Crowdsensing-based bridge vibration monitoring using a sparse network of random mobile sensors: Theory and numerical verifications

Abstract

Vibration monitoring of bridges is essential for the safety and maintenance of transportation infrastructure. Traditional methods rely on placing sensors directly on bridges, a process that is often costly and difficult to scale. An emerging alternative involves utilizing sensors mounted within vehicles as they traverse the bridge. However, this approach often faces challenges with continuous monitoring due to the limited time vehicles spend on the structure. This paper presents a novel framework for predicting bridge responses and identifying its modal characteristics through the crowdsensing of sparse vibration data from a network of vehicles traversing the bridge. The framework employs vehicles’ body accelerations and positional data to estimate bridge responses at distributed virtual fixed sensing nodes (VFSNs). By randomly selecting some vehicles as sensing agents at sequential timestamps, it ensures a reliable and continuous flow of data. Additionally, the framework mitigates the influence of road roughness and vehicle dynamics by utilizing residual contact-point responses between the rear and front axles of the sensing vehicles. Simulations of a three-span bridge under realistic traffic conditions, including road roughness and vehicle–bridge interaction, were conducted to validate the framework’s accuracy. Despite an 80% data missing rate and relying on only two sensing agents along with 17 VFSNs, the framework successfully identified the first three modes of the bridge with MAC values above 95% and natural frequencies with relative errors below 3%. Response predictions showed an accuracy exceeding 70%. Various factors were investigated, including traffic speed, the number of sensing agents and VFSNs, ambient noise effects, and the impact of the random vehicle selection process. The results confirmed the robustness of the framework against ambient noise and randomness in sensing agent selection. The optimal configuration was identified as two sensing agents and 17 VFSNs.