湖南大学学报（自然科学版）2024，Vol.51Issue(7)：21-29,9.DOI:10.16339/j.cnki.hdxbzkb.2024064
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
摘要
Abstract
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.关键词
循环神经网络/龙格库塔法/损伤识别Key words
recurrent neural network/Runge-Kutta method/damage detection分类
建筑与水利引用本文复制引用
翁顺,雷奥琦,陈志丹,于虹,颜永逸,余兴胜..基于模型嵌入循环神经网络的损伤识别方法[J].湖南大学学报（自然科学版）,2024,51(7):21-29,9.基金项目
国家重点研发计划资助项目(2023YFC3805700),National Key R&D Program of China(2023YFC3805700) （2023YFC3805700）
中铁第四勘察设计院集团有限公司课题(KY2023014S,KY2023126S),Research Fund of China Railway Siyuan Survey and Design Group Co.,Ltd.(KY2023014S,KY2023126S) （KY2023014S,KY2023126S）
华中科技大学交叉研究支持计划(2023JCYJ014),Interdisciplinary Research Support Program of HUST(2023JCYJ014) （2023JCYJ014）
