Development of video analytics with template matching methods for using camera as sensor and application to highway bridge structural health monitoring

Abstract

Structural health monitoring (SHM) has been conducted by placing sensors on structures, so-called contact sensors, to measure displacements, strains, and accelerations of engineering structures. Contact sensing is accurate and effective, but suffers from some critical shortcomings, such as high cost, intensive labor, and traffic interruption. With the advancement and wide availability of digital video cameras, compounded with the development and enhancement of computer vision algorithms, vision-based sensing using a video camera as a sensor for SHM has become a viable alternative and a complementary method to remotely capture structural responses. Several computer vision algorithms are proposed, together with specific type of video camera, for capturing and processing the SHM video for the dynamic structural responses, e.g. displacements of selected targets. Up to date, there is a limited number of successful applications using video camera as a sensor in SHM practice. To facilitate the real-world applications of vision-based SHM, this paper presents the integrated methods, and a software tool for analyzing SHM videos using template matching algorithms along with a subpixel method that enhances the accuracy of the vision-based structural responses. The integrated approach is flexible and versatile for not only extracting displacements, strains, velocities, and accelerations, but also detecting damage of the desired multiple points or areas (templates) from videos. The methods and the software tool have been applied to a comprehensive analysis of the lab structural test, which was video-recorded by a third party without any involvement of the authors or any intension in using video camera as sensor. Good agreement for the lab test has been achieved for the structural responses (e.g. displacement and strain) and the damage detection recorded by the conventional sensors and extracted from the video by applying the integrated tool. The approach has been further validated on a highway bridge in the field, where a contact-based SHM system is permanently installed on the bridge and serves a good baseline reference for validating the proposed approach. The vision-based SHM results obtained for the highway bridge application show good correlation with the structural responses captured by conventional sensors and indicate that the developed analysis methods and software tool perform well for highway bridge test and structural health monitoring.