the small CCD camera installed on
the front of the inspection robot. From the results of performance tests of visual inspection, it
was confirmed that obtained images from the extremely short distance have adequate quality
for visual inspection. By this visual inspection function, it became possible to observe detailed
surface conditions such as corrosion of steel members and cracks of concrete decks.
 Location identification and mapping function
When the inspection robot is used at areas that are invisible or the location of existing corrosion
or cracks, inspectors need to know the location of the robot. In consideration of the size of therobot and the situation of inspection sites, in this research, a location identification function
using obtained images by visual inspection camera was implemented to the inspection robot.
This function traces the movement trajectory of the inspection robot by calculating the
movement of the image before and after the robot movement. Furthermore, using values
obtained by the location identification function, a mapping function that connects the obtained
images as a map was also implemented. The image obtained by the mapping function ispreserved as an inspection record. Thus, it is a function assumed to use for judging the progresssituation of corrosion and cracks.
 Crack detection function
A function to detect cracks using Gabor wavelet transformation applying to the image obtained61

by the visual inspection camera as an optional function was implemented. The visualinspection camera has a narrow filming range, therefore, it is possible to check cracks of over
0.2 mm visually. As an advantage, this function visually emphasizes the crack part and thecrack width and length can be determined from the image.
 Vibration measurement function
This is a function to obtain bridge vibrations using the acceleration sensor installed to theinspection robot. Laboratory experiments were conducted to investigate whether the vibrationof the robot itself affects the acquisition of natural frequency of bridges. As a result, theinfluences of obtaining bridge vibrations due to the inspection robot, mainly the air pad was
not confirmed. It is because natural frequency of the robot is around 150Hz and it is higher
than natural frequency of bridges. Moreover, the possibility of acquiring bridge vibration was
confirmed by performance test at existing bridge.
 Hammering test function (optional function)
Currently, the inspection robot has no hammer for hammering test. However, for the futureuse, the method to detect the internal defects of concrete components of steel bridges using thevibration and the sound, which becomes the basis after introducing the hammering system inthe future, was verified.
Performance and applicability of all functions were confirmed by experiments at the target
surfaces, the existing bridge or the trial bridge mentioned in Chapter 2. In the next chapter, acorrosion damage evaluation method will be investigated. It is assumed that the method will be
used as an independent inspection method and also as a detailed inspection function of theinspection robot.

62

Figure 3.1

Original image taken by the visual inspection camera and image after applying
distortion correction

Figure 3.2

Figure 3.3

Filming range and color discrimination of the visual inspection camera

Image taken by the visual inspection camera at vertical member of truss inexisting bridge
63Figure 3.4

Images from both the visual inspection camera and surrounding camera

Figure 3.5

Flow of the POC method

64

(a)

(b)

(c)

(d)
Figure 3.6

Example of applying the location identification and mapping function,(a)Images taken at the initial and after one movement of the inspection robot, (b)Initial image attached on the base map, (c) Result from applying the location
identification function, (d) Image from applying the mapping function

65

Figure 3.7

Schematic image of the location identification method using the inspection robot

Figure 3.8

Initial and end position of the robot in the performance test66

Table 3.1

Result of location identification after modificationNo.

x (pixel)y (pixel)

x (mm)x (mm)θ (degree)

1

0

0

0

0

0

2

35

198

1.8

19.8

0.9

3

-44

202

-2.3

20.2

-0.5

4

-26

181

-1.3

18.1

-1

5

250

20

12.9

2

9.1

6

280

-29

14.5

-2.9

18.6

7

97

157

5.0

15.7

19.1

8

96

156

5.0

15.6

18.8

9

104

155

5.4

15.5

18.2

10

100

159

5.2

15.9

18.7

11

110

152

5.7

15.2

19.3

12

120

148

6.2

14.8

19.7

13

115

150

5.9

15

20.5

Total

63.9

164.9

20.5

Real

61

165

19

Error

-2.9

0.1

1.5

Figure 3.9

Image from applying the mapping function
67

Figure 3.10

Initial and end position of the robot in the second performance test

68Figure 3.11

Mapping result of the robot from the second performance test

69

(a)

(b)

(c)
Figure 3.12(d)Method to detect cracks in concrete components of steel bridges, (a) Original
image and edge emphasis process, (b) Process of applying Gabor wavelet
transformation, (c) Crack assessment process, (d) Noise reduction process

Figure 3.13

Sensor arrangements for the laboratory experiments to evaluate vibration
characteristics of the inspection robot
70Direct attached

(a)

Robot

(b)

Direct attached

Robot

(c)
Figure 3.14

Input and output signal of the experiments, (a) Input triangular wave of 2 Hz, (b)Output acceleration wave, (c) Acceleration Fourier Spectrum from obtained
acceleration data

71

(a)

(b)

72

(c)
Figure 3.15

Gain, phase lag, and coherence as evaluation indexes, (a) Gain, (b) Phase lag,(c) Coherence

Figure 3.16

Measurement conditions when obtaining bridge vibration
73

(a)

(b)
Figure 3.17

Experimental results of measuring bridge vibration, (a) Full Acceleration Fourier
Spectrum, (b) Magnification

74

Table 3.2

Micro amplifier

Condenser microphone

Figure 3.18

Specification of microphone

Offset voltage±0.2 mV (max: ±1mV)

Input bias current±0.2 pA (max: ±10 pA)

Input voltage noise

8 μVrms

Input voltage range±0.3 mV

Input impedance

1013 || 6 Ω || pF

Output

1 mV

Power supply

2.2 to 5.5 V

Temperature range-40 to 85 ℃

Diameter

9.7 mmHeight

6.7 mmSensitivity (0 dB = 1V / pA )-42.0±2.0 dB

Directivity

Omnidirectional

Power supply

1.5 V

Operating voltage range

1.0 to 10.0 V

Maximum current

0.5 mA

Terminal

PIN

Housing material

Aluminum

S / N ratio

More than 60.0 dB

Frequency range

50 to 16000 Hz

Condition of the robot during the hammering test75

PSD (Acceleration)
PSD (Acceleration)

(a)

(b)
Figure 3.19

Hammering test results using the robot acceleration sensor, (a) Surface with aninternal defect, (b) Healthy surface

76

PSD (Sound)Figure 3.20

Hammering test results using the microphone

Inspection condition and information of movement and inspection duration of theTable 3.3

inspection robot
Inspection
Target location

5 meters above set position

Measurement area

Width: 240 mm / Length: 200 mm

Inspection robot
Movement time (Forward movement)

3 seconds

Movement time (10 degrees rotation)

5 seconds

Location identification time

5 seconds (average)

Measurement time

2.5 seconds

Time for taking pictures

2 seconds

Table 3.4
Number of movements(During measurements)
Case 1Case 2

298
(98)
298
(98)

Calculation results of inspection duration
Movement time

1430 s1430 s77

Location identification time
(Measurement time)
1490 s(including 245 s)
190 s + (245 s)

Total
operationtime
49 min31 min78

Chapter 4

Proposal of Corrosion Damage Evaluation Method—————————————————

Abstract———————————————

Corrosion causes severe damage to steel bridges. Therefore, by focusing on
corrosion damage, a method which is able to concurrently analyze various
corrosion damage conditions such as local thickness reduction or theformation of a rust layer on steel members has been investigated in thischapter. A method based on eddy current testing has been proposed. In thisresearch, a relatively low frequency eddy current between 1 and 1000Hz has
been considered for the proposed corrosion damage evaluation method,which performs inspection from only one side of a steel members. Thismethod has the potential to be a simple non-destructive testing method.
Moreover, this method can be used both as an independent measurement
system as well as one of the robotic inspection functions.
——————————————————————————————————————

79

4.1.

Overview

In steel bridges, corrosion causes severe damage which consequently lowers the safety anddurability of the bridge. Recently, many cases of traffic restrictions, reinforcement repair works
and replacement work due to severe corrosion damage on steel bridges have been reported[24,68,109,110]. If significant corrosion occurs on main structural members and impacts a large
area, the economic burden increases as the area requiring maintenance increases. Therefore, aninspection and measurement system specifically for corrosion damage is important.
Corrosion tends to occur in areas that are narrow or complex in structure as these areas areoften humid or are not rain-washed. However, it is difficult to perform inspections of narrow
locations and places where the arrangement of members is complicated. Moreover, it has been
made clear through past research that corrosion damage lowers the load-carrying capacity of steel
members [69-77]. Hence, much research has been carried out to develop evaluation methods ofthe remaining load- carrying capacity by using the ratio of the designed thickness to the residual
thickness of steel members [71-77]. If it was possible to obtain the residual plate thickness of steel
members during inspection, it would enable the remaining load-carrying capacity to be evaluatedusing one of the aforementioned evaluation methods, and thus be an effective inspection method
for steel bridges. However, evaluating residual thickness by visual inspection using conventional
inspection processes have proven difficult. Caliper or ultrasonic testing is methods have attempted
to evaluate residual thickness, however these testing methods are limited due to the requirementof surface treatment and the large number of sections that are difficult to access. Hence inspection
methods which require surface preparation should be avoided. In this research, eddy current
testing was investigated for corrosion damage evaluation as it is one non-destructive testing
method which does not require surface preparation. However, methods using eddy current inprevious research [86-96] required relatively large power consumption and the need of large
equipment, which poses a problem during field measurements. Although the use of small probes
has also been investigated, there are still problems as the actual thickness range that can bemeasured is small and so it is difficult to detect detailed damage conditions such as the thickness
reduction due to local corrosion.
In this chapter, a new approach to corrosion damage evaluation based on eddy current testinghas been proposed. The inspection system was made compact and potable to detect local
information with greater ease. To obtain corrosion damage information such as rust thickness andresidual plate thickness, relatively low frequency eddy current, between 1 and 1000 Hz, wasapplied. Corrosion of steel members in actual structures can be distinguished by three patterns,
where corrosion occurs on either the front or back surfaces or both surfaces, as shown in Figure4.1. Moreover, considering the accessibility of actual structures, a method to acquire corrosion
80

damage conditions by taking measurements from either the front or back surface of member only
was also examined as shown in Figure 4.1. To develop the proposed corrosion damage evaluation
method, firstly, differences in output signal characteristics due to damage conditions wereclarified using numerical simulation. Further data analysis was then carried out to investigate thedifferences in output signal characteristics. Next, a sweep wave that can obtain various
information regarding plate thickness during one excitation was designed as the excitation
waveform. An evaluation method using either a detected voltage or both a detected voltage andphase as indexes was then examined. The phase in this research focuses on the phase lag of an
input current with respect to the detected voltage. Finally, based on the results of all theaforementioned steps, two corrosion damage evaluation methods that consider the characteristics
of corrosion damage were proposed. One method uses only the detected voltage to evaluate
corrosion damage, and the other uses both the detected voltage and the phase. Performance andefficiency of the two proposed methods were clarified by experiment. Furthermore, the proposed
methods have been developed to be used as both an independent measurement system as well as
one of the robotic inspection functions. Thus a method to install an eddy current probe onto theinspection robot and its consequent performance was also examined.

4.2.

Basic Concept of Eddy Current Testing Method

Eddy current testing is one electromagnetic testing method used in non-destructive testing, andmakes use of electromagnetic induction to detect and characterize surface and sub-surface flaws
in conductive materials. Eddy current flows through conductive material surfaces when a coil thatgenerates an alternating magnetic field is placed on the surfaces of conductive materials as shownin Figure 4.2. The conductor then generates an electromotive force that hinders the magnetic fluxchange through the conductor. This current is called eddy current from its shape. The eddy current
phenomenon can be expressed by the model shown in Figure 4.3. The secondary coil substitutes
a target conductor and hence can be considered as the resistance of the measurement target.
Figure 4.3(b) shows a circuit diagram of Figure 4.3(a). The left side of Figure 4.3 is a primarycoil and the right side is a secondary coil. The secondary coil represents the measurement target.
In Figure 4.3, V1 indicates the AC power source with a sinusoidal wave, and R1 and R2 indicate
resistance, I1 and I2 indicate current, L1 and L2 show inductance, and ωL1 and ωL2 are reactanceof the primary and secondary coil respectively. When an alternating current is passed through theprimary coil, the inductance in the primary coil side L1 creates a magnetic flux. An electromotive
force is then generated in the inductance L2 by the magnetic flux, and a current I2 flows throughthe resistance R2 connected to the inductance L2. It should be note that R1 is the coil resistance,81and the resistance of the measurement target is R2. A secondary current, which flows to thesecondary coil side, generates a secondary magnetic flux in a direction the cancels the magnetic
flux of the primary current by the law of electromagnetic induction. This phenomenon changes
the electromotive force due to the self-induction of the primary circuit. Therefore, theelectromotive force of the primary circuit varies depending on the value of the resistance R 2 ofthe secondary circuit.
Penetration depth 𝛿, which is considered to be an important parameter in obtaining plate
thickness information, is determined from the frequency f of the excitation current, the relative
permeability 𝜇 and the electric conductivity 𝜎 of the target conductors. The penetration depth𝛿 represented by the following equation is the depth at which an eddy current of constant strengthis generated in a target conductor and shows the value at which the eddy current is 1⁄𝑒 of thevalue at the surface. In addition, an eddy current can also exist deeper than the penetration depth,
and its depth changes due to the presence of defects.

𝛿=

1

(𝑚)

√𝜋𝑓𝜇𝜎

(4.1)

Based on Equation (4.1) the penetration depth becomes larger if a lower frequency is used. In
addition, when performing the eddy current testing, the penetration depth is affected byphenomena such as eddy current loss in high frequency bands, as described later. In this research,the method to acquire corrosion information such as thickness of steel members was examinedconsidering this particular phenomenon.
Detected voltage vv is an important parameter in eddy current testing. The relationshipbetween the detected voltage 𝑣𝑣 and phase transition 𝜙 of the voltage based on a current andalso impedance 𝑍 is expressed by the following equation.
𝑣𝑣 = 𝑉𝑚 𝑠𝑖𝑛(𝜔𝑡 + 𝜙)

(4.2a)

𝑍 = √𝑅 2 + (𝜔𝐿)2(4.2b)

𝑉𝑚 = 𝑍𝐼𝑚(4.2c)

Analysis and experimental results in this research have been evaluated using 𝑉𝑚 in Equation(4.2c). That there are cases where evaluation has been performed by using an impedance plane inconventional eddy current testing [111,112] for surface or sub-surface crack detection. Therefore,
rather than just using the detected voltage as an evaluation index, using the phase between the
input current and the detected voltage as an evaluation index and the corresponding evaluation
method will also be examined.
82

4.3.

Numerical Simulation on Characteristics on Low Frequency Eddy
Current

In this section, an analysis is performed assuming a difference in damaged condition due tocorrosion of a steel plate using low frequency eddy current of 1 to 1000 Hz. The characteristicsof the detected voltage and the phase in the eddy current testing is grasped in order to acquire thecorrosion damage information such as thickness of rust layer and residual plate thickness. In thisanalysis, using a carbon steel flat plate model to obtain corrosion damage information. The rustlayer and the residual plate thickness were modeled as lift-off, which is the relative distance
between coil and conductor surface and plate thickness (Figure 4.1). Analysis was carried out
using plate thickness, lift-off and excitation frequency as parameters, thus the influence of eachparameter change on the detected voltage and phase was investigated.
The lift-off in eddy current testing includes position change by operations, coating films ofmembers, corrosion depth due to corrosion products, and their combinations, yet operation
problems are not handled in this research. Generally, since members are painted, lift-off isassumed corrosion depth and coating films. Moreover, there are also materials, which are without
coating films such as weathering steels [19,21-23] so that lift-off in this study is treated as"corrosion depth or a composite of coating films and corrosion depth". In this research, 2dimensional axisymmetric analysis by general-purpose physics simulation software COMSOLMultiphysics [113-115] was used.
4.3.1. Analysis Conditions
Since the eddy current testing is a non-destructive inspection method using electromagnetic
phenomena, governing equation is given by Maxwell's equation. The frequency used for the eddy
current testing in this research is a relatively low frequency therefore, terms relating to thedisplacement current in the Maxwell equation can be neglected. Equations satisfying the magnetic
field are shown as follows:∇ × 𝑩/𝜇 = 𝑱(4.3a)∇∙𝑩=0(4.3b)∇ × 𝑬 = −𝜕𝑩/𝜕𝑡(4.3c)

where 𝑩 is magnetic flux density, 𝑱 is current density, 𝑬 is electric field, and 𝜇 is
permeability. When conducting numerical analysis based on Equation (4.3a)-(4.3c), finite element
analysis [111,113-116] on electromagnetism is performed using vector potential A defined by ∇ ×
𝑨 = 𝑩.
83

In the analysis, a model in which a coil is placed in the center of the carbon steel plate wasestablished to investigate the detected voltage and the phase characteristics to the plate thickness
and lift-off signal. The analysis model is shown in Figure 4.4, physical property values of thematerials are shown in Table 4.1, specifications of a coil used in this analysis are shown in Table
4.2, and variables used in the analysis are shown in Table 4.3. The eddy current probe used in thisresearch is a mutual induction system consisting of two coils that are excitation coil and detection
coil for both analysis and experiment. In the analysis, an alternating current is input to theexcitation coil, and the voltage obtained through the detection coil is evaluated as a measured
value. The soft iron shown in Table 4.1 is used for the core, and the excitation coil and thedetection coil are coils shown in Table 4.2 respectively. The analysis in this research is not aiming
to reproduce the experiment, but to grasp the tendency of the detection signal for connecting
through to the experiment. Therefore, the coil and the materials are not limited to specific type,
and the conditions shown in the tables are assumed. In the analysis of eddy current testing, themesh size influences the analysis accuracy. For the consideration of the mesh size, using thepenetration depth shown in Equation 4.1 and making it less than half of the penetration depth
confirmed to capture the influence of eddy current loss well [117,118]. The input current is up to
1000 Hz in this analysis, and then the penetration depth δ at 1000Hz is 0.65mm. Figure 4.5 shows
an influence of mesh division on an eddy current loss which will be described. The horizontal
axis shows the width per mesh layer in the steel plate which the eddy current flows, and it isnormalized by the penetration depth δ. The vertical axis shows eddy current loss normalized by
the minimum eddy current loss value We. Since the eddy current distribution can be accurately
reproduced when the mesh of the steel plate is divided finely, it can be confirmed that the eddy
current loss converges to a fixed value. As a result, it is able to consider that the mesh of the steel
plate should be divided smaller than the penetration depth δ. In this study, in order to apply theeddy current testing in the thickness direction, therefore, minimum mesh size is selected to use
1/3δ, which is more accurate than 1/2δ, taking into consideration calculation time. Finally, theminimum element size which is 0.2 mm and maximum element size of 2.0 mm were selected forthe analysis. The mesh division of the analysis model focused near coil and plate is shown in
Figure 4.6 and full mesh model applied aforementioned process is shown in Figure 4.4.
The value of the plate thickness is 9 mm as a healthy condition, from which 1 mm by one
step of thickness reduction is simulated. In the eddy current testing, the permeability change ofthe steel was considered influencing to measurement results, however, since it is assumed that thechange is small in the low frequency area used in this study, the permeability is assumed to beconstant in this analysis. Changes in plate thickness were set assuming thinning due to corrosion,and changes in lift-off were set with the assumption of a presence of rust layer and a coating film
for corrosion prevention. An alternating current 0.1 A was input to the excitation coil. The heating84

value due to the eddy current is also taken into consideration in the model constitution formula.
4.3.2. Fundamental Characteristics of Detected Voltage in Eddy Current Testing
In this study, basic characteristics of the eddy current are grasped with the excitation waveform
as a sinusoidal wave and sweep wave. Using detected voltage as an index. Analysis was carriedout by changing a plate thickness, lift-off, and frequency as parameters.
First, a relationship between the detected voltage and the frequency is shown in Figure 4.7.
Figure 4.7 shows the case that lift-off is 0 mm and plate thickness is 9mm. It was confirmed by
the figures that the detected voltage increases with the frequency increases. Since the magnetic
flux which is penetrated conductors increases toward high frequency side, eddy current also
actively flows accordingly.
Next, a relationship between the detected voltage and the plate thickness at each frequency
is shown in Figure 4.8. The graphs indicate that the detected voltage increases as the plate
thickness increases. In this research, the plate thickness is written as 'thickness' in figures. Figure
4.9 shows an eddy current density distribution obtained by analysis. Figure 4.9(a) shows the eddy
current density distribution at plate thickness of 6 mm and 9 mm. The lift-off is unified at 0 mm
in Figure 4.8 and Figure 4.9(a). Figure 4.9(b) shows the eddy current density distribution atplate thickness of 9 mm with lift-off 0 mm and 0.3 mm. Since thickness at 6 mm and 9 mm show
similar distribution tendencies, eddy current that was supposed to occur in the area corresponding
to reduce the plate thickness disappears. Therefore, the detected voltage is thought to decrease. In
addition, when lift-off occur the effective area of eddy current at plate surfaces becomes slightly
bigger. On the other hand, the graph of Figure 4.8(d) shows peak at thickness 7 mm and thendecreases. Phenomenon, which a peak appears at the point where the detected voltage gradually
increases with increasing the plate thickness and then decreases, are also reported by Yamada etal [119,120]. This phenomenon is considered to be related to an eddy current loss. The eddy
current loss is a phenomenon that eddy current changes power consumed in the conductor into
heat and dissipates energy. It can be expressed by the following equation.
𝑊𝑒 = 𝜋 2 𝐵2 𝑓 2 𝑑2 /6𝜌

(4.4)

Where, 𝑊𝑒 [W/m3] is eddy current loss, 𝜌 is resistivity and 𝑑 [m] is plate thickness.
From the above equation, it is conceivable that the higher the frequency, the thicker the plate
thickness and the larger the eddy current loss becomes. As a result, it is considered that energy
dissipates and then the detected voltage decreases. A ratio of eddy current loss to total energy
consumption in the eddy current testing is showed in Figure 4.10. It is confirmed that the ratio of
eddy current loss increases from around 100 Hz as shown in Figure 4.10, and this eddy current
85

loss may affect thickness detection.
Finally, a relationship between the detected voltage and the lift-off at each frequency isshown in Figure 4.11. It is confirmed that the detected voltage decreases as lift-off increases
regardless of the frequency. Hence, it is considered that the more the coil separates from the target,
the less the amount of magnetic flux penetrating target conductors, so that a strength of eddy
current weakens. As a results, the lift-off estimation has possibility in high frequency area whichis considered to be high in the focused frequency in this research, and thickness estimation isconsidered to be able to perform in low frequency area such as 1 Hz or 10 Hz.
4.3.3. Fundamental Characteristics of Phase in Eddy Current Testing
In this subsection, phase characteristics due to lift-off and plate thickness change were also
grasped same as detected voltage.
Figure 4.12 shows the relationship between the phase and the frequency. It is known thatwhen the sinusoidal AC voltage is applied to the coil, the phase of the current is delayed by 90
degrees with respect to the voltage. According to Figure 4.12, the phase decreases from 90
degrees as the frequency increases. It is assumed that the degree of the phase change varies
depending on the properties of the conductor material and the coil to be applied. However, it isthought that it can be applied to various materials by grasping this phase characteristic for eachmaterial. As the frequency is higher, the eddy current prone to flow, further it influences the phaseof the detection signal. Therefore, it is considered that the phase change is also affected by thefrequency change.
Relationships between phase and lift-off at each frequency are shown in Figure 4.13. Here,the plate thickness is also unified to 9 mm which is a healthy condition. As can be seen from
Figure 4.13, it can be confirmed that the phase increases with the increase of lift-off regardlessof frequency. In the eddy current testing, the resistance of conductor affects to the detected voltage
signal, therefore, the phase change due to the lift-off is considered to be a phenomenon caused by
the change in the degree of magnetic coupling between the coil and the conductor.
Relationships between phase and plate thickness at each frequency are shown in Figure 4.14.
The lift-off is unified at 0 mm which is a healthy condition. It is confirmed by the figures that thephase decreases with increasing plate thickness moreover, it is also confirmed that the differenceis larger as the frequency is lower at 1 Hz or 10 Hz. It is similar tendency with the detected voltage,
therefore, it can be considered the phase lag of the detected voltage changes similar to the its
amplitude. The eddy current penetrates from the conductor surface toward the inside however, its
transmission speed is a finite value determined by some parameters such as frequency. That is,
the eddy current is transmitted at a finite speed thus the phase delays as it enters the interior.
In a case where the detected voltage is used as an index, eddy current loss, which is a86phenomenon that the eddy current changes power in the conductor into heat and energy dissipates,
was confirmed. In the frequency range exceeding 100 Hz, the detected voltage tends to decrease
due to energy dissipation owing to the influence of the eddy current loss. On the other hand, when
the phase is used as an index, the difference in phase due to plate thickness change becomes
smaller as the frequency becomes higher so that it is considered that measurement at 1000 Hz isdifficult. Yet, since there is no change in slope, it is conceivable that the influence of eddy current
loss is less likely to be affected than the detected voltage.
In the eddy current testing, it is known that the effect of electromagnetic induction decreases
in the low frequency, the generation of eddy current weakens, the effect of permeability is strong,
and the noise becomes large. It is considered that the effect of magnetic permeability is affectingthat the difference of the phase is larger at 10 Hz than 1 Hz. In addition, it is known that electric
conductivity of conductors also affects eddy currents. As the conductivity increases, eddy currents
flow easily, thus sufficient eddy current can be flown even if the frequency is set to a relatively
low frequency. Since the flow ability of the eddy current depends on conductors, it is notnecessarily the same trend in all materials. However, it is assumed that the aforementioned
tendency does not change significantly because the permeability and the electric conductivity are
related to penetration depth and strength of the eddy current. Hence, it is considered to be
independent of changes in plate thickness and lift-off. Note that, it is conceivable that thistendency is changed depending on the structure and material of the coil.
Based on the above analysis results and consideration, by using the phase as an index, thepossibility of plate thickness was considered to be able to obtain even at high frequencies in thisresearch such as 100 Hz and 500 Hz which is available of estimating lift off by detected voltage.
This idea suggests a possibility to be able to perform a short time measurement.
Taking the all above analysis results comprehensively into consideration, the effect on liftoff and plate thickness of the detected signal can be expressed as in Figure 4.15. The curve in
Figure 4.15 is the transition of the detected voltage and the angle αbetween the detected voltage
and the input current I, which is criterion, is shown as a phase. R represents a resistance, XL
represents reactance, and VR and VL represent voltages applied to both ends of the resistance andthe reactance, respectively. The model in Figure 4.15 is an example simulating the fact that thedamage progresses in the direction of the arrow and the thickness reduction occurred after theformation of the rust layer.

87

4.4.

Proposal of Corrosion Damage Evaluation Method Focusing on
Difference of Dynamic Magnetic Field Characteristics due toDamage Condition

In this section, based on the detected voltage characteristics and the phase characteristics causedby the differences in plate thickness and lift-off revealed by the numerical simulation in theprevious section, methods which is for simultaneous estimation of the lift-off and the plate
thickness from the obtained detected signal is proposed and investigated. From the analysis resultin the previous section, it was confirmed the lift-off has a possibility to obtain by detected voltage
in high frequency area such as 100 Hz or more. Moreover, it was confirmed that the plate thickness
can be estimated low frequency with the detected voltage. In addition, using phase, the plate
thickness is able to estimate in the high frequency over 100 Hz which was difficult to obtain by
the detected voltage. Therefore, in this research, two corrosion damage evaluation methods were
proposed and investigated their applicability. One method uses only detected voltage for
estimating both the lift-off and the thickness, and the other uses both detected voltage and thephase. In the proposed methods, assuming the thickness reduction due to corrosion represents asplate thickness change and the existence of rust layer or a coating film for anticorrosion is assumed
to be a lift-off change. Therefore, using proposed methods, it becomes possible to judge whether
the damage is thickness reduction or formation of rust layer through measurement and further
estimate degrees of damage. Characteristics of two methods are summarized in Table 4.4.
4.4.1. Investigation of Input Waveforms and Evaluation Indexes used in Proposed Method
In this research, in order to make a simple analysis method, a waveform that can acquire
information such as depth and surface shape by one-time measurement was considered. As shownin Equation 4.1, since a penetration depth is frequency dependent, it is possible to obtain more
information at once by having multiple frequency components. In previous research, pulse waves
are widely used for thickness detection [86-90]. Nonetheless, since sweep waves whose frequency
varies with passage of time [121,122] also contains many frequencies and the spectrum is alreadyknown, thus in this study sweep waves in addition to sine waves are selected. Considering numberof waves at low frequency area, log sweep was adopted for excitation wave. Since the sweep
waves have many frequency components and signal varies with time, it has possibility of detecting
new information which cannot be obtained by sine waves. In order to make effective use of sweep
wave, wavelet transformation which can see signal in time-frequency domain is attempted to use
in this study. In addition, the wavelet transformation is effective in reducing electric noise
therefore, reduction in S / N ratio can be expected in experiments using low frequency area. Even
when using other waveforms in the future, it is considered possible to evaluate by the same88method, and it can be a simple method aimed at this research. In this study, Mexican hat with highversatility is adopted for mother wavelet, and expressed by following equation.

𝑊𝜓 [𝑥(𝑡)] =∞̅̅̅̅̅̅̅̅̅̅̅
𝑡−𝑏∫ 𝑥(𝑡)𝜓(
)𝑑𝑡
𝑎
√𝑎 −∞

1

𝜓(𝑡) = (1 − 𝑡 2 )𝑒 −0.5𝑡2(4.5a)(4.5b)

Where, 𝑥(𝑡) is original wave, 𝑊𝜓 [𝑥(𝑡)] is wavelet coefficient, 𝑎 is scale parameter, 𝑏 is shiftparameter and 𝜓(𝑡) is mother wavelet which is Mexican hat. To analyze obtained detectedvoltage when the detected voltage is the evaluation index, wavelet transformation is applied andwavelet coefficient is used as an index instead of original detected voltage data. Example results
applying wavelet transformation is shown in Figure 4.16. Figure 4.16(a) shows the case of
exciting 1 to 100 Hz of sweep wave to the steel plate with plate thickness of 9 mm and lift-off of
0 mm. Figure 4.16(b) indicates the thickness signal strength using plate thickness 9mm and 8 mm.
It can be confirmed that wavelet transformation can analyze data with time information. In thisresearch, maximum values were used as evaluation index in each frequency. Then, relationships
between wavelet coefficient and plate thickness at each frequency are shows in Figure 4.17. The
tendency shows similar to Figure 4.8 and Figure 4.11 which are not applied wavelet
transformation. Moreover, the value after applying wavelet transformation increases ten times
more than original detected voltage data was confirmed. Thus, it was confirmed that the wavelet
coefficient can be applied.
4.4.2. Corrosion Damage Evaluation Method Focused on Detected Voltage Characteristics
This method which uses only detected voltage as an evaluation index named hereafter CDE
method (VV). From the detected voltage characteristics shown in the previous section, it was
revealed that the lift-off change has a greater influence on the difference of the detected signal
than the plate thickness change. CDE method (VV) is discussed using the influence of thedifferences of detected signals which applied wavelet coefficient.
In order to see the influence on the detected voltage signal due to the plate thickness change
and the lift-off change for each frequency, a sweep wave of 1 to1000 Hz was excited. First, ∆𝑊𝐶𝑡and ∆𝑊𝐶𝐿 shown in the following formulas were obtained as plate thickness signal intensity andlift-off signal intensity focusing on the change in detected voltage accompanying each lift-off andthickness change.
∆𝑊𝐶𝑡 = 𝑊𝐶(𝑑) − 𝑊𝐶(𝑑0 )(4.6a)∆𝑊𝐶𝐿 = 𝑊𝐶(𝑙) − 𝑊𝐶(𝑙0 )(4.6b)

89

Where, 𝑑0 is the reference value of the plate thickness, 𝑙0 is the reference value of the lift-off,and d and l are the measured values, respectively. The plate thickness signal intensity when theplate thickness changes 1 mm and the lift-off signal intensity when the lift-off changes 0.3 mm
are as shown in Figure 4.18(a) and Figure 4.18(b) respectively. In Figure 4.18 conditions that
lift-off of 0 mm and 0.3 mm, and plate thickness of 9 mm and 8 mm was used. In these figures,the most disadvantageous conditions within the range assumed in this research are shown as an
example. However, since the lift-off change influences on the difference of the detection signal
more than the change in the thickness, the thickness reduction of 1 mm considered to have thesmallest difference in thickness signal strength was adopted as one of the disadvantageousconditions. In order to confirm the degree of influence of each difference, the ratio of the plate
thickness signal strength and the lift-off signal strength is taken and the relationship with thefrequency is shown in Figure 4.18(c). The vertical axis in Figure 4.18(c) is a thickness lift-off
signal intensity ratio (hereafter called TL signal ratio). The TL signal ratio shows a tendency todecrease as the frequency increases. Furthermore, it was confirmed that on the high frequency
side most of the signals are caused by the lift-off, whereas on the low frequency side the ratio
including the signal due to the plate thickness increases.
Target frequencies for estimating both the lift-off and the plate thickness is determined using
these features. In this research target frequency is from 1Hz to 1000Hz. Based on the above
characteristics, since the TL signal ratio (Figure 4.18(c)) is sufficiently small which means
influence of plate thickness change is negligibly small and lift-off information is contained in alarge amount, frequency is selected as a lift-off estimation frequency (fL). In the selection of thelift-off estimation frequency, the frequency range which includes the influence of the eddy current
loss needs to be excluded. Frequency range where eddy current loss is dominant shows thenegative value in Figure 4.18(c) and is excluded. Lift-off signal strength is much bigger thanthickness signal strength, therefore, excluding high frequency area influences by eddy current loss,
100 Hz is considered to be a lift-off estimation frequency (fL). Using the sweep waveform isconsidered to be effective for calculating the TL signal ratio over a wide frequency range.
Moreover, it is possible to capture the tendency considered to be different depending on thematerial at once. On the other hand, the frequency with the largest information on the thickness
is 1 Hz. Therefore, a plate thickness estimation frequency (ft) can be 1 Hz. However, in an actual
measurement, the detected voltage in low frequency is small therefore, influence of experimental
noise needs to be consider. The amplitude order of the 1 Hz sinusoidal wave is 10-3, whereas thenoise order was 10-4 and the value TL signal ration at 10Hz is close to 1 Hz. Therefore, in thismethod, the phase that can be more stably detected in the high frequency area is used as the index
for plate thickness estimation. Finally, 10 Hz is selected for plate thickness estimation frequency(ft). As summarize, a lift-off estimation frequency (fL) has selected 100 Hz and a thickness
90

estimation frequency (ft) was selected 10 Hz.
Figure 4.19(a) show a lift-off estimation curve (VV), that is the relationship between wavelet
coefficient and lift-off at 100 Hz determined as the lift-off estimation frequency. It was confirmedthat all lines show similar trend irrespective of plate thickness. From this relationship, it is possible
to approximate the wavelet coefficient-lift-off relation used exponentially. Figure 4.19(b) showsa thickness estimation curve (VV), that is the relationship between wavelet coefficient and plate
thickness at 10 Hz, which is the thickness estimation frequency. Regarding the wavelet
coefficient-plate thickness relation, linear interpolation is used to perform approximation withmultiple straight lines. Since the wavelet coefficient-plate thickness relationship changes not only
by frequency but also by liftoff, the coefficient of the approximation curve is set as a function of
the lift-off. Figure 4.19(b) is a result obtained by interpolating values of 0.2 mm from the resultsof lift-off 0.1, 0.3 mm then superimposing them.
Based on the above results, a flow for estimating lift-off and plate thickness in CDE method(VV) is shown in Figure 4.20. First, a database which is reference data used for measurement isconstructed. Specifically, a sweep wave is excited in a calibration specimen so that data obtainedby changing the frequency, plate thickness and lift-off is acquired. It is preferable that thecalibration specimen is made of the same material as measurement targets. Thereafter, a TL signal
ratio graph is created from the acquired data, and then frequencies fL for estimating lift-off and ft
for estimating thickness are selected. Furthermore, a wavelet transform is performed on thedetected voltage signal. The wavelet coefficient-lift-off relationship, which called hereafter liftoff estimation curve (VV), is created using the detected voltage at frequency fL, and the wavelet
coefficient -plate thickness relationship, which called hereafter thickness estimation curve (VV),is similarly created using the obtained lift-off in addition to detected voltage at frequency ft. The
process up to here becomes a database which uses when applying this method to measurementtargets. Next, a measurement is performed on the actual measurement targets so that wavelet
transformation is also applied to the detection waveform obtained there. The frequency step wave
which connects two waves with lift-off estimation frequency and thickness estimation frequency
was proposed to use in this method. The lift-off is estimated from the lift-off estimation curve(VV) and the value of the obtained detection waveform at the frequency fL. Finally, using the
thickness estimation curve (VV) and the value of the obtained detection waveform, the plate
thickness is able to be estimated. Since tfL in Figure 4.20 represents from the surface to the healthy
part on the back side, it is necessary to subtract the detected lift-off amount from the actual plate
thickness t. Hereafter, corrosion damage evaluation method using detected voltage as the index
named CDE method (VV).

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4.4.3. Corrosion Damage Evaluation Method Focused on Phase Characteristics
In the CDE method (VV), in order to avoid the phenomenon of eddy current loss at frequency at
100 Hz where the eddy current loss becomes big at plate surfaces, thickness estimation frequency
was selected in the low frequency side. In the previous section about analysis, the possibility ofthickness estimation at high frequency area using phase was indicated. Therefore, in this section,
CDE method (VP) which uses the phase for thickness estimation is investigated and proposed.
In the beginning of obtaining database is the same as CDE method (VV) until obtaining lift-off.
In the CDE method (VV), the lift-off estimation has little error and has sufficient accuracy in theexperiment discussed in next section so that the lift-off is estimated also using the detected voltage
in this method. In addition, in the CDE method (VV), wavelet transformation is applied to theobtained detection signal with the main objective of noise reduction in the low frequency area, sothat its coefficient was used as an index. To obtain the database, wavelet transformation is applied
as it is because sweep wave is used and also including low frequency area. Besides, in lift-off
estimation, it is assumed that only frequency area in which noises are difficult to be generated isassumed to be used, thus, to simplify data processing by using the originally detected voltage as
an index. In contrast, in thickness estimation, attempting to use the phase which confirmed theacquisition potential in the previous chapter in the high frequency area. Applying a hybrid system
which uses two indicators, it is considered that multilateral evaluation becomes possible. The liftoff estimation frequency was determined 100 Hz in the CDE method (VV). Therefore, in thismethod, attempting to use same frequency as lift-off estimation frequency to estimate thickness
using phase. In this method, lift-off estimation frequency and thickness estimation frequency aresame thus these two frequencies are collectively used as a corrosion damage estimation frequency(fc).
After selecting the corrosion damage estimation frequency, a sinusoidal wave based on theselected frequency is input to the calibration specimen to obtain the lift-off estimation curve (VP)
using detected voltage as an index and the thickness estimation curve (VP) using phase as anindex are calculated. A lift-off estimation curve (VP), that is the relationship between detectedvoltage and lift-off at 100 Hz, which is the corrosion damage estimation frequency (fc) is shownin Figure 4.21. As shown in Figure 4.21, it was confirmed that the same tendency is exhibited
regardless of thickness even when the detected voltage obtained without applying the wavelet
transformation. Therefore, it was indicated that the estimation can be performed at the corrosion
damage estimated frequency without applying the wavelet transformation. Since it was also
confirmed that this relation can be well approximated by an exponential function, thisapproximate curve is taken as a lift-off estimation curve (VP). On the other hand, Figure 4.22
shows the relationship between the phase and thickness at frequency 100Hz. Figure4.22 showsthat each curve had similar intervals by lift-off and showed the same tendency. In addition, these92thickness estimation curves (VP) are subjected to poly-linear approximation by linear
interpolation. Since this thickness estimation curve (VP) varies not only by frequency but also by
lift-off, the coefficient of the approximation curve is set as a function of the lift-off. Thus, thethickness estimation curve (VP) is calculated using the value obtained by the lift-off estimation.
All the points in the figure are values obtained by analysis. A broken line shown in Figure 4.22(c)is as an example curve obtained by interpolation from the value obtained by poly-linear
approximation when the lift-off is assumed to be 0.2 mm.
Finally, a sinusoidal wave with corrosion damage estimation frequency is input to themeasurement target to estimate the lift-off from the value of the obtained detected voltage and thelift-off estimation curve (VP). Then, the thickness is estimated from the thickness estimation
curve (VP) calculated using the obtained lift-off value. Although it is preferable that the databaseis obtained for each measurement target, it is considered that it can be applied to a structure of asteel type having close characteristics. Moreover, this method that is using the original detectedvoltage without applying the wavelet transform to the lift-off estimation, suggests that the timeon the calculation process can be shortened. It is necessary to subtract the detected lift-off amountfrom the obtained thickness to obtain an actual residual thickness same as CDE method (VV).
Above process shows the corrosion damage evaluation method using phase characteristics
proposed in this research, and Figure 4.23 shows the flow of this method.

4.5.

Experimental Evaluation of Proposed Corrosion Damage
Evaluation Method

In this section, in order to examine the applicability of the two corrosion damage evaluation
methods proposed in the previous section, an experiment was conducted to apply the proposed
method to a steel plate having corrosion damage. A database was obtained using a stepped steel
plate assuming thickness reduction due to corrosion as a calibration specimen and the rustthickness and residual thickness of the actual corroded specimen were estimated. All theexperiments were carried out in this study five times in each place and evaluated using the averagevalue. It was confirmed that the variation in five trials was about ± 0.0003 mm. A transfer function
is used to calculate the phase in the experiment. The relationship between the input and the output
waveform is expressed as follows using the transfer function. In the experiment, the transfer
function is calculated from the power spectral density of the input and output waveform. Then thephase is calculated by using the obtained transfer function.

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𝑉(𝑠) = 𝐺(𝑠) × 𝑈(𝑠)(4.7a)

𝐺(𝑠) = 𝑃𝑦𝑥 (𝑓) × 𝑃𝑥𝑥 (𝑓)(4.7b)

𝜙 = arg(𝐺(𝑠))(4.7c)

Where, 𝐺(𝑠) is the transfer function, 𝑈(𝑠)is the Laplace transform of the input waveform, 𝑉(𝑠)is the Laplace transform of the output waveform, 𝑃𝑦𝑥 (𝑓) is the cross power spectral density and𝑃𝑥𝑥 (𝑓) represents the power spectral density of the input waveform. In the experiment, the phaseis calculated by using Equations (4.7b) and (4.7c).
4.5.1. Measurement System
The measurement system used in this research realizes measurement with a small eddy current
probe. It consists of a measurement probe (magnetic coil), a signal generator that generates aninput wave, a data logger that records detection signals and laptop computer which control thesignal generator and the data logger (Figure 4.24). This system is considered to be a mechanismwith a simple configuration, further high filed applicability from the standpoint of power
consumption.
Table 4.5 shows the specifications of the measurement probe used in this research. As shownin the table, the probe used in this study is compact and can obtain detailed information evencompared with the existing plate thickness measurement system, in addition, it can be used fornarrow members. The coil can connect four cables. Therefore, it can be confirmed there are twocoils such as excitation coil and detection coil same as analysis. The calibration specimen used inthis research shown in Figure 4.25. Assuming a thickness reduction of 1 mm, the calibration
specimen is made into a four-step staircase shape from 9 mm to 6 mm. Here, the thickness of 9mm is regarded as a healthy part. Lift-off in the experiment is measured in units of 0.1 mm byinserting a plastic gauge between the calibration specimen and the coil. Since the signal generator
used in this research is voltage control, the input current is obtained by connecting a shunt resistor(Figure 4.24), which is a resistor for current detection to detect the circuit current, in series withthe circuit and measuring the potential difference across the resistor.
4.5.2. Measurement to Obtain Database
4.5.2.1. Selection of focused frequencies
First, using a calibration specimen to obtain detection signal with changing the lift-off from 0 mm
to 0.3 mm at each plate thickness of 9 mm to 6 mm by inputting sweep wave. Considering theanalysis result, log sweep wave which changes from 10 Hz to 500 Hz is generated for 500 seconds
in order to aim at a database of higher accuracy. The signal generator using in this research isvoltage control therefore, amplitude of all waves using in this experiment was selected 5V. Since94

it is voltage control, input current is not stable at each measurement. Therefore, in this experiment
the output signal obtained was calibrated by setting the input current to the same amplitude. The
following results show the values after applying calibration.
Figure 4.26 shows the plate thickness signal strength (ΔWCt) using plate thickness of 9 mm
and 6mm at the lift-off of 0 mm. The thickness signal strength is not shown negative values in theexperiment yet the tendency is similar to the analytical results. Figure 4.27 shows the lift-off
signal strength (ΔWCL) based on the lift-off of 0 mm and 0.3 mm when the thickness is 9 mm.
Although it showed a peak at 500 Hz in the analysis, it was confirmed that the calibration
specimen showed a peak at 100 Hz and gradually decreased. The result that the peak is shown at
a certain point then decrease is similar to the analysis. The difference between the analysis andthe experiment is considered to be attributable to the assumption that there are some differencesin the core material and the core structure of analysis and experiment. In addition, the material or
physical properties were assumed values, therefore, they are not exactly same as the experiment.
The influence of experimental equipment is also considered.
Figure 4.28 shows the TL signal ratio obtained by the experiment. At low frequency area,
the influence of thickness signal strength is large, and the tendency that the influence of the liftoff signal strength became stronger toward higher frequencies was confirmed. This trend is similar
to the analysis. A rattling of the signal on the high frequency is considered to occur by the
thickness signal being unstable due to the influence of the eddy current loss.
The frequency at 100 Hz which the TL signal ratio is sufficiently small and has the largest
lift-off signal strength is considered to be appropriate as both the lift-off estimation frequency for
the CDE method (VV) and the corrosion damage estimation frequency for the CDE method (VP).
In addition, the frequency where includes the much thickness information is considered to be
10Hz. The frequency at 10 Hz is appropriate to the thickness estimation frequency. From theabove, it can be possible that thickness and lift-off can be estimated by the proposed methods inthe previous chapter. It is conceivable that the lift-off signal strength, plate thickness signal
strength and the TL signal ratio are different depending on the materials so that it is necessary toobtain it in advance as a database. It becomes an advantage that it is possible to confirm the effectof magnetic permeability or eddy current loss since it is possible to see various frequency bands
individually by exciting a sweep wave.
4.5.2.2. Obtaining lift-off and thickness estimation curve in CDE method (VV)Data at the excitation frequency of 100 Hz are extracted using 16 different detection signals
respect to lift-off change and thickness change obtained initially. The TL signal ratio shows thefrequency with the largest information on the thickness is considered to be 10 Hz. The reason
which 1 Hz is not selected even the thickness information is more than 10 Hz is noise effect as95

mentioned in previous section. Therefore, the results of the experiments show the lift-off
estimation frequency (fL) is 100 Hz and thickness estimation frequency (ft) is 10 Hz in thecalibration specimen. Figure 4.29 shows the lift-off estimation curve (VV) which is the wavelet
coefficient-lift-off relationship at 100 Hz and Figure 4.30 shows the plate thickness estimation
curve (VV) which is the wavelet coefficient-thickness relationship at 10 Hz. It can be confirmedthat the same trend as the analysis is shown. In addition, in Figure 4.30, it was confirmed that it
was possible to superimpose values of 0.2 mm interpolated from the results of lift-off 0.1, 0.3 mmby the same method as the analysis. Moreover, in this method, possibility of estimating thickness
thicker plate was considered because the penetration depth in low frequency is larger than highfrequency. Figure 4.31, Figure4.32 show the lift-off estimation curve and thickness estimation
curve using thicker calibration specimen with steps from 16 mm to 19 mm. This result indicatesthat the possibility of thickness estimation of thicker plate using CDE method (VV). Moreover,Figure4.31 indicates lift-off is able to estimate without considering thickness.
4.5.2.3. Obtaining lift-off and thickness estimation curve in CDE method (VP)
To obtain lift-off and thickness estimation curve, sine waves applied to excitation wave. However,to confirm the condition of measurement sites, using sweep same as obtaining the TL signal ratio
was used to obtain calibration reference. The condition of performing measurement is always
different such as AC frequency or condition of ground. Specifically, sweep waves are excited tothe calibration specimen (for example, with a thickness of 9 mm and a lift-off of 0 mm, etc.) as areference, before starting measurement and then, the result is compared with the reference dataobtained by this experiment. By subtracting it as an error due to measurement conditions, errors
in the experimental environment can be eliminated. As an example of obtaining data, Figure 4.33
shows the relationship between the phase and the frequency of thickness 9 mm with lift-off 0 mm
and lift-off 0.3 mm and 8 mm with lift-off 0 mm. The results indicate that the tendency of analysis
is able to capture by experiments, further the phase differences related to lift-off and thickness arewell obtained.
Obtaining the TL signal ratio confirmed that the corrosion damage estimation frequency isappropriate at 100 Hz for the specimen used in this method. Figure 4.34 shows the relationshipbetween detected voltage and lift-off which is the lift-off estimation curve (VP). From the factthat the detection signal becomes smaller as the lift-off becomes larger, which is a feature whenusing the detection voltage as an index, it can be considered that the thickness of rust layer of themeasurement target can be estimated using Figure 4.34. In addition, the proposed CDE method
VP) which is not applied wavelet coefficient shows the similar lift-off estimation curve (VV) as
CDE method (VV) which perform wavelet transformation to detected voltage, thus it isconsidered to be able to shorten the time in data processing and to have high applicability in the96

field measurement.
Figure 4.35 shows the relationship between the phase and the thickness which is the
thickness estimation curve (VP). It showed the same trend as the analysis in which the amount ofchange increases with decreasing plate thickness. Although there was concern that the differencein the amount of phase change in the analysis was small, it was confirmed that it shows a sufficientdifference for estimation. All the points in the figure show the experimental values furthermore,
the broken line at the lift-off of 0.2 mm shows the curve calculated by poly linear approximation
using the values of lift-off 0.1 mm and 0.3 mm. Since it was confirmed that the approximate curveshows good agreement with the lift-off of 0.2 mm in the experiment, it was confirmed that the
thickness estimation curve can be calculated from the lift-off value obtained from the lift-off
estimation curve. As mentioned above, it is able to estimate the residual thickness of measurementtarget using the relationship of Figure 4.35.
It was confirmed the phase at 100 Hz taken out from the sweep wave and the phase obtainedusing sine wave at 100 Hz were equivalent. Therefore, the values obtained by exciting the sine
wave to measurement targets can be used to estimate the plate thickness and the lift-off byapplying the values to estimation curves.
4.5.3. Measurement Experiment using a Corrosion Specimen
From the above results obtained by experiments using the calibration specimen, it shows thepossibility of estimating lift-off and plate thickness with both CDE methods. In this section, an
estimation of the rust thickness and the residual thickness of a steel plate which has corrosion
damage collected from the actual structure (hereafter named corrosion specimen) using the liftoff estimation curves and the thickness estimation curves was performed. Corrosion specimen
using in this experiment shows in Figure 4.36. P1, P2 and P3 indicate the measurement points.
The probe was placed directly at the measurement point to carry out the measurements. The rustlayer was not removed and the back surface shape was measured using a laser displacement meter.
Moreover, it was evaluated as thickness reduction due to corrosion. The rust layer as the lift-off
in this experiment was measured using a film thickness meter. Figure 4.37 shows the back surface
shape of the corrosion specimen measured with a laser displacement meter. The rust thickness
and the residual thickness of the corrosion specimen were determined from the measurement
results of the laser displacement meter and the film thickness meter, thus the obtained values wereused for the evaluation. Note that the measurement with the laser displacement meter is a value
including the rust layer, so that the residual thickness is evaluated by subtracting the rust layer.
Since the residual thickness in the proposed method assumes the average value of the probe area
which is the influence range of the eddy current as the measurement value, a value averaged by
the area of the coil around the measurement point is calculated as the residual thickness of the97corrosion specimen. Excitation wave in the CDE method (VV) uses two wave which are lift-off
estimation frequency and thickness estimation frequency. Considering each wave number, a 100
Hz wave for 5 seconds and a 10 Hz wave for 30 seconds was decided to generate. For the CDE
method (VP) uses the data obtained by the lift-off estimation frequency. Although it is possible toshorten the measurement time, it was set to include many wavenumbers to improve accuracy inthis experiment.
Figure 4.38(a) shows the lift-off estimation result and Figure 4.38(b) shows the plate
thickness estimation result. Both the lift-off estimation and the plate thickness estimation isevaluated using wavelet coefficient of detected voltage in the CDE method (VV). The lift-off
estimation is used the original detected voltage without applying the wavelet transform thenthickness estimation is performed using the phase in the CDE method (VP). The values of Realin the figure was obtained by the film thickness meter and the laser displacement meter. As shownin Figure 4.38(a), the rust thickness of Real and the rust thickness calculated as the lift-off by the
CDE methods were all evaluated to be less than 0.1 mm, so that good agreement results wereobtained. The errors of both CDE methods are almost same. Therefore, it can be considered thatsince frequency at 100 Hz includes not big noise, process of applying wavelet transformation canbe excluded in the lift-off estimation process of CDE method (VP). The measurement values
obtained by the both CDE methods are calculated using a database therefore, the values are set toconform to the measurement accuracy of the film thickness meter.
On the other hand, in thickness estimation in the voltage method, it was confirmed that the
maximum error was around 8%. In this research, the errors less than 10% is considered to be
accepted. Therefore, CDE method (VV) was confirmed to have enough accuracy. As for the
thickness estimation in CDE method (VP), it was also confirmed that error of each measurement
point can be calculated with an accuracy of less than 5% as shown in Figure 4.38(b). Therefore,
it is able to confirm that the errors are reduced by the CDE method (VP).
It was also confirmed that the influence of measurement error did not significantly affect theestimation results. As a result, it has been indicated that the thickness of rust layer and the residual
thickness of the corroded steel member can be experimentally well evaluated by both CDE
method proposed in this research. In the CDE method (VV), it was necessary to use two different
frequencies. However, it was confirmed that by using proposed CDE method (VP), it is possible
to obtain two corrosion damage information such as rust thickness and residual plate thickness
only with one frequency. In fact, the maximum error is about 5% and the measurement time perpoint is around 5 seconds thus, the measurement realized labor saving by improving the accuracy
and shortening the measurement time. On the other hand, using low frequency to estimate
thickness in the CDE method (VV), it has possibility of applying thicker plate (Figure 4.31,
Figure4.32). Moreover, since there are some differences in factors such as eddy current loss or98

electrical conductivity which influence the detected voltage and phase respectively, and degree of
influence, it is conceivable that each CDE method can be selected according to the characteristics
of measurement targets. On the other hand, the error characteristics at each measurement point
are consistent in both methods hence the influence of the measurement error in the laser
displacement meter or the film thickness meter realized as Real in Figure 4.38 is considered.
Furthermore, in the eddy current testing, the eddy current decreases as it proceeds to the inside of
the plate, so that there is a possibility that the eddy current does not coincide with the probe area.
Although it is reported that the influence of the rust layer is small in the measurement with anelectromagnetic type thickness meters [123], there is a possibility that rust having electric
conductivity may be contained in the corrosion product. Thus it is necessary to investigate for the
future works. Additionally, the lift-off estimation using the amount of phase change is considered
to be possible from the analysis result, even though it was not considered in this research,
therefore, it will also be investigated in the future works.

4.6.

Application of Proposed Corrosion Damage Evaluation Method to
Robotic Inspection

According to the above experiments, the corrosion damage evaluation method was established as
one inspection method. In this section, implementing this CDE method to the robotic inspection
function was investigated. The problem of implementing this method directly to the inspection
robot is the size of the probe. The needs of the inspection robot are to perform inspections on thepart that is narrow or difficult to access, so the inspection robot is made relatively small. In orderto keep this concept and to reduce a burden on the robot, a smaller probe than probe used in theprevious section was decided to introduce. The specifications of the small probe are shown in
Table 4.6. The probe used up to the previous section is referred to as a φ23.7 probe, and the probe
used for installing to the inspection robot is named a φ10 probe. It is assumed that detection
system is same as the φ23.7 probe which is the mutual induction system consisting of theexcitation coil and the detection coil. In this section, database acquisition experiments using small
coil were conducted to confirm the applicability of φ10 probe and the versatility of CDE method.
All conditions except the probe type. CDE method (VP) has ability to measure each point fasterthan CDE method (VV). Therefore, in this study, a verification experiment on the CDE method(VP), which is a more convenient method considering the installation to the inspection robot was
conducted.

99

4.6.1. Measurement to Obtain DatabaseIn the same way as acquiring the database with the φ23.7 probe, first obtain the TL signal ratio toselect corrosion damage estimation frequency. Sweep waves are input to the calibration specimen
to obtain the detection signal which is acquired by changing the thickness and lift off, respectively.
Figure 4.39 shows the lift-off signal strength (ΔWCL) using the lift-off of 0 mm and 0.3 mm when
the thickness is 9 mm. Although it does not show a clear peak as the φ23.7 probe, the tendency
that it rises to 300 Hz and then shows a downward trend was confirmed. For this reason, it isassumed that the lift-off of the detection signal and the strength with respect to the plate thickness
change depending on the probe specification such as the shape or the material of the core. Figure
4.40 shows the TL signal ratio of φ10 probe. At the low frequency, the influence of the plate
thickness signal was large, and as the frequency became higher, the tendency was predominantly
governed by the influence of the lift-off. The corrosion damage estimation frequency in the φ10probe is 300 Hz since the lift-off signal strength is sufficiently large and the influence of the plate
thickness disappears. In addition, the frequency area where the TL signal ratio shows negativevalue is excluded. Since the penetration depth at 300 Hz becomes smaller than 100 Hz, it may
possible to be difficult to detect the plate thickness. However, the penetration depth is a reference
value therefore, 300 Hz is selected for φ10 probe and experiments to obtain plate thickness
estimation curve and lift-off estimation curve are attempted to obtain.
Figure 4.41 shows the relationship between detected voltage and lift-off which is the lift-off
estimation curve (VP_φ10). The wavelet transformation was not performed to detected voltage.
The value of the detected voltage in the φ23.7 probe was 1.0 V with the lift-off of 0 mm however,it was 0.06 V in the φ10 probe. Although the detected voltage was small even at 300 Hz, it was
confirmed that the graph shows the same trend as lift-off estimation curve obtained using φ23.7
probe. This result indicates the possibility of lift-off estimation using obtained curve shown in
Figure 4.41.
Figure 4.42 shows the relationship between the phase and the frequency. The area of low
frequency such as under 100Hz is excluded due to noise and too small detected voltage to use for
evaluation. The graph indicates the same tendency which shows the phase becomes small as thefrequency increases, as φ23.7 probe (Figure 4.33). Figure 4.43 shows the relationship between
the phase and the plate thickness which is the plate thickness estimation curve (VP_φ10). The
phase becomes large as the thickness becomes small. This is the same tendency as φ23.7 probe.
However, the phase becomes small as the lift-off becomes large. This is the opposite tendency tothe φ23.7 probe. The possible reason of this difference is coil specification such as size, numberof turns or position of both detection coil and inducing coil. To confirm the size effect of coils, afurther smaller coil which the diameter of the probe is 7 mm is used. Results indicate the sametendency as the small coil. Therefore, aforementioned reason can be considered. On the other100

hand, regardless of this difference, it can be confirmed the clear difference with both the lift-off
and the thickness. Hence, it is able to consider the possibility of estimating thickness with obtained
thickness estimation curve.
All the points in the figure show the experimental values furthermore, the broken line at thelift-off of 0.2 mm shows the curve calculated by poly linear approximation using the values oflift-off 0.1 mm and 0.3 mm. Since it was confirmed that the approximate curve shows good
agreement with the lift-off of 0.2 mm in the experiment, it was confirmed that the thickness
estimation curve can be calculated from the lift-off value obtained from the lift-off estimation
curve. As mentioned above, it is able to estimate the residual thickness of measurement target
using the relationship of Figure 4.43.
4.6.2. Measurement Experiment using a Corrosion Specimen
In this section, using the above estimation curves of lift-off and thickness obtained by experiments
using the calibration specimen, the applicability of the φ10 probe to corrosion specimen was
confirmed. The corrosion specimen and other conditions except the probe are same as the previous
experiments. In the φ10 probe experiments, the corrosion damage evaluation frequency of 300
Hz was used therefore, the measurement time per each point can be reduced to 1 second.
Figure 4.44(a) shows the lift-off estimation result and Figure 4.44(b) shows the plate thickness
estimation result. Since the size of the probe became small, the effective area of eddy current
became also small. Therefore, the values of Real are slightly changed and the φ10 probe isconsidered to be able to obtain more local information than the φ23.7 probe.
As shown in Figure 4.44(a), the thickness of rust layer in Real and the rust thickness
calculated as the lift-off by the CDE method were all evaluated to be less than 0.1 mm, so that it
can be considered the CDE method (VP) with the φ10 probe (named hereafter CDE method(VP_φ10)) has enough accuracy.
On the other hand, in plate thickness estimation using the φ10 probe, it was confirmed thatthe maximum error was around 7%. In this research, the errors less than 10% is considered to be
accepted. Therefore, the φ10 probe was confirmed to have enough accuracy even at frequency
300 Hz. This results indicate the measurement time per point can significantly reduce. Moreover,the inspection robot has interval time in each movement. Therefore, this measurement time
reduction can be advantage to install the φ10 probe to the inspection robot since it shows thepossibility of inspection while robot is moving.
4.6.3. Investigation of Installing Method of Small Eddy Current Probe on the Proposed
Inspection Robot
In the previous section, applicability of CDE method (VP_φ10) was confirmed. In this section,101

concretely the method of installing this φ10 probe to the inspection robot was examined and theapplicability in the condition where the φ10 probe was installed on the inspection robot wasverified. Since various influencing factors such as vibration can be considered when installing the
φ10 probe to the inspection robot, efficient installing method and applicability were verified using
the corrosion test specimen. Furthermore, the movement performance of the inspection robot in
the condition where the φ10 probe was installed, was confirmed on a corrosion surface and aconcrete surface.
An attachment condition of the probe to measurement surfaces is important in case of
installing the φ10 probe to the inspection robot to estimate rust thickness and thickness with good
accuracy. In addition, it is necessary to eliminate the physical influence such as vibration or to
subtract influences by comparison with the reference value obtained with calibration specimen.
The experiments discussed in previous sections, the probes were placed directly on the specimen,
and accuracy was secured without touching the probe during measurement. As mentioned earlier,as lift-off is greatly influenced and it is heavily involved in thickness estimation accuracy,
therefore, constantly management of lift-off is the most important. At the time of air pad is
attached or not attached to surfaces, the body of the inspection robot moves about 8 to 10 mm up
and down.
The inspection robot advances while repeating attachment and non-attachment by the air
pads. In this mechanism, during movements, it is considered difficult to measure while constantly
controlling the lift-off, therefore, it is expected that the measurement is carried out when the robot
is fully attached to surfaces in the end of each movement. Assuming that the measurement isperformed in practice, data can be acquired at intervals of about 20 mm. In consideration of
aforementioned conditions, the mechanism by which the probe attaches to target surfaces wasinvestigated with the height of the robot fully attached as a reference.
In order to make fully attachment of the probe, a sponge air pad using a chloroprene sponge
used for a sponge bellows air pad was applied. Diameter of this sponge air pad is 15 mm. A probe
holder for attaching the φ10 probe to the inspection robot was made using a 3D printer andattached it to the back side of the inspection robot. The probe holder has four sponge air pads
which is for actualize fully attachment. The probe holder and the condition of installing it to theinspection robot are shown in Figure 4.45. As shown in the figure, when the inspection robot is
not attached to surfaces, the probe holder is floating from the surface about 10 mm. It is considered
to be able to remove the influence of lift-off at the maximum by attracting the sponge air pad tomake a state of firmly attaching to target surfaces when the inspection robot is fully attached tosurfaces.

102

4.6.4. Confirmation of the Robotic Movement while Installing Small Eddy Current Probe
As shown in Figure 4.45, the probe is connected to the inspection robot with special probe holder.
The probe holder was designed aiming not to be heavy as to affect the movement of the inspection
robot. However, during the movement of the inspection robot, it was necessary to pull up theprobe holder with only the force of the piston of the air cylinder. The probe holder is floatingwhen the inspection robot is moving, hence, it was conceivable to influence the smooth movementof the inspection robot. Therefore, the movement performance of the inspection robot was
confirmed at a corroded metal surface and a concrete wall surface.
Figure 4.46 shows the movement performance on the corroded metal surface and the mortar
wall surface. It was confirmed that the robot moves forward and retreat more stable than before
installing the φ10 probe. Since the left and right legs of the upper part in the inspection robot are
fixed at a constant distance by the probe holder, even if excessive force is applied to the robot the
air pad can always move in parallel with target surfaces. Problems were not confirmed even in
the probe pulling up operation which was concerned to influence robotic movement. However,during the rotation motion, the robot rotates by expansion and contraction of the piston one by
one it kept the air pads parallel to surfaces using aforementioned the bending force of the robot
body. It was confirmed that the rotation angle was reduced and the re-attachment after the rotation
became unstable due to the constraint of the movement of the upper part of the insertion robot
body. This problem was able to solve by starting the movement from the lower part on which aprobe holder is not installed at the time of the robot rotation movement. Yet this solution increases
wasteful movement. Therefore, another effective installation method of the probe will be
investigated in future works.
4.6.5. Application on a Corrosion Specimen using Proposed Robot
The influence of the air pump, the air compressor, a vibration due to the inspection robot on
measurement data obtained by the φ10 probe which is installed on the inspection robot needs tobe confirmed and eliminate the influences if necessary. Specifically, the results measured using
the calibration specimen by the probe installed the inspection robot, named hereafter robot probe,
are compared with the data acquired by the φ10 probe in the previous section. Data using therobot probe is acquired by changing the lift-off or thickness to check whether the two data havedifferences. Since the robot probe is brought into close contact with surfaces by the sponge air
pad, it is not assumed that lift-off as a gap occurs. Moreover, since it is also necessary to adjust
the installation height of the probe holder, before starting the measurement on target surfaces, it
is important to do aforementioned process as a calibration.
The calibration method and the movement performance when the φ10 probe was installed tothe inspection robot was examined. In this section, finally the applicability of CDE method103

(VP_robot) which is the CDE method (VP) using the robot, was investigated using the corrosion
specimen. It was confirmed that the vibration of the robot and the air compressor or the air pump
had no influence on the lift-off or the thickness estimation. Since the corrosion specimen is small
and it is difficult to attach the robot on the same surface, measurement was carried out by placing
the corrosion specimen under the probe holder connected to the inspection robot and attaching
the sponge air pads of the probe holder to the corrosion specimen. Figure 4.47 shows the resultsof estimating the lift-off and the thickness of corrosion specimen using CDE method (VP_robot)
as well as CDE method (VP_φ10). The conditions except installing the φ10 probe to theinspection robot are same as the measurement with the φ10 probe in the previous section.
Therefore, for the purpose of comparing the measurement accuracy, the measurement result withthe φ10 probe alone is also shown in Figure 4.47. In the CDE method (VP_robot), the values
which were after applying calibration process was used as the evaluation value. It was confirmedthat the evaluation results of each CDE method and the values of Real show good agreement
respectively. Since the measurement result shows good agreement, it is considered that noproblems with the installing method of the φ10 probe on the inspection robot were confirmed.
Therefore, it is able to considered that the CDE method performed by the inspection robot hasenough evaluating possibility.

4.7.

Summary

In this research, CDE method using low-frequency eddy current that can acquire corrosion
damage information such as thickness of rust layer and residual plate thickness more easily
without accompanying surface preparation such as removal of corrosion products and coating
films are more compact and more convenient was proposed. In order to propose a new method toevaluate the corrosion damage condition by inspection from either the front or back surface of thesteel member, a study based on numerical simulation and experiment was conducted. Moreover,the small eddy current probe was installed to the robot to perform the corrosion damage evaluation
method as the robotic inspection function. The corrosion damage needs to inspect prone to appear
the sections which is narrow or difficult to access by inspectors. Therefore, it can be consideredthat combination of eddy current without needs of surface preparation and robotic inspection
which can approach aforementioned sections are one good option to apply to the CDE method.
The conclusions in this research are described below.


Numerical simulation confirmed the tendency that the detected voltage becomes larger as theplate thickness becomes larger and the detected voltage becomes smaller as the lift off is104

larger. The change related to the lift-off is relatively large than change with the thickness. At
the high frequency area, a tendency was confirmed that the detected voltage shows a peak at
a certain thickness and decreases thereafter despite thickness becomes large. The possibility
that the penetration depth is reduced and the electromotive force is lowered due to the eddy
current loss have been shown as one of the causes.
Numerical simulation focusing on the phase confirmed the tendency that the phase decreases
as the plate thickness decreases and as the lift-off increases. On the other hand, the influenceof eddy current loss to the phase confirmed smaller than detected voltage. Therefore, phase
was selected as plate thickness estimation index.


Based on the characteristics obtained in analysis, two CDE methods were proposed. One is
CDE method (VV) that uses only detected voltage as an evaluation index and the other is
CDE method (VP) that is combines the phase characteristics revealed by numerical simulationand the detected voltage characteristics was proposed. CDE method (VV) uses two different
frequencies to obtain thickness of rust layer and residual plate thickness. For estimating rustthickness, high frequency area in this research was used and for residual plate thickness
estimation, low frequency was used.
In the proposed CDE method (VP), the lift-off is estimated by using the detected voltage andthe phase is used for the thickness estimation. Therefore, the plate thickness can be estimatedby using same sinusoidal wave for estimating lift-off.
In addition, the influence of lift-off and plate thickness are different so that it is possible todetermine whether the damage is the formation of a thinned or rust layer or not and to obtain
both information simultaneously.



By experiments using the calibration specimen for database acquisition, the detected voltage
and phase characteristics clarified by the analysis were reproduced, then the lift-off estimation
curve and the thickness estimation curve for both methods necessary for actual measurement
were obtained. Especially in CDE method (VP) the possibility of estimating the lift-off
directly using the original detection voltage without applying the wavelet transform wasconfirmed.

Using the steel plate with corrosion damage collected from the actual structure, thickness ofrust layer and residual plate thickness were estimated by using both CDE methods. The results
indicated that these estimations are actually possible.



The applicability of CDE method (VP) with the φ10 probe was also confirmed by preparing
database and estimating rust thickness and residual thickness of the corrosion specimen. The
results indicate the possibility of applying CDE method (VP) to the φ10 probe.
Moreover, the applicability of CDE method (VP) using φ10 probe as the robotic inspection
was successfully performed. The special probe holder which is for installing the φ10 probe to105the inspection robot was made using 3D printer. Performance of the robotic movement after
installing the φ10 probe was confirmed at surfaces of corroded metal and concrete.
The applicability of the CDE method (VP_robot) was also confirmed using the corrosion
specimen. The results were similar to the CDE method (VP_φ10). It can be considered the
CDE method (VP_robot) is able to be one of the robotic inspection function.
All results indicate that the possibility of obtaining the thickness of rust layer and the residual
plate thickness with a relatively φ10 probe is demonstrated. Moreover, it became possible toobtain more detailed information than the conventional plate thickness measurement system.
The CDE method (VV) has advantage that can apply to the thicker plate. The CDE method (VP)
has advantage that can perform measurement in very short time. Since there are some differencesin factors such as eddy current loss or electrical conductivity which influence the detected voltage
and phase respectively, and degree of influence, it is expected that each CDE method can beselected according to the characteristics of measurement targets. The CDE method is also able toperform using the inspection robot. Therefore, it can perform at the places where the inspectors
cannot reach before. It can be considered that all CDE methods have a possibility to be a versatileinspection method.

106

Figure 4.1

Corrosion damage pattern and an inspection method

Figure 4.2(a)Figure 4.3

Eddy current mechanism(b)Conceptual image of eddy current testing, (a) Conceptual image, (b) Circuit
diagram107

Figure 4.4

Table 4.1

Analysis model

Physical property values of material using in analysis

Material

Relative permeability

Electro conductivity [S/m]Carbon steel

160

6.0×106Air

1

0

Soft iron (without losses)

2000

1

Table 4.2

Specification of coil

Number of turns

400

Diameter [mm]16.4

Height [mm]3.7

Table 4.3

Variables used in analysis

Variables

Values using in analysis

Plate thickness [mm]6, 7, 8, 9

Lift-off [mm]0, 0.1, 0.2, 0.3

Excitation frequency [Hz]1, 10, 100, 1000, Sweep wave 1 to 1000

108

Figure 4.5

Figure 4.6

Influence of mesh division on eddy current loss

Detailed mesh division of analysis model

109

Figure 4.7

Relationship between detected voltage and frequency

(a)

(b)

(c)

(d)

Figure 4.8

Relationship between detected voltage and plate thickness, (a) 1Hz, (b) 10Hz, (c)100Hz, (d) 1000Hz
110

(a)

(b)
Figure 4.9

Eddy current density distributions, (a) Plate thickness of 6 mm and 9 mm with 0
mm lift-off, (b) Lift-off of 0 mm and 0.3 mm for 9 mm plate thickness111

Figure 4.10

Ratio of eddy current loss to total energy consumption

-

(a)

-

(b)

-

(c)
Figure 4.11-

(d)Relationship between detected voltage and lift-off, (a) 1Hz, (b) 10Hz, (c) 100Hz,(d) 1000Hz

112

Figure 4.12

Relationship between phase and frequency

(a)

(b)

(c)

(d)

Figure 4.13

Relationship between phase and lift-off, (a) 1Hz, (b) 10Hz, (c) 100Hz, (d)1000Hz

113

(a)

(b)

(c)

(d)

Figure 4.14

Relationship between phase and plate thickness, (a) 1Hz, (b) 10Hz, (c) 100Hz,(d) 1000Hz

Figure 4.15

Conceptual image of the effect on lift-off and thickness by the detected signal

114

Table 4.4

Features of proposed CDE methods

Lift-offThickness

FeatureCDE method (VV)

Voltage

Voltage

Deeper thickness

CDE method (VP)

VoltagePhase

Speedy measurement

(a)

(b)
Figure 4.16

Example results applying wavelet transformation, (a) Plate thickness 9mm andlift-off 0 mm, (b) Plate thickness signal strength
115

(a)

(b)

(c)

(d)

Figure 4.17

Relationship between wavelet coefficient and plate thickness, (a) 1Hz, (b) 10Hz,(c) 100Hz, (d) 1000Hz

1160.00100.0005

ΔWCt

0

200

400

600

800

1000

800

1000

-0.0005-0.0010Frequency (Hz)

(a)
0.35

0.30

ΔWCL

0.25

0.20
0.15
0.10

0.05
0

0

200

400
600
Frequency (Hz)

(b)
0.15

ΔWCt/ΔWCL

0.10

0.1136
0.09430.05

0.0106-0.05-0.0001

0.0001

0

0

200

400
600
Frequency (Hz)-0.0062

800

1000

(c)
Figure 4.18

Signal strength, (a) Plate thickness signal strength, (b) Lift-off signal strength,(c) TL signal ratio
117Thickness 6mm
Thickness 7mm
Thickness 8mm
Thickness 9mm

Lift-off 0mm
Lift-off 0.1mm
Lift-off 0.2mm

Lift-off 0.3mm

Exponential approximation(a)
Figure 4.19(b)Corrosion damage estimation curve, (a) Lift-off estimation curve (VV),Thickness estimation curve (VV)Figure 4.20

Flow of estimating lift-off and plate thickness (VV)

118

(b)

Detected voltage (V)Thickness 6mm
Thickness 7mm
Thickness 8mm
Thickness 9mm

Exponential approximation

Figure 4.21

Lift-off estimation curve (VP)

(a)

(b)

(c)

(d)

Figure 4.22

Plate thickness estimation curve (VP), (a) Lift-off 0 mm, (b) Lift-off 0.1 mm,(c) Lift-off 0.2 mm, (d) Lift-off 0.3 mm

119Figure 4.23

Flow of estimating lift-off and plate thickness (VP)

Figure 4.24

Measurement system

120

Table 4.5

Specification of measurement probe

Probe diameter [mm]23.7

Probe height [mm]58.3

Probe weight [g]50.7

Figure 4.25

Figure 4.26

Calibration specimen

Thickness signal strength121Figure 4.27

Lift-off signal strength

Figure 4.28

TL signal ratio

122

Experimental result

y = 2.23e-1.75x
R2 = 0.996

Exponential approximation

Figure 4.29

Lift-off estimation curve (VV)L = 0.0 mm
L = 0.1 mm
L = 0.2 mm(interpolated)
L = 0.3 mm

Figure 4.30

Plate thickness estimation curve (VV)123

Experimental result

y = 2.23e-1.75x
R2 = 0.995

Exponential approximation

Figure 4.31

Lift-off estimation curve (VV) using thicker steel plate with thickness of 16 mm
to 19 mm

L = 0.0 mm
L = 0.1 mm
L = 0.2 mm(interpolated)
L = 0.3 mm

Figure 4.32

Plate thickness estimation curve (VV) using thicker steel plates with thickness of
16 mm to 19 mm

124

90

Phase (degree)

85

80

75

Thickness 9mm, Lift-off 0mm
Thickness 8mm, Lift-off 0mm
Thickness 9mm, Lift-off 0.3mm

70
101

102

Frequency (Hz)Figure 4.33

Relationship between phase and frequency as experimental example

Experimental result

y = 0.96e-1.77x
R2 = 0.997
Exponential approximation

Figure 4.34

Lift-off estimation curve (VP)

125

Lift-off 0.3mm
Lift-off 0.2mm

Lift-off 0.1mm

Lift-off 0mm
Calculated

Figure 4.35

Plate thickness estimation curve (VP)

Figure 4.36

Figure 4.37

Corrosion specimen

Back surface shape of corrosion specimen
126

(a)

(b)
Figure 4.38

Corrosion damage estimation results, (a) Estimation result of rust layer thickness,(b) Estimation result of residual plate thickness

Table4.6

Specification of small probe

Probe diameter [mm]10.0Probe height [mm]46.5Probe weight [g]

9.4

127

ΔWCL

Lift-off signal strength (φ10 probe)

ΔWCt/ ΔWCL

Figure 4.39

Figure 4.40

TL signal ratio (φ10 probe)

Experimental result

y = 0.061e-1.87x
R2 = 0.998
Exponential approximation

Figure 4.41

Lift-off estimation curve (VP_φ10)
128

Phase (degree)

87

84

81

Thickness 9mm, Lift-off 0mm
Thickness 8mm, Lift-off 0mm
Thickness 9mm, Lift-off 0.1mm

100

200

300

400

Frequency (Hz)Figure 4.42

Relationship between phase and frequency

Lift-off 0mm
Lift-off 0.1mm
Lift-off 0.2mm

Lift-off 0.3mm
Calculated

Figure 4.43

Plate thickness estimation curve (VP_φ10)

129

500

(a)

(b)
Figure 4.44

Corrosion damage estimation results (VP_φ10), (a) Estimation result of rust layer
thickness, (b) Estimation result of residual thickness

130

Figure 4.45

(a)Figure 4.46

Appearance of probe holder and inspection robot with a φ10 probe(b)
Movement performance on a corroded metal surface and concrete wall surface,(a) Corroded metal surface, (b) Mortar wall surface

131

(a)

(b)
Figure 4.47

Corrosion damage estimation results (VP_robot), (a) Estimation result of rustlayer thickness, (b) Estimation result of residual thickness

132

133

Chapter 5
Conclusions and Recommendations

134

5.1.

Conclusions

The results obtained in this research are related to the objectives of this research. Conclusions of
this study are described as follows:

Development of a mobile robotic inspection device which can move on various surfaces
and access locations difficult for inspectors.

An inspection robot which can move across various surfaces to carry inspection devices for
robotic inspection has been developed. The developed inspection robot uses air pads to attach tovarious types of surfaces includes differences in materials, roughness, and angles and air cylinders
to move forward, retreat and rotate. Moreover, to evaluate the suction condition which canevaluate attachment condition of the inspection robot, a gap coefficient, which is the averageheight between the air pads and surface has been defined. Using this gap coefficient, a quantitativeevaluation method for the suction state has been proposed. The most important factor to use air
pad is attachment condition, therefore, the suction state helps inspectors to estimate how well theinspection robot will attach to target bridges in advance of carrying out inspection. Further, toimprove attachment condition, sponge bellows air pad and silicone sponge bellows air pad were
proposed. Finally, performance of robotic movement was examined on target surfaces, at anexisting bridge and at the trial bridge.


Investigation of robotic inspection functions for observation and various measurements.

For steel bridges, which consist of steel or concrete sections, it is necessary to inspect damaged
parts such as concrete decks with cracks or steel decks or truss components with corrosion. In thisstudy, inspection functions as observation and measurement in concept of robotic inspection is
implemented to the inspection robot. In the robotic inspection, robot is assumed to work at blindspot in addition, when inspectors find cracks or corrosion damage the location information of therobot is important. Therefore, the following inspection function are successfully implemented tothe inspection robot.
(a) Visual inspection function using attached camera(b) Location identification function using obtained images by visual inspection camera(c) Crack detection function for decks(d) Function to determine structural behavior using attached acceleration sensor(e) Evaluation of hammering test results (optional function)
The influence of the inspection robot vibration to obtaining bridge behavior was confirmedby laboratory experiment and it can be distinguished from other signals such as noise. Therefore,135

it was confirmed to be able to perform vibration measurement of bridges. The current installed
equipment is two cameras and acceleration sensor. It can be considered that to perform anotherinspection using small sensors, it is able to replace or add sensors considering the needs of target
bridges. As a result, it is conceivable that versatile inspection system has been proposed.



Proposal of an efficient corrosion damage evaluation method.
An independent inspection method (General inspection)
One of the robotic inspection functions (Robotic inspection)

To acquire corrosion damage information such as a thickness of rust layer and a residual plate
thickness, corrosion damage evaluation method based on eddy current was investigated andproposed. Focusing on the characteristics of detected signal, two evaluation methods were
proposed. One is using only detected voltage for estimating the thickness of rust layer and theresidual plate thickness by exciting two different frequency sine waves (CDE method (VV)). Thismethod is very suitable even for thicker plates. The other method is using both detected voltage
and phase between input current and detected voltage (CDE method (VP)). It needs only one
wave to excite. Therefore, this method has an advantage for short time measurement.
Applicability of these methods were confirmed using corrosion specimen which was collectedfrom actual bridge. Moreover, installing the φ10 probe to the inspection robot, CDE method (VP)
can be one of the robotic inspection function. Applicability after installing the φ10 probe to theinspection robot was also confirmed using corrosion specimen.
All methods have enough accuracy for the measurement in the regular or detailed inspection
process. As a robotic inspection function, it can perform at the places where the inspectors cannot
reach before. Eddy current can acquire rust layer or coating film therefore, surface preparation isneedless. Therefore, the concept of the robotic inspection which access target sections by theinspection robot and perform simple and effective inspection can be achieved. It can be consideredthat all CDE methods have a possibility to be a versatile inspection method.
Finally, a method to perform simple and effective inspection has been successfully achieved
using the proposed robotic inspection and corrosion damage evolution method. A robotic
inspection system using proposed inspection robot and corrosion damage evolution method forsteel bridges have been proposed and applicability for the actual bridge inspection has been
examined. The proposed inspection robot can be applied various types of surfaces and surface
conditions. Moreover, proposed corrosion damage evaluation method has become an independent
inspection method and one of the robotic inspection function. However, it is difficult to cross overhigh steps for the inspection robot because there is no mechanism to move the robot legs up and136

down or change the angle. The applicability of robotic inspection could be further improved if thesections where the inspection robot is able to access can be extended. In addition, the applicabilityof corrosion damage evaluation method could be further improved if the coil shape can be
optimized because for example shape of the coil can exclude the influence of lift-off noise.

5.2.

Recommendations

From the conclusions of this study, the followings are proposed as versatility enhancements of
the robotic inspection and corrosion damage evaluation method.
1.

If the inspection robot can cross over high steps such as parts of welded joints or concrete
joints, the performance area of the inspection robot can be extended. To achieve this withcondition of using vacuumed state and air pad, the leg parts of the inspection robot need to
be flexible and controlled by electrical devices such as motor.

2.

If the inspection robot has more inspection devices to obtain different information such as
inclination of surfaces, temperature and humidity of inspection area, or temperature ofsurfaces, the inspection robot can apply to more versatile inspection. Installing other devices
such as a hammer for hammering test, a tilt sensor, a gyro sensor, a thermography camera or
replacing devices from current devices, it is considered to actualize aforementioned
inspection.

3.

Considering performing corrosion damage evaluation using the inspection robot,
management of controlling lift-off is difficult. When the method performed by inspector’shand, the lift-off is also influenced to the measurement results. If the lift-off noise can be
reduced by optimizing the coil, accuracy and applicability of the method is able to increase.

137

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