Dongming Feng, PhD, is Professor of Civil Engineering at Southeast University. His major fields of research include computer vision-based structural health monitoring, safety assessment, maintenance, and rehabilitation of cable-supported bridges. Maria Q. Feng, PhD, is Renwick Professor of Civil Engineering at Columbia University. Her research is at the forefront of multidisciplinary science and engineering within novel sensors, structural dynamics and health monitoring algorithms, nondestructive evaluation techniques, and smart materials/structures.

• List of Figures ixList of Tables xvSeries Preface xviiPreface xixAbout the Companion Website xxi1 Introduction 11.1 Structural Health Monitoring: A Quick Review 11.2 Computer Vision Sensors for Structural Health Monitoring 31.3 Organization of the Book 72 Development of a Computer Vision Sensor for Structural Displacement Measurement 112.1 Vision Sensor System Hardware 112.2 Vision Sensor System Software: Template-Matching Techniques 152.2.1 Area-Based Template Matching 162.2.2 Feature-Based Template Matching 202.3 Coordinate Conversion and Scaling Factors 222.3.1 Camera Calibration Method 232.3.2 Practical Calibration Method 252.4 Representative Template Matching Algorithms 282.4.1 Intensity-Based UCC Technique 282.4.2 Gradient-Based Robust OCM Technique 332.4.3 Vision Sensor Software Package and Operation 392.5 Summary 403 Performance Evaluation Through Laboratory and Field Tests 433.1 Seismic Shaking Table Test 433.2 Shaking Table Test of Frame Structure 1 463.2.1 Test Description 463.2.2 Subpixel Resolution 473.2.3 Performance When Tracking Artificial Targets 483.2.4 Performance When Tracking Natural Targets 493.2.5 Error Quantification 513.2.6 Evaluation of OCM and UCC Robustness 513.3 Seismic Shaking Table Test of Frame Structure 2 563.4 Free Vibration Test of a Beam Structure 593.4.1 Test Description 593.4.2 Evaluation of the Practical Calibration Method 603.5 Field Test of a Pedestrian Bridge 633.6 Field Test of a Highway Bridge 663.7 Field Test of Two Railway Bridges 673.7.1 Test Description 693.7.2 Daytime Measurements 723.7.3 Nighttime Measurements 723.7.4 Field Performance Evaluation 753.8 Remote Measurement of the Vincent Thomas Bridge 813.9 Remote Measurement of the Manhattan Bridge 823.10 Summary 874 Application in Modal Analysis, Model Updating, and Damage Detection 894.1 Experimental Modal Analysis 914.1.1 Modal Analysis of a Frame 914.1.2 Modal Analysis of a Beam 974.2 Model Updating as a Frequency-Domain Optimization Problem 1014.3 Damage Detection 1084.3.1 Mode Shape Curvature-Based Damage Index 1084.3.2 Test Description 1094.3.3 Damage Detection Results 1104.4 Summary 1125 Application in Model Updating of Railway Bridges under Trainloads 1155.1 Field Measurement of Bridge Displacement under Trainloads 1165.2 Formulation of the Finite Element Model 1185.2.1 Modeling the Train-Track-Bridge Interaction 1185.2.2 Finite Element Model of the Railway Bridge 1205.3 Sensitivity Analysis and Finite Element Model Updating 1215.3.1 Model Updating as a Time-Domain Optimization Problem 1225.3.2 Sensitivity Analysis of Displacement and Acceleration Responses 1235.3.3 Finite Element Model Updating 1275.4 Dynamic Characteristics of Short-Span Bridges under Trainloads 1305.5 Summary 1366 Application in Simultaneously Identifying Structural Parameters and Excitation Forces 1396.1 Simultaneous Identification Using Vision-Based Displacement Measurements 1406.1.1 Structural Parameter Identification as a Time-Domain Optimization Problem 1416.1.2 Force Identification Based on Structural Displacement Measurements 1426.1.3 Simultaneous Identification Procedure 1446.2 Numerical Example 1466.2.1 Robustness to Noise and Number of Sensors 1476.2.2 Robustness to Initial Stiffness Values 1506.2.3 Robustness to Damping Ratio Values 1506.3 Experimental Validation 1546.3.1 Test Description 1546.3.2 Identification Results 1556.4 Summary 1577 Application in Estimating Cable Force 1717.1 Vision Sensor for Estimating Cable Force 1727.1.1 Vibration Method 1727.1.2 Procedure for Vision-Based Cable Tension Estimation 1737.2 Implementation in the Hard Rock Stadium Renovation Project 1747.2.1 Hard Rock Stadium 1757.2.2 Test Description 1767.2.3 Estimating and Validating Cable Force 1787.3 Implementation in the Bronx-Whitestone Bridge Suspender Replacement Project 1847.3.1 Bronx-Whitestone Bridge 1847.3.2 Estimating Suspender Tension 1857.4 Summary 1878 Achievements, Challenges, and Opportunities 1918.1 Capabilities of Vision-Based Displacement Sensors: A Summary 1918.1.1 Artificial vs. Natural Targets 1928.1.2 Single-Point vs. Multipoint Measurements 1928.1.3 Pixel vs. Subpixel Resolution 1938.1.4 2D vs. 3D Measurements 1948.1.5 Real Time vs. Post Processing 1948.2 Sources of Error in Vision-Based Displacement Sensors 1958.2.1 Camera Motion 1968.2.2 Coordinate Conversion 1978.2.3 Hardware Limitations 1988.2.4 Environmental Sources 1988.3 Vision-Based Displacement Sensors for Structural Health Monitoring 1998.3.1 Dynamic Displacement Measurement 1998.3.2 Modal Property Identification 2018.3.3 Model Updating and Damage Detection 2028.3.4 Cable Force Estimation 2038.4 Other Civil and Structural Engineering Applications 2048.4.1 Automated Machine Visual Inspection 2048.4.2 Onsite Construction Tracking and Safety Monitoring 2068.4.3 Vehicle Load Estimation 2068.4.4 Other Applications 2078.5 Future Research Directions 208Appendix: Fundamentals of Digital Image Processing Using MATLAB 211A.1 Digital Image Representation 211A.2 Noise Removal 214A.3 Edge Detection 216A.4 Discrete Fourier Transform 217References 221Index 229