Performance comparison of five regression-based machine learning techniques for estimating load-carrying capacity of steel frame using direct analysis

Abstract

The development of computer science has promoted the application of scientific and technological achievements to construction engineering in general and steel structure design in particular. Recently, the steel frame structure design has applied advanced analytical methods to take into account the inelastic behavior of the material and the geometric nonlinear properties of the structure, leading to the results obtained close to the real structure. In addition, to reduce computational time and effort, it has been applied advanced machine learning (ML) algorithms to predict behavior, helping to accelerate decision-making, improve efficiency, and reduce errors. In this paper, 5 popular machine learning algorithms are currently being conducted for the regression-based to estimate the ultimate load capacity of steel frames, including Linear Regression, Deep Learning, Support Vector Machine, Random Forest, and XGBoost. The effectiveness of applying these methods is tested through a numerical example surveying a 20-story space steel frame. The performance of ML algorithms is evaluated by comparing the mean-squared error (MSE) and computational efforts. The results show that among the 5 selected methods, the XGBoost has obvious advantages and is superior in terms of both MSE and calculation time.