Research on axial bearing capacity of rectangular concrete-filled steel tubular columns based on artificial neural networks

• Published in: Frontiers of Computer Science Vol. 11; no. 5; pp. 863 - 873
• Main Authors: DU, Yansheng, CHEN, Zhihua, ZHANG, Changqing, CAO, Xiaochun
• Format: Journal Article
• Language: English
• Published: Beijing Higher Education Press 01.10.2017; Springer Nature B.V
• Subjects: artificial neural network; Artificial neural networks; axial bearing capacity; Bearing capacity; Computer Science; Concrete columns; model prediction; Neural networks; parametric study; Prediction models; rectangular CFT columns; Research Article; Steel columns; Steel ratios; Steel tubes; Thickness ratio; 人工神经网络模型; 力学模型; 土木工程; 实验数据; 矩形钢管混凝土柱; 轴压承载力; 轴向承载力; 高强度材料; artificial neural network; model prediction; axial bearing capacity; rectangular CFT columns; parametric study
• ISSN: 2095-2228; 2095-2236
• DOI: 10.1007/s11704-016-5113-6

Design of rectangular concrete-filled steel tubular （CFT） columns has been a big concern owing to their complex constraint mechanism. Generally, most existing methods are based on simplified mechanical model with limited experimental data, which is not reliable under many conditions, e.g., columns using high strength materials. Artificial neural network （ANN） models have shown the effectiveness to solve complex problems in many areas of civil engineering in recent years. In this paper, ANN models were employed to predict the axial bearing capacity of rectangular CFT columns based on the experimental data. 305 experimental data from articles were collected, and 275 experimental samples were chosen to train the ANN models while 30 experimental samples were used for testing. Based on the comparison among different models, artificial neural network modell （ANN1） and artificial neural network model2 （ANN2） with a 20- neuron hidden layer were chosen as the fit prediction models. ANN1 has five inputs： the length （D） and width （B） of cross section, the thickness of steel （t）, the yield strength of steel （fy）, the cylinder strength of concrete （fc＇）- ANN2 has ten inputs： D, B, t, fy, f′, the length to width ratio （D/B）, the length to thickness ratio （D/t）, the width to thickness ratio （B/t）, restraint coefficient （ξ）, the steel ratio （α）. The axial beating capacity is the output data for both models.The outputs from ANN1 and ANN2 were verified and compared with those from EC4, ACI, GJB4142 and AISC360-10. The results show that the implemented models have good prediction and generalization capacity. Parametric study was conducted using ANN1 and ANN2 which indicates that effect law of basic parameters of columns on the axial bearing capacity of rectangular CFT columns differs from design codes.The results also provide convincing design reference to rectangular CFT columns.