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Provides comprehensive coverage of theory and hands-on implementation of computer vision-based sensors for structural health monitoring


This book is the first to fill the gap between scientific research of computer vision and its practical applications for structural health monitoring (SHM). It provides a complete, state-of-the-art review of the collective experience that the SHM community has gained in recent years. It also extensively explores the potentials of the vision sensor as a fast and cost-effective tool for solving SHM problems based on both time and frequency domain analytics, broadening the application of emerging computer vision sensor technology in not only scientific research but also engineering practice.


Computer Vision for Structural Dynamics and Health Monitoring presents fundamental knowledge, important issues, and practical techniques critical to successful development of vision-based sensors in detail, including robustness of template matching techniques for tracking targets; coordinate conversion methods for determining calibration factors to convert image pixel displacements to physical displacements; sensing by tracking artificial targets vs. natural targets; measurements in real time vs. by post-processing; and field measurement error sources and mitigation methods. The book also features a wide range of tests conducted in both controlled laboratory and complex field environments in order to evaluate the sensor accuracy and demonstrate the unique features and merits of computer vision-based structural displacement measurement.





Offers comprehensive understanding of the principles and applications of computer vision for structural dynamics and health monitoring

Helps broaden the application of the emerging computer vision sensor technology from scientific research to engineering practice such as field condition assessment of civil engineering structures and infrastructure systems

Includes a wide range of laboratory and field testing examples, as well as practical techniques for field application

Provides MATLAB code for most of the issues discussed including that of image processing, structural dynamics, and SHM applications



Computer Vision for Structural Dynamics and Health Monitoring is ideal for graduate students, researchers, and practicing engineers who are interested in learning about this emerging sensor technology and advancing their applications in SHM and other engineering problems. It will also benefit those in civil and aerospace engineering, energy, and computer science.

Computer Vision for Structural Dynamics and Health Monitoring

Table of contents i





List of Figures vi





List of Tables xiii





Chapter 1 Introduction 1





1.1 Structural health monitoring: a quick review 1





1.2 Computer vision sensor for structural health monitoring 4





1.3 Organization of the book 8





Chapter 2 Development of Computer Vision for Structural Displacement Measurement 12





2.1 Hardware of vision sensor system 12





2.2 Software of vision sensor system: template matching techniques 15





2.2.1 Area-based template matching using cross correlation or sum of squared differences 16





MATLAB (R) Code - 2D template matching using NCC 19





2.2.2 Feature-based template matching 21





MATLAB (R) Code - Functions to detect interest points and extract feature descriptors 22





2.3 Coordinate conversion and scaling factor 23





2.3.1 Camera calibration method 24





2.3.2 Practical calibration method 26





2.4 Representative template matching algorithms 29





2.4.1 Intensity-based UCC technique 30





MATLAB (R) Code - Displacement time history measurement using UCC 32





2.4.2 Gradient-based robust OCM technique 34





MATLAB (R) Code - Template matching using OCM 37





2.4.3 Vision sensor software package and operation 40





2.5 Summary 42





Chapter 3 Performance Evaluation through Laboratory and Field Tests 44





3.1 Seismic shaking table test 44





3.2 Shaking table test of frame structure I 47





3.2.1 Test description 48





3.2.2 Subpixel resolution 49





3.2.3 Performance by tracking artificial targets 51





3.2.4 Performance by tracking natural targets 53





3.2.5 Error quantification 55





3.2.6 Evaluation of robustness of OCM and UCC 55





3.3 Seismic shaking table test of frame structure II 60





3.4 Free vibration test of a beam structure 63





3.4.1 Test description 64





3.4.2 Evaluation of the practical calibration method 65





3.5 Field Test of a pedestrian bridge 67





3.6 Field test of a highway bridge 70





3.7 Field test of two railway bridges 72





3.7.1 Test description 73





3.7.2 Daytime measurement 76





3.7.3 Nighttime measurement 78





3.7.4 Field performance evaluation 80





3.8 Remote measurement of Vincent Thomas Bridge 86





3.9 Remote measurement of Manhattan Bridge 88





3.10 Summary 92





Chapter 4 Application in Modal Analysis, Model Updating and Damage Detection 95





4.1 Experimental modal analysis 97





4.1.1 Modal analysis of a frame 98





MATLAB (R) Code - Modal analysis using ERA 101





4.1.2 Modal analysis of a beam 103





4.2 Model updating as frequency-domain optimization problem 106





MATLAB (R) Code - Model updating of the three-story frame structure 110





4.3 Damage detection 113





4.3.1 Mode shape curvature-based damage index 114





4.3.2 Test description 115





4.3.3 Damage detection results 116





MATLAB (R) Code - MSC-based damage detection of the beam structure 117





4.4 Summary 118





Chapter 5 Application in Model Updating of Railway Bridge under Trainloads 120





5.1 Field measurement of bridge displacement under trainloads 122





5.2 Formulation of finite element model 123





5.2.1 Modeling of train-track-bridge interaction 124





5.2.2 Finite element model of the railway bridge 126





5.3 Sensitivity analysis and finite element model updating 127





5.3.1 Model updating as time-domain optimization problem 127





5.3.2 Sensitivity analysis of displacement and acceleration responses 129





5.3.3 Finite element model updating 134





5.4 Dynamic characteristics of short-span bridge under trainloads 135





5.5 Summary 141





Chapter 6 Application in Simultaneous Identification of Structural Parameters and Excitation Forces 143





6.1 Simultaneous identification using vision-based displacement measurement 144





6.1.1 Structural parameter identification as a time-domain optimization problem 145





6.1.2 Force identification based on structural displacement measurement 146





6.1.3 Simultaneous identification procedure 148





6.2 Numerical example 151





6.2.1 Robustness to noise and sensor number 152





6.2.2 Robustness to initial stiffness values 156





6.2.3 Robustness to damping ratio estimation errors 157





6.3 Experimental Validation 159





6.3.1 Test description 159





6.3.2 Identification results 161





6.4 Summary 164





MATLAB (R) Code - Simultaneous identification of structural parameters and excitation forces 165





Chapter 7 Application in Cable Force Estimation 172





7.1 Vision sensor for cable force estimation 173





7.1.1 Vibration method 173





7.1.2 Procedure for vision-based cable tension estimation 174





7.2 Implementation in the Hard Rock Stadium renovation project 175





7.2.1 Hard Rock Stadium 176





7.2.2 Test description 180





7.2.3 Cable force estimation and validation 181





MATLAB (R) Code - Vibration method for cable tension force estimation 185





7.3 Implementation in the Bronx-Whitestone Bridge suspender replacement project 186





7.3.1 Bronx-Whitestone Bridge 187





7.3.2 Suspender tension estimation 188





7.4 Summary 191





Chapter 8 Achievements, Challenges and Opportunities 192





8.1 Capabilities of vision-based displacement sensor: a summary 192





8.1.1 Artificial vs. natural targets 193





8.1.2 Single- vs. multipoint measurement 193





8.1.3 Pixel vs. subpixel resolution 194





8.1.4 2D vs. 3D measurement 195





8.1.5 Real time vs. post processing 196





8.2 Error sources of vision-based displacement sensor 197





8.2.1 Camera motion 198





8.2.2 Coordinate conversion 199





8.2.3 Hardware limitations 200





8.2.4 Environmental sources 201





8.3 Vision-based displacement sensors for structural health monitoring 201





8.3.1 Dynamic displacement measurement 202





8.3.2 Modal property identification 204





8.3.3 Model updating and damage detection 205





8.3.4 Cable force estimation 207





8.4 Other civil and structural engineering applications 207





8.4.1 Automated machine visual inspection 207





8.4.2 Onsite construction tracking and safety monitoring 209





8.4.3 Vehicle load estimation 210





8.4.4 Other applications 211





8.5 Future research directions 212





Appendix: Fundamentals of digital image processing using MATLAB (R) 214





Bibliography 222