Bridge damage identification under the moving vehicle loads based on the method of physics-guided deep neural networks

Abstract

Modeling uncertainty or modeling error has been widely recognized as one major challenge for damage identification of bridge structures by moving vehicles. This study proposed a physics-guided deep neural network approach to improve the accuracy of damage identification in vehicle-induced bridge vibration response, which aims to minimize the influence of the modeling uncertainty on damage identification. Firstly, the physical relationship between the damage to the bridge with the acceleration signal of the bridge, the physics-guided deep neural network, and its damage diagnosis method is introduced. The feasibility of the damage identification method based on physics-guided deep neural networks is theoretically explained. Secondly, a numerical case of damage identification of the simply supported bridge under moving vehicle loads is conducted to compare the accuracy of damage identification between the proposed method and the traditional method. Finally, a bridge field test is implemented to verify the applicability and effectiveness of the proposed method. The results illustrate that the method can be utilized to accurately identify the damage locations of simply supported bridges under a moving vehicle.