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Monitoring and managing the structural health of bridges requires expensive specialized sensor networks. In the past decade, researchers predicted that cheap ubiquitous mobile sensors would revolutionize infrastructure maintenance; yet extracting useful information in the field with sufficient precision remains challenging. Herein we report the accurate determination of critical physical properties, modal frequencies, of two real bridges from everyday vehicle trip data. We collected smartphone data from controlled field experiments and uncontrolled Uber rides on a long-span suspension bridge in the USA (The Golden Gate Bridge) and developed an analytical method to accurately recover modal properties. We also successfully applied the method to partially-controlled crowdsourced data collected on a short-span highway bridge in Italy. Further analysis projected that the inclusion of crowdsourced data in a maintenance plan for a new bridge could add over fourteen years of service (30% increase) without additional costs. Our results suggest that massive and inexpensive datasets collected by smartphones could play a role in monitoring the health of existing transportation infrastructure.

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There is a growing attention in real-time bridge condition assessment using data from drive-by vehicles as a potentially scalable approach. Most system identification methods are based on synchronized vibration data collection for this purpose. This study presents an approach for bridge modal identification that estimates high-resolution absolute value of the operational mode shapes using asynchronous mobile data. With each trip of a vehicular sensor, the spatio-temporal response of the bridge is sampled, along with various sources of noise, e.g. vehicle dynamics, environmental effects, road profile, etc. The crowdsourced modal identification using continuous wavelet (CMICW) method is proposed that gradually magnifies the bridge dynamical signatures and mitigates noise over the spatio-temporal map. The performance of the CMICW method is validated in an experimental setting. The method successfully identifies natural frequencies and absolute value of the operational mode shapes of a bridge with high resolution and accuracy. Notably, by including data collected from various bridge lanes, the method can reconstruct 3D representation of the mode shapes. The influence of the speed of the mobile sensors on the accuracy of the estimated modal properties is investigated as well. Using a hybrid simulation framework, the effect of vehicle dynamic is included in the mobile sensing data. The study shows that the CMICW method is successful in discounting the effect of vehicle dynamics, thereby strengthening the bridge modal information. Finally, a blind source separation technique is implemented to separate the effects of road irregularities, which further improves the accuracy of modal property estimates. This study contributes to the growing body of knowledge on mobile crowdsensing for physical properties of transportation infrastructure.