Intelligent local buckling design of stainless steel I-sections in fire via Artificial Neural Network

被引：8
|
作者：
Xing, Zhe [1 ,2 ,3 ]
Wu, Kaidong [3 ,4 ]
Su, Andi [5 ,6 ]
Wang, Yuanqing [2 ]
Zhou, Guangen [1 ]
机构：
[1] Zhejiang Southeast Space Frame Co LTD, Hangzhou 311209, Peoples R China
[2] Tsinghua Univ, Dept Civil Engn, Beijing 100084, Peoples R China
[3] Hohai Univ, Coll Civil & Transportat Engn, Nanjing 210024, Peoples R China
[4] MCC Grp, Cent Res Inst Bldg & Construct Co Ltd, Beijing 100088, Peoples R China
[5] Harbin Inst Technol, Key Lab Struct Dynam Behav & Control, Minist Educ, Harbin 150090, Peoples R China
[6] Harbin Inst Technol, Key Lab Smart Prevent Mitigat Civil Engn Disasters, Minist Ind & Informat Technol, Harbin 150090, Peoples R China
来源：
STRUCTURES | 2023年 / 58卷
基金：
中国国家自然科学基金;
关键词：
Stainless steel; Local buckling; Fire; I-sections; Artificial Neural Network;
D O I：
10.1016/j.istruc.2023.105356
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
Traditional local buckling design methods of stainless steel I-sections in fire generally adopt the effective width method. However, in order to precisely consider the influence of fire conditions, traditional methods are tedious and their accuracy is not ideal. To fill this gap, an intelligent local buckling design method of stainless steel Isections in fire via Artificial Neural Network (ANN) is proposed. A comprehensive data set including test and FE results is built and the correlation among parameters is evaluated. Based on this data set, ANN models are developed and optimized in accordance with the benchmark of Kruppa's criteria, and then k-Fold crossvalidation is conducted to avoid overfitting. Finally, the optimized ANN models are assessed and compared with traditional design methods in terms of accuracy and reliability, which indicates that ANN is suitable for the local buckling design of stainless steel I-sections in fire.
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页数：12
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