Computer Vision for Structural Dynamics and Health Monitoring. Dongming Feng

software for measuring displacement time histories from video images. General principles are presented, including various template‐matching techniques for tracking targets and coordinate‐conversion methods for converting image pixel displacements to physical displacements. Vision sensor software packages are developed for real‐time multipoint displacement measurement based on two representative template‐matching techniques: upsampled cross‐correlation (UCC) and orientation code matching (OCM).

      Chapter 3 presents a wide range of tests conducted in both laboratory and field environments to evaluate the performance of the vision‐based sensor system for dynamic displacement measurement. The accuracy of the measured displacement time histories is evaluated by comparing vision sensor results from tracking high‐contrast artificial targets or low‐contrast natural targets on the structural surface with those obtained with conventional reference sensors. The robustness of the vision sensor is examined against adverse environmental conditions such as dim light, background image disturbance, and partial template occlusion. The vision sensor system is also tested on outdoor in situ structures, including a pedestrian bridge, a highway bridge, two railway bridges, and two long‐span suspension bridges. Dynamic displacements induced by various excitations are measured during the daytime and at night from different distances with and without artificial targets installed. These tests confirm the efficacy of the computer vision sensor system for measuring structural dynamic responses in outdoor environments.

      Chapters 4–7 demonstrate the use of measured displacement data for SHM. Chapter 4 compares modal analysis results based on displacement response data with those from conventional acceleration data. Furthermore, the identified modal parameters are used to update structural parameters such as the stiffness of a three‐story frame structure and to detect damage in a beam structure.

      Chapter 5 describes a model‐updating approach for railway bridges, which is based on time‐domain optimization of analytical models using in situ measurement of the bridge displacement time histories under trainloads. A finite element model of the bridge is developed, considering the train‐track‐bridge dynamic interaction. A sensitivity analysis investigates the intrinsic effects of parameters of the train, track, and bridge subsystems on the dynamic response of the bridge. The model‐updating approach is applied to a short‐span bridge to identify train parameters such as speed as well as bridge structural parameters such as stiffness. The computer vision–based model updating approach can be developed into an effective tool for long‐term SHM of short‐span railway bridges.

Chapter 6 explores a method for simultaneous identification of structural parameters and unknown excitation forces by using only displacement response (i.e. output‐only), as in reality it is often highly difficult to measure excitation forces (i.e. input). Numerical analysis investigates the accuracy, convergence, and robustness of the identified results. Laboratory experiments on a beam structure accurately identified the hammer excitation forces as well as the beam stiffness from the beam displacement response measured by a single camera, validating this output‐only method and demonstrating its potential for low‐cost, long‐term SHM.

      Chapter 7 presents the application of the computer vision sensor for cost‐effective estimation of tension forces in cables, the most important component in cable‐supported bridges and roof structures. Compared with the existing vibration method based on acceleration measurements, which requires the installation of sensors on the cable, noncontact computer vision measurement of the cable vibration represents significant time and cost savings. This computer vision–based method is implemented in two engineering projects to estimate the cable forces of the cable‐supported roof structure of the Hard Rock Stadium in Florida and the suspender forces of the Bronx‐Whitestone Bridge in New York. Satisfactory agreement is found between cable forces measured by the vision‐based sensor and conventional accelerometers.

      Chapter 8 provides an overview of the achievements made thus far in computer vision sensor technology through a state‐of‐the‐art literature review as well as a summary of this book. It also discusses challenges and opportunities, which the authors hope will inspire continued research on an extended adoption of computer vision technology for solving civil and structural engineering problems.

      Appendix A further introduces the fundamentals of digital image processing using MATLAB, including digital image representation, noise removal, edge detection, and discrete Fourier transform.