Crowdsourcing Bridge Vital Signs with Smartphone Vehicle Trips

TL;DR

This paper demonstrates that smartphone vehicle trip data can accurately determine bridge modal frequencies, enabling cost-effective, large-scale infrastructure monitoring by repurposing existing mobile sensor data.

Contribution

It introduces an analytical method to extract bridge structural properties from crowdsourced smartphone data, showing potential for large-scale, low-cost infrastructure health monitoring.

Findings

Accurately recovered modal frequencies from smartphone data.

Repurposed existing mobile datasets for structural health insights.

Crowdsourced data can extend bridge service life by over 14 years.

Abstract

A key challenge in monitoring and managing the structural health of bridges is the high-cost associated with specialized sensor networks. In the past decade, researchers predicted that cheap, ubiquitous mobile sensors would revolutionize infrastructure maintenance; yet many of the challenges in extracting useful information in the field with sufficient precision remain unsolved. Herein it is shown that critical physical properties, e.g., modal frequencies, of real bridges can be determined accurately from everyday vehicle trip data. The primary study collects smartphone data from controlled field experiments and "uncontrolled" UBER rides on a long-span suspension bridge in the USA and develops an analytical method to accurately recover modal properties. The method is successfully applied to "partially-controlled" crowdsourced data collected on a short-span highway bridge in Italy. This study verifies that pre-existing mobile sensor data sets, originally captured for other purposes, e.g., commercial use, public works, etc., can contain important structural information and therefore can be repurposed for large-scale infrastructure monitoring. A supplementary analysis projects that the inclusion of crowdsourced data in a maintenance plan for a new bridge can add over fourteen years of service (30% increase) without additional costs. These results suggest that massive and inexpensive datasets collected by smartphones could play an important role in monitoring the health of existing transportation infrastructure.