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.