Collaborative Research: A Novel Video-Assisted Integrated Approach for Enhancing Bridge Health Monitoring

University Of California-Irvine, Irvine CA

Investigators

Abstract

Abstract Collaborative Projects 0510507 (M. Feng, Univ California/ Ivine) 0510655 (Chin-An Tang, Wayne Statye Univ) In the long-term health monitoring of bridge structures, system identification that is essential for assessing the global condition of the bridge is often based on measured vibration response or the system output. Two major obstacles remain in the path to realization of an integrated health monitoring system. First, knowledge of the input (i.e., traffic excitation) is unknown or limited, rendering an accurate assessment of the state of the bridge structures difficult. Second, efficient procedure must be developed for processing and interpreting the vast amount of data from sensors. This research focuses on developing a novel, video-assisted approach for enhancing bridge health monitoring. Important traffic information characterizing the input excitation, such as the vehicle arrival times, speeds and types, is proposed to be extracted from digital video. Since camera systems are readily available in many existing bridges (as surveillance cameras), this proposal represents a low-cost approach to significantly enhance our understanding of the traffic input. Novel algorithms for processing and analyzing synchronized traffic information and bridge vibration data will perform integrated system identification and improve the health monitoring This is a joint research between two universities encompassing the expertise necessary for the successful completion of this project. Professor Tan and Professor Yin from WSU will be in charge of (1) development and verification of the traffic excitation model and the bridge damage diagnosis on a statistical basis, (2) vehicle classification from the video pattern recognition and (3) web-based data sharing and presentation of the monitoring project for educational purpose. Professor Feng at UCI will take the lead in (1) installation of weather-proof video cameras and field data collection at a fully instrumented bridge, (2) development of real-time algorithms on a chip allowing transmission of processed traffic data to significantly reduce the amount of data transmission and (3) development of system identification algorithms based on system output and partial input information obtained from the video.