Bridge Damage Identification Based on Encoded Images and Convolutional Neural Network

Abstract

Bridges are prone to damage from various factors, impacting the overall safety of transportation networks. Accurate damage identification is crucial for maintaining bridge integrity. This study proposes a novel method using encoded images and a convolutional neural network (CNN) for bridge damage identification. By converting raw acceleration data into encoded images, the data can be represented from multiple perspectives, enhancing the extraction of essential features related to bridge damage states. The method was validated using data simulated from a continuous rigid-frame bridge model. The results demonstrate that using encoded images as inputs yields a higher recall rate, precision, and F1-score compared to using acceleration responses as inputs, achieving a comprehensive accuracy of 92%. This study concludes that the combination of encoded images and CNN provides a robust approach for accurate and efficient bridge damage identification.