基于LSTM神经网络的多源数据融合桥梁变形重构方法

A multi-source data fusion method for bridge displacement reconstruction based on LSTM neural network

桥梁变形是桥梁状态评估的重要指标,在测量桥梁位移时,直接测量方法如LVDT、RTK-GPS、LDV等在实际应用中有很多局限性。由于从所测得的动应变中获得实时的位移模态和应变模态较为困难,而且大多数应变测量的采样频率较低,基于应变的间接桥梁变形重构方法在应用中仍存在一定的不足。提出一种基于长短时记忆(LSTM)神经网络重构桥梁变形的方法,该网络融合应变和加速度数据在训练后可以用作测量数据实时重构桥梁的变形。在应变模态、位移模态以及桥梁中性轴未知的情况下,该方法可以准确地重构变形。通过数值模拟和试验验证了重构结果的准确性,结果表明,基于LSTM的数据融合方法在不同条件下都可以实现高精度的桥梁变形重构。

Deformation of the bridge is an important index of bridge condition assessment.The direct measurement methods for the bridge deformation such as LVDT,RTK-GPS and LDV have a lot of limitations.Because it is difficult to obtain the real-time displacement modes and strain modes from the measured dynamic strains while the sampling frequency of strain measurement is low,the indirect bridge deformation reconstruction method based on strain still has some shortcomings in engineering practice.This paper proposed a data fusion method to reconstruct the deformation of bridge structure based on LSTM neural network using the measured strain and acceleration data.In this paper,the accuracy of the reconstruction results is verified by numerical simulation and experiment without strain modes,displacement modes and neutral axis.The results show that data fusion method based on LSTM can achieve high accuracy under different conditions.