基于模型嵌入循环神经网络的损伤识别方法

Model-Embedding based Damage Detection Method for Recurrent Neural Network

目前,绝大多数基于深度学习的结构损伤识别方法依靠深度神经网络自动提取结构的损伤敏感特征,并通过损伤状态之间特征的差异实现模式分类识别.然而,这些方法面临着损伤量化难度大的挑战,并且需要大量的模型训练数据.本文提出基于模型嵌入循环神经网络(Model-Embedding Recurrent Neural Network,MERNN)的损伤识别方法.首先,通过数据驱动的卷积神经网络(Convolutional Neural Network,CNN)建立荷载-响应之间的映射关系,然后,利用龙格库塔法改进传统的循环神经网络,建立基于循环神经网络架构的数值计算单元.最后,基于结构响应计算值与实测响应残差构成的损失函数与神经网络的自动微分机制来实现结构刚度参数的更新,进而实现结构损伤识别.数值模拟框架与实验室的3层剪切型框架的损伤识别结果表明,本文提出的方法能基于少量响应数据准确量化结构损伤.

Currently,the majority of structure damage identification methods based on deep learning rely on deep neural networks to automatically extract damage-sensitive features of structures and achieve pattern classification recognition through the differences in features between damage states.However,these methods face challenges in the accurate quantification of damage and require a large amount of data for model training.This article proposes a damage detection method based on a model-embedding recurrent neural network(MERNN).Firstly,a data-driven convolutional neural network was used to establish the mapping relationship between load and response.Then,the traditional recurrent neural network was improved using the Runge-Kutta method to create a numerical computing unit based on the recurrent neural network architecture.Finally,based on the loss function composed of the residual errors between measured responses and computed responses,the structural stiffness parameters were updated with the automatic differentiation mechanism of the neural network to achieve structural damage identification.Damage identification results of a numerical three-layer frame and a laboratory-scale shear-type frame indicate that the proposed method can accurately quantify structural damage based on the limited amount of response datas.