Trial of traffic environment sensing using neural networks and time domain analysis of bridge acceleration response including ground vibration propagation

Abstract

This study attempts to construct a traffic environment sensing system using a bridge vibration response and proposes a method for detecting traffic using time-domain feature extraction and a neural network. Analysis was performed by examining the feature values of bridge acceleration, the wave group period, and the delay of the waveforms that focus on the mode shape. After evaluating the features, a neural network was constructed to identify the traffic environment around the bridge. According to the verification, the matching rate reached 90 % if there was no congestion, demonstrating the possibility of meeting the accuracy requirement of the mechanical traffic census.