土木与环境工程学报（中英文）2024，Vol.46Issue(1)：110-121,12.DOI:10.11835/j.issn.2096-6717.2022.130
基于数字孪生和深度学习的结构损伤识别
Structural damage identification based on digital twin and deep learning
摘要
Abstract
The time span of the civil engineering structural damage state usually accounts for a small part of the total life cycle.In order to solve the problem that traditional data-driven structural damage identification methods lack enough damage state data for training,a structural damage identification method based on digital twins and deep learning is proposed in this paper for practical application.Firstly,the digital twin is constructed by using the numerical simulation model and online monitoring data to obtain the"big data"of the structural dynamic response under different damage conditions.Secondly,to get rid of the dependence on the external excitation,the empirical mode decomposition method and transmissibility function are used to preprocess the obtained data.Then,the damage identification is realized by using deep learning.To verify the effectiveness of this method,untrained monitoring data of structures are analyzed.The results show that the method has good generalization ability and can identify the structural damage condition effectively.The problem of data hunger is solved by digital twin technology,and the deep neural network trained by the intrinsic mode vibration transmissibility function data sets can still maintain a high accuracy of damage identification without any seismic information.The combination of the two methods can make structural health monitoring more active,reliable and efficient.关键词
数字孪生/深度学习/固有模态传递率函数/损伤识别/结构健康监测Key words
digital twin/deep learning/intrinsic mode vibration transmissibility function/damage identification/structural health monitoring分类
建筑与水利引用本文复制引用
唐和生,王泽宇,陈嘉缘..基于数字孪生和深度学习的结构损伤识别[J].土木与环境工程学报（中英文）,2024,46(1):110-121,12.基金项目
上海市级科技重大专项(2021SHZDZX0100) （2021SHZDZX0100）
土木工程Ⅰ类高峰学科建设经费(2022-3-YB-07)Shanghai Municipal Science and Technology Major Project(No.2021SHZDZX0100) （2022-3-YB-07）
Top Discipline Plan of Shanghai Universities-Class Ⅰ(No.2022-3-YB-07) （No.2022-3-YB-07）
