Crowdsourcing bridge dynamic monitoring with smartphone vehicle trips

Abstract

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.