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

Turn off MathJax

Article Contents

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

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

Citation:

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

Citation:

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

PDF( 3790 KB)

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.

FullText(HTML)

References(27)

References

[1]	刘坚.钢与混凝土组合结构设计原理[M].北京:科学出版社, 2005.
[2]	刘坚, 田勇, 刘长江, 等.圆钢管钢筋再生混凝土短柱轴压承载力[J].建筑科学与工程学报, 2020, 37(5):97-105.
[3]	刘坚, 张鹏程, 江进, 等.圆钢管 H 型钢再生混凝土短柱的轴压承载力分析[J].西南交通大学学报, 2020, 55(6):1280-1286.
[4]	CHENG G, ZHOU X, LIU J, et al.Seismic behavior of circular tubed steel-reinforced concrete column to steel beam connections[J].Thin-Walled Structures, 2019, 138(5):485-495.
[5]	张鹏程.钢管(约束)H型钢再生混凝土短柱轴压力学性能研究[D].广州:广州大学, 2020.
[6]	YU Q, TAO Z, LIU W, et al.Analysis and calculations of steel tube confined concrete (STCC) stub columns[J].Journal of Constructional Steel Research, 2010, 66(1):53-64.
[7]	GEYER P, SINGARAVEL S.Component-based machine learning for performance prediction in building design[J].Applied Energy, 2018, 228:1439-1453.
[8]	TRUNG N T, SHAHGOLI A F, ZANDI Y, et al.Moment-rotation prediction of precast beam-to-column connections using extreme learning machine[J].Structural Engineering and Mechanics, 2019, 70(5):639-647.
[9]	幸坤涛, 赵国藩, 岳清瑞.高强钢管混凝土核心柱抗震性能神经网络评估[J].工业建筑, 2002, 32(10):63-65.
[10]	刘坚著.钢结构高等分析的二阶非弹性理论与应用[M].北京:科学出版社, 2012:255-310.
[11]	刘坚, 潘澎, 李东伦, 等.考虑楼板刚度贡献的梁柱节点半刚性连接弯矩-转角神经网络模型[J].建筑科学与工程学报, 2016, 33(1):99-105.
[12]	AHMADI M, NADERPOUR H, KHEYRODDIN A.ANN model for predicting the compressive strength of circular steel-confined concrete[J].International Journal of Civil Engineering, 2017, 15:213-221.
[13]	JAYALEKSHMI S, JEGADESH J S S, GOEL A.Empirical approach for determining axial strength of circular concrete filled steel tubular columns[J].Journal of The Institution of Engineers (India):Series A, 2018, 99(2):257-268.
[14]	NADERPOUR H, NAGAI K, FAKHARIAN P, et al.Innovative models for prediction of compressive strength of FRP-confined circular reinforced concrete columns using soft computing methods[J].Composite Structures, 2019, 215(5):69-84.
[15]	张清允, 陈志华, 王小盾.基于神经网络的矩形钢管高强混凝土计算方法[J].石家庄铁道大学学报(自然科学版), 2018, 31(1):11-15.
[16]	WANG H J, ZHU H B, WEI H.Bearing capacity of concrete filled square steel tubular columns based on neural network[J].Advanced Materials Research, 2012, 502:193-197.
[17]	TRAN V L, THAI D K, NGUYEN D D.Practical artificial neural network tool for predicting the axial compression capacity of circular concrete-filled steel tube columns with ultra-high-strength concrete[J].Thin-Walled Structures, 2020, 151.DOI: 10.1016/j.tws.2020.106720.
[18]	DU Y S, CHEN Z H, ZHANG C Q, et al.Research on axial bearing capacity of rectangular concrete-filled steel tubular columns based on artificial neural networks[J].Frontiers of Computer Science, 2017, 5 (11):863-873.
[19]	王宣鼎.钢管约束型钢混凝土短柱轴压及偏压力学性能研究[D].哈尔滨:哈尔滨工业大学, 2013.
[20]	闫标.钢管约束型钢混凝土中长柱静力性能及设计方法研究[D].兰州:兰州大学, 2013.
[21]	刘连鹏.圆钢管约束型钢超高强混凝土短柱轴压受力性能研究[D].大连:大连理工大学, 2015.
[22]	王昕培.圆钢管约束型钢高强混凝土短柱受压力学性能研究[D].大连:大连理工大学, 2018.
[23]	飞思科技产品研发中心.神经网络理论与MATLAB7实现[M].北京:北京电子工业出版社, 2005.
[24]	TRASK A, HILL F, REED S, et al.Neural arithmetic logic units[C]//The Thirty-Second Annual Conference on Neural Information Processing Systems.Saarland:DBLP, 2018:8046-8055.
[25]	邓庆明.基于因素影响的水土流失程度敏感性研究[J].黑龙江水利科技, 2019, 47(11):157-160.
[26]	JULIAN D O, MICHAEL K J, RUSSELL G D.An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data[J].Ecological Modelling, 2004, 178(3/4):389-397.
[27]	GARSON G D.Interpreting neural-network connection weights[J].AI Expert, 1991, 6(4):46-51.