Long-Term Monitoring of Bridge Settlements using Vision-Based Embedded System , PEER Report 2020/26

Abstract: 

The State of California is highly seismic, capable of generating large-magnitude earthquakes that could cripple the infrastructure of several large cities. Yet the annual maintenance of the State’s bridges, such as highway overpasses, is not robust due to budget and staff constraints. Over 1000 bridges were not inspected according to the California Department of Transportation’s (Caltrans) 2015 Maintenance Plan. To help engineers monitor infrastructure conditions, presented within is a device recently developed that employs modern sensing, computing, and communication technologies to autonomously measure and remotely report vertical settlements of bridges, such as highway overpasses. Given the limitations of existing measurement devices, we propose a novel vision-based method that employs a camera to take a picture of a projected laser beam. This new device is referred to as the Projected Laser Target Method (PLTM).

This report documents the embedded system design and development of two prototypes. The first prototype implements communication over a local WIFI network using synchronous code to measure distance over time; this PLTM is deployed in a laboratory setting. The second device under study implements communication over a Bluetooth Low Energy system using asynchronous code and communication over 2G cellular networks using synchronous code, with the aim of determining its accuracy in the field. This report evaluates the performance of the field-suitable system in terms of its system reliability, measurement accuracy and precision, power consumption, and its overall system performance.

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Author: 
Henry L. Teng
Khalid Mosalam
Publication date: 
December 15, 2020
Publication type: 
Technical Report
Citation: 
Teng, H. L., & Mosalam, K. (2020). Long-Term Monitoring of Bridge Settlements using Vision-Based Embedded Systems (Report No. 2020/25). Pacific Earthquake Engineering Research Center (PEER). https://apps.peer.berkeley.edu/publications/peer_reports/reports_2020/2020_26_Teng_Final.pdf