WAVELET SCATTERING TRANSFORM OF BRIDGE ACCELERATION AND CLASSIFICATION OF TRAFFIC VEHICLES USING NEURAL NETWORKS

Abstract

This study aimed at the traffic sensing based on B-WIM algorithm that analyzes the traffic environment using the structural response of bridges. The time-frequency analysis based on the wavelet scattering transform extracted features from traffic-induced vibration of a bridge in service. The wavelet scattering transform calculated the scattering coefficients through the multi-layered convolution of input signals by the wavelet and scaling functions. To learn neural networks from limited number of traffic data, the learning data was amplified by subsampling the scattering coefficient in the time direction. An identification flow of traffic vehicles was proposed based on multi-level classification with learned neural networks. Even from a small number of traffic dataset, the identification of a test truck and local buses was achieved with high accuracy by amplifying the learning data.