Strength prediction of circular CFST columns through advanced machine learning methods. (1st July 2022)

• Record Type: Journal Article
• Authors: Hou, Chao; Zhou, Xiao-Guang
• Is Part Of: Journal of building engineering. Volume 51(2022)
• Publication Date: 2022-07-01
• Subjects: Concrete-filled steel tube (CFST) -- Machine learning (ML) -- Strength prediction -- Axial compression strength -- Correlation analysis; Building -- Periodicals; 690.05
• DOI: 10.1016/j.jobe.2022.104289
• Languages: English
• ISSNs: 2352-7102

Abstract

Abstract: Concrete-filled steel tube (CFST), well recognized for its excellent mechanical behaviour and economic efficiency, is widely used as a main load-carrying component in various kinds of structures. Machine learning (ML) is one of the promising artificial intelligence methods which just starts to be utilized for the advanced prediction of structural performances. This paper attempts to evaluate the feasibility of combining mechanism analysis to optimize ML models in predicting the axial compression strength of circular CFST columns. A comprehensive database containing 2, 045 circular CFSTs under axial loading was established through extensive literature survey. Based on correlation analysis and mechanism analysis, input parameters for ML models were rationally selected. Then back-propagation neural network (BPNN), genetic algorithm (GA)-BPNN, radial basis function neural network (RBFNN), Gaussian process regression (GPR) and multiple linear regression (MLR) models were established. It was revealed that the established ML models, especially GPR, could reliably predict the strengths of CFST with higher accuracies and wider applicable ranges than existing methods in current design standards. By subdividing the database according to column slenderness, ML models achieved improved accuracy for strength prediction, whilst little effect on the model accuracy was generated by random subdivisions. This indicates that when adopting ML methods in structural engineering sector, optimization of the models can be expected on the basis of rational understanding towards the corresponding structural mechanism. Highlights: A comprehensive database composed of 2, 045 circular CFST test samples is established. Correlation and mechanism analysis are adopted to identify input parameters for machine learning models. Mainstream machine learning methods BPNN, GA-BPNN, RBFNN and GPR are established for strength prediction. GA-BPNN, RBFNN and GPR models are optimized through database subdivision as per failure mechanisms. The influence of random subdivision and conditional subdivision on the model effectiveness is evaluated.