Research on Neural Network Analysis Model of Bearing Capacity of Steel Tubed Steel Reinforced Concrete Cylinder

LIU Jian; ZHAO Yu; WANG Fei-cheng; LIU Zhang-jiang; CENG Rong-sen; ZHOU Guan-gen; QI Yu-liang; REN Da; CHEN Yuan; XIAO Hai-peng; PENG Lin-miao

doi:10.13204/j.gyjzg22010519

Volume 52 Issue 9

Sep. 2022

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Article Navigation > INDUSTRIAL CONSTRUCTION > 2022 > 52(9): 147-152,120

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LIU Jian, ZHAO Yu, WANG Fei-cheng, LIU Zhang-jiang, CENG Rong-sen, ZHOU Guan-gen, QI Yu-liang, REN Da, CHEN Yuan, XIAO Hai-peng, PENG Lin-miao. Research on Neural Network Analysis Model of Bearing Capacity of Steel Tubed Steel Reinforced Concrete Cylinder[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(9): 147-152,120. doi: 10.13204/j.gyjzg22010519

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Research on Neural Network Analysis Model of Bearing Capacity of Steel Tubed Steel Reinforced Concrete Cylinder

doi: 10.13204/j.gyjzg22010519

1. College of Civil Engineering, Guangzhou University, Guangzhou 510006, China;
2. Guangdong Engineering Technology Research Center for Complex Steel Structures, Guangzhou 510006, China;
3. Zhejiang Southeast Space Frame Co., Ltd., Hangzhou 311200, China;
4. Guangzhou Construction Industry Research Institute Co., Ltd., Guangzhou 510000, China

Received Date: 2022-01-05
Available Online: 2023-02-06

Abstract

Abstract

According to the existing calculation formula of the bearing capacity of steel tubed steel reinforced concrete (TSRC) columns under axial and eccentric compression, the neural network analysis model of the bearing capacity of TSRC columns under axial and eccentric compression was proposed. Ten sensitive parameters affecting the bearing capacity were selected to determine the number of nodes in the input layer, and the bearing capacity of TSRC cylinder was taken as the output layer. The number of nodes in the hidden layer was determined as 12 according to the mean square error MSE and correlation coefficient R by trial and error method, and the N10-12-1 neural network analysis model was established. The prediction results of the neural network analysis model show that the maximum error was only 6.08%, indicating that the established neural network analysis model for the bearing capacity of TSRC cylinder under axial compression and eccentric londing was a good method. Finally, sensitivity analysis based on Garson algorithm was carried out to obtain the influence degree of each input parameter on the bearing capacity of TSRC cylinder, which could be used for reference in engineering design.

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References

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