Analysis of Damage in a Warren Truss Bridge Using CAE and DANN Neural Networks

Bridges require constant monitoring to detect damages. This study analyzes the Japanese Warren truss bridge using neural networks: Convolutional Autoencoder (CAE) and Domain-Adversarial Neural Network (DANN). The methodology focuses on two aspects: reconstruction of bridge acceleration data with CAE and damage analysis with DANN using CAE-processed data. CAE is trained to reconstruct acceleration data by recovering missing data and generating new data to improve dataset quality. Then, DANN uses this data to identify and evaluate anomalies in the bridge structure. The results obtained were 84% accuracy with respect to the synthetic data generated with the CAE network and 95% accuracy and an F1-score of 92% in the damage analysis of the bridge with the DANN network.