Bridge Structural Health Monitoring using Asynchronous Mobile Sensing Data

TL;DR

This paper introduces a novel crowdsourced mobile sensing method using smartphones in passing vehicles to identify bridge modal properties, effectively mitigating noise and vehicle effects, and successfully extracting torsional mode shapes.

Contribution

It presents the CMICW method for bridge modal identification from mobile data, including a hybrid simulation framework to account for vehicle dynamics, and demonstrates high accuracy in experimental validation.

Findings

Successfully identifies natural frequencies and mode shapes of a bridge.

First to extract torsional mode shape information from mobile sensor data.

Effectively mitigates vehicle and environmental noise effects.

Abstract

This study presents a flexible approach for bridge modal identification using smartphone data collected by a large pool of passing vehicles. With each trip of a mobile sensor, the spatio-temporal response of the bridge is sampled, plus various sources of noise, e.g., vehicle dynamics, environmental effects, and road profile. This paper provides further evidence to support the hypothesis that through trip aggregation, such noise effects can be mitigated and the true bridge dynamics are exhibited. In this study, the continuous wavelet transform is applied to each trip, and the results are combined to estimate the structural modal response of the bridge. The Crowdsourced Modal Identification using Continuous Wavelets (CMICW) method is presented and validated in an experimental setting. In summary, the method successfully identifies natural frequencies and absolute mode shapes of a bridge with high accuracy. Notably, these results are the first to extract torsional mode shape information from mobile sensor data. Moreover, the influence of vehicle speed on the estimation accuracy is investigated. Finally, a hybrid simulation framework is proposed to account for the vehicle dynamics within the raw mobile sensing data. The proposed method is successful in removing vehicle dynamic effects and identifying modal properties. These results contribute to the growing body of knowledge on the practice of mobile crowdsensing for physical properties of transportation infrastructure.