典型文献
Predicting the capacity of perfobond rib shear connector using an ANN model and GSA method
文献摘要：
Due to recent advances in the field of artificial neural networks(ANN)and the global sensitivity analysis(GSA)method,the application of these techniques in structural analysis has become feasible.A connector is an important part of a composite beam,and its shear strength can have a significant impact on structural design.In this paper,the shear performance of perfobond rib shear connectors(PRSCs)is predicted based on the back propagation(BP)ANN model,the Genetic Algorithm(GA)method and GSA method.A database was created using push-out test test and related references,where the input variables were based on different empirical formulas and the output variables were the corresponding shear strengths.The results predicted by the ANN models and empirical equations were compared,and the factors affecting shear strength were examined by the GSA method.The results show that the use of ANN model optimization by GA method has fewer errors compared to the empirical equations.Furthermore,penetrating reinforcement has the greatest sensitivity to shear performance,while the bonding force between steel plate and concrete has the least sensitivity to shear strength.
文献关键词：
中图分类号：
作者姓名：
Guorui SUN;Jun SHI;Yuang DENG
作者机构：
School of Civil Engineering,Central South University,Changsha 410075,China;Key Laboratory of Structures Dynamic Behavior and Control of the Ministry of Education,Harbin Institute of Technology,Harbin 150090,China;National Engineering Laboratory for High-Speed Railway Construction,Changsha 410075,China
文献出处：
引用格式：
[1]Guorui SUN;Jun SHI;Yuang DENG-.Predicting the capacity of perfobond rib shear connector using an ANN model and GSA method)[J].结构与土木工程前沿,2022(10):1233-1248
A类：
perfobond,connectors,PRSCs
B类：
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AB值：
0.487086
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