Machine Learning for Predicting Required Cross-Sectional Dimensions of Circular Concrete-Filled Steel Tubular Columns

Abstract

Machine learning methods are widely used to predict the bearing capacity of concrete-filled steel tubular (CFST) columns. However, in addition to this task, the engineer often faces the inverse problem: to determine what cross-section dimensions of the CFST column are required for given loads. This paper is devoted to the development of machine learning models for predicting the geometric parameters of a circular cross-section for concrete-filled steel tubular (CFST) columns under the combined action of bending moments and compressive axial forces. This problem has not been solved by machine learning methods before. The main focus is on automating the design process of CFST columns using the CatBoost algorithm and artificial neural networks. Three machine learning models were developed to solve the problem. The first and second models are based on the CatBoost algorithm. They predict the column diameter at minimum and maximum wall thicknesses, respectively. The third model is an artificial neural network, which is designed to determine the wall thickness of a CFST column. The models were trained on synthetic data generated in accordance with Russian design codes. The first and second models demonstrated high accuracy in predicting the column diameter (RMSE = 3.86 mm and 4.12 mm, respectively). The third model showed high efficiency over the entire range of wall thicknesses (correlation coefficient R = 0.99974). Feature importance analysis using SHAP values confirmed the key role of bending moment and axial force in predicting geometric parameters.

Topics & Concepts