Bridging the Gap: Commodifying Infrastructure Condition Data with Crowdsensing

Abstract

Structural information deficits about our aging bridges allowed several avoidable catastrophes in recent years. Data-driven methods for bridge vibration monitoring enable frequent, accurate structural assessments; however, the high costs of large-scale deployments of these systems make important condition information a luxury for bridge owners. Smartphone-based monitoring is inexpensive yet has produced structural information, i.e., modal frequencies, in crowdsensing applications. However, scalable bridge damage detection systems are unknown. Here we present the most extensive real-world study on bridge monitoring with crowdsourced smartphone-vehicle trips and simulate damage detection capabilities. Our method analyzes over 500 trips across four bridges with main spans ranging from 30 to 1300 meters in length, representing about one-quarter of US bridges, and extracts absolute value mode shapes, a damage-sensitive feature. We demonstrate a bridge health monitoring platform compatible with ride-sourcing data streams that check conditions daily. The result is the potential to commodify data-driven structural assessments globally.