Damage Identification of Bridge Based on Multi-branch Convolutional Neural Network under Moving Load

Abstract

To address the challenge of detecting damage in a large number of in-service small and medium-span bridges, this study proposes a damage identification method based on a multi-branch convolutional neural network (CNN) under moving loads. Two CNN architectures—a dual-branch model and a multi-branch model—are developed for structural damage identification. The sensitivity of damage identification to sensor location is also investigated. First, various damage scenarios are simulated using a finite element model of a bridge. The structure is excited by a moving vehicle load, and the resulting structural vibration responses are extracted through transient analysis. These responses are then used as input to train and validate the established CNN models. Finally, the effectiveness and accuracy of the proposed method are verified through a laboratory-scale model test. The results demonstrate that both the dual-branch and multi-branch CNN models exhibit higher computational efficiency and better identification performance than a single-branch CNN model under multiple damage scenarios. Furthermore, the identification results show no obvious difference among sensors placed at different locations for the same damage case.