Bridge Failure Risk Prediction Using Geospatial Data Processing via Multi-Head Attention Deep Learning Model
TL;DR
This study introduces a novel deep learning framework using InSAR data and multi-head attention to predict bridge failure risks, aiming to enhance infrastructure safety with scalable monitoring.
Bridge Failure Risk Prediction Using Geospatial Data Processing via Multi-Head Attention Deep Learning Model
Francesco Della Santa; Federico Sisci; Farbod Khosro Anjom; Flavio Pino; Marco Civera
https://doi.org/10.1109/ACCESS.2025.3648853
Volume 14
Bridge failures are a significant threat to infrastructure safety and public security, which demand cost-effective and scalable monitoring systems. This work proposes a novel data-driven framework for early warning of bridge collapse risk, leveraging Interferometric Synthetic Aperture Radar (InSAR) displacement time series and Deep Learning. A mathematical formulation of a bridge-collapse risk ind...