Damage identification of simply supported beam based on autocorrelation standard deviation vectors with BP neural network and fiber optic sensing

In order to effectively identify the damage characteristics of simply supported beam, a damage identification method for simply supported beam combining autocorrelation standard deviation vector and BP neural network is proposed. The damage simulation of simply supported beam is carried out through numerical simulation to obtain the strain response under different damage conditions, and then the autocorrelation of the strain response is processed to obtain the autocorrelation standard deviation vector damage index, and the autocorrelation standard deviation vector before and after the damage of the simply supported beam is used as the input parameter of the neural network to establish the damage identification model. Based on the fiber optic grating (FBG) sensing network technology, the strain response of simply supported beam is obtained, and the feasibility of the damage identification model is verified through experiments. The experimental results show that this method can accurately recognize the damage location and damage degree of simply supported beam.