A new neural-network-based method for structural damage identification in single-layer reticulated shells  

作　　者：Jindong ZHANG Xiaonong GUO Shaohan ZONG Yujian ZHANG 

机构地区：[1]College of Civil Engineering,Tongji University,Shanghai 200092,China [2]China Construction Eighth Engineering Division Co.,Ltd.,Shanghai 200135,China

出　　处：《Frontiers of Structural and Civil Engineering》2024年第1期104-121,共18页结构与土木工程前沿（英文版）

基　　金：the financial support provided by the National Natural Science Foundation of China(Grant No.51478335).

摘　　要：Single-layer reticulated shells(SLRSs)find widespread application in the roofs of crucial public structures,such as gymnasiums and exhibition center.In this paper,a new neural-network-based method for structural damage identification in SLRSs is proposed.First,a damage vector index,NDL,that is related only to the damage localization,is proposed for SLRSs,and a damage data set is constructed from NDL data.On the basis of visualization of the NDL damage data set,the structural damaged region locations are identified using convolutional neural networks(CNNs).By cross-dividing the damaged region locations and using parallel CNNs for each regional location,the damaged region locations can be quickly and efficiently identified and the undamaged region locations can be eliminated.Second,a damage vector index,DS,that is related to the damage location and damage degree,is proposed for SLRSs.Based on the damaged region identified previously,a fully connected neural network(FCNN)is constructed to identify the location and damage degree of members.The effectiveness and reliability of the proposed method are verified by considering a numerical case of a spherical SLRS.The calculation results showed that the proposed method can quickly eliminate candidate locations of potential damaged region locations and precisely determine the location and damage degree of members.

关 键 词：single-layer reticulated shell damage identification neural network convolutional neural network cross-partitioning method 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]