典型文献
Structural performance assessment of GFRP elastic gridshells by machine learning interpretability methods
文献摘要：
The prediction of structural performance plays a significant role in damage assessment of glass fiber reinforcement polymer(GFRP)elastic gridshell structures.Machine learning(ML)approaches are implemented in this study,to predict maximum stress and displacement of GFRP elastic gridshell structures.Several ML algorithms,including linear regression(LR),ridge regression(RR),support vector regression(SVR),K-nearest neighbors(KNN),decision tree(DT),random forest(RF),adaptive boosting(AdaBoost),extreme gradient boosting(XGBoost),category boosting(CatBoost),and light gradient boosting machine(LightGBM),are implemented in this study.Output features of structural performance considered in this study are the maximum stress as f1(x)and the maximum displacement to self-weight ratio asf2(x).A comparative study is conducted and the Catboost model presents the highest prediction accuracy.Finally,interpretable ML approaches,including shapely additive explanations(SHAP),partial dependence plot(PDP),and accumulated local effects(ALE),are applied to explain the predictions.SHAP is employed to describe the importance of each variable to structural performance both locally and globally.The results of sensitivity analysis(SA),feature importance of the CatBoost model and SHAP approach indicate the same parameters as the most significant variables for1(x)andf2(x).
文献关键词：
中图分类号：
作者姓名：
Soheila KOOKALANI;Bin CHENG;Jose Luis Chavez TORRES
作者机构：
Department of Civil Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;Department of Civil Engineering,Technical University of Loja,Loja 110102,Ecuador
文献出处：
引用格式：
[1]Soheila KOOKALANI;Bin CHENG;Jose Luis Chavez TORRES-.Structural performance assessment of GFRP elastic gridshells by machine learning interpretability methods)[J].结构与土木工程前沿,2022(10):1249-1266
A类：
gridshells,gridshell,asf2,shapely,for1,andf2
B类：
Structural,performance,assessment,GFRP,elastic,by,machine,learning,interpretability,methods,structural,plays,significant,role,damage,glass,fiber,reinforcement,polymer,structures,Machine,ML,approaches,implemented,this,study,maximum,stress,displacement,Several,algorithms,including,linear,regression,LR,ridge,RR,support,vector,SVR,nearest,neighbors,KNN,decision,tree,DT,random,forest,RF,adaptive,boosting,AdaBoost,extreme,gradient,XGBoost,category,CatBoost,light,LightGBM,Output,features,considered,f1,self,weight,ratio,comparative,conducted,Catboost,model,presents,highest,accuracy,Finally,interpretable,additive,explanations,SHAP,partial,dependence,plot,PDP,accumulated,effects,ALE,applied,explain,predictions,employed,describe,importance,each,both,locally,globally,results,sensitivity,analysis,SA,indicate,same,parameters,most,variables
AB值：
0.563446
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