Commodifying infrastructure spatial dynamics with crowdsourced smartphone data

Structural information deficits about our aging bridges have led to 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, current methods cannot extract spatial vibration characteristics, which are needed for damage identification. 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.

Methods
This data set was collected from various sources: the research team, ANAS employees, and Uber drivers. The method for data collection and data processing for each dataset can be found in the related works.