Wireless SmartVision system for synchronized displacement monitoring of railroad bridges

The deflection of railroad bridges under in-service loads is an important indicator of the structure's health. Over the past decade, an increasing number of studies have demonstrated the efficacy of using vision-based approaches for displacement tracking of civil infrastructure. These studies have relied primarily on external processing of manually recorded videos of a structure's motion to estimate displacements. To date, vision-based techniques applied to long-term structural health monitoring have yet to be proven effective as an alternative to the traditional displacement measurement methods, such as linear variable differential transformers. This paper proposes a wireless SmartVision system (WSVS) that uses edge computing to directly output bridge displacements that can be sent to the end user. The system estimates displacements using both target-free and target-based approaches. A synchronized sensing framework is developed for multipoint displacement estimation using several wireless vision-based nodes for full-scale displacement-based modal analysis of structures. Pose estimation using an AprilTag, a fiducial marker, is employed with a modified algorithm for improved displacement tracking of targets installed on a bridge, yielding subpixel accuracy. The robustness of the results in field conditions is enhanced by linking a tracking quality factor to each timestamp to handle vision-related uncertainties. To meet the need for precise error metrics evaluation, an inexpensive cyber-physical setup using a synthetic testing environment is also developed in this study. Following laboratory validation, field tests on a cable-stayed pedestrian bridge were performed to demonstrate the efficacy of the proposed WSVS.

Automated summary

This paper proposes a wireless SmartVision system for monitoring the displacement of bridges, which directly outputs displacement results through edge computing. The system estimates displacements using both target-free and target-based approaches, and utilizes multiple wireless vision-based nodes for full-scale displacement analysis. By introducing pose estimation and tracking quality factor, the accuracy and robustness of displacement tracking are improved.