Enhancing Bridge Structural Health Monitoring Through Acoustic Sensing: A Comprehensive Approach to Traffic Analysis and Incident Detection

This paper introduces a pioneering approach to bridge structural health monitoring through the development of a unified microphone assembly. This system enhances the detection and localization of vehicles on bridges by utilizing generalized cross-correlation and Time Difference of Arrival (TDOA) analysis. A novel combination of audio features is processed using Convolutional Neural Networks (CNN) and Vision Transformers (ViT) to classify vehicle types while detecting critical incidents like crashes and skidding. The proposed integrated system processes the Acoustic Emissions into a comprehensive database that records each vehicle's timestamp, lane occupancy, movement direction, vehicle type, and detected incidents. The effectiveness of this system not only demonstrates its potential to improve bridge safety but also sets a new benchmark for applying acoustic technologies in structural health monitoring.