物理信息神经网络在桥梁移动荷载识别中的应用研究OA

Bridge moving load identification is a key technology in structural health monitoring and intelligent transportation systems,and it is of great significance for ensuring bridge safety and optimizing maintenance.The application of intelligent control technology in this field has promoted its development.This paper focuses on the innovative application of the physics-informed neural networks(PINNs)algorithm in bridge moving load identification.Research shows that PINNs,by embedding the beam vibration partial differential equation into the neural network loss function,can significantly improve moving load identification accuracy,noise resistance,and physical consistency.Its applications mainly include bridge displacement reconstruction,influence surface identification,and synchronous identification of multi-vehicle loads.The current main challenges lie in computational efficiency,handling of complex boundary conditions,and the high dependence of model reliability on physical equations.The research results can provide a reference for in-depth study and application of bridge intelligent monitoring technology.