Predicting the ultimate strength of rectangular concrete-filled steel tube columns under eccentric loading using a knowledge-enhanced machine learning framework

Junbin Lou, Yixuan Li, Shicheng Zheng, Qian Feng, Guannan Wang, Rongqiao Xu, Xudong Qian,
Predicting the ultimate strength of rectangular concrete-filled steel tube columns under eccentric loading using a knowledge-enhanced machine learning framework,
Engineering Applications of Artificial Intelligence,
Volume 160, Part B,
2025,
111935,
ISSN 0952-1976,
https://doi.org/10.1016/j.engappai.2025.111935.
(https://www.sciencedirect.com/science/article/pii/S0952197625019372)
Abstract: Rectangular concrete-filled steel tubes (RCFST) are widely used as structural columns in the field of structural engineering due to their exceptional mechanical properties. In practice, RCFST columns often experience eccentric loads, resulting in combined compression and bending. Although several empirical equations describe the mechanical behavior of CFST columns in terms of mechanical mechanism, their predictive ability is still limited by the necessary simplifying assumptions and application scope. This paper presents the development of a knowledge-enhanced machine learning (KeML) framework, with 15 input parameters, to address the limitations of conventional deterministic approaches and existing machine learning approaches, both suffering from low accuracy and robustness. To facilitate this, a comprehensive database consisting of 447 experimental data is established and divided into training and test sets, enabling the training and evaluation of the proposed KeML model. Comparative analyses against original machine learning models reveal that embedding knowledge can improve predictive accuracy and weak robustness, as well as reduce the required number of training data. Furthermore, a sensitivity analysis demonstrates that the cross-section size predominantly influences the ultimate strength of the RCFST columns subjected to eccentric loading. Finally, based on the sensitivity analysis and correlation analysis, a predictive equation for the ultimate strength of RCFST columns is developed using genetic programming.
Keywords: Rectangular concrete-filled steel tube columns; Knowledge-enhanced; Eccentric loading; Ultimate strength prediction; Machine learning; Interpretability