Algorithmically Enhanced Data-Driven Prediction of Shear Strength for Concrete-Filled Steel Tubes

Shengkang Zhang¹, Yong Jin^2,*, Soon Poh Yap^1,*, Haoyun Fan¹, Shiyuan Li³, Ahmed El-Shafie⁴, Zainah Ibrahim¹, Amr El-Dieb⁵
1 Department of Civil Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, 50603, Malaysia
2 Department of Civil Engineering, School of Architecture and Energy Engineering, Wenzhou University of Technology, Wenzhou, 325000, China
3 Department of Civil Engineering, The University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo, 153-8505, Japan
4 National Water & Energy Centre, United Arab Emirates University, Al Ain, P.O. Box 15551, United Arab Emirates
5 Department of Civil and Environmental Engineering, United Arab Emirates University, Al Ain, P.O. Box 15551, United Arab Emirates
* Corresponding Author: Yong Jin. Email: email ; Soon Poh Yap. Email: email

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2025.075351

Received 30 October 2025; Accepted 04 December 2025; Published online 23 December 2025

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Abstract

Concrete-filled steel tubes (CFST) are widely utilized in civil engineering due to their superior load-bearing capacity, ductility, and seismic resistance. However, existing design codes, such as AISC and Eurocode 4, tend to be excessively conservative as they fail to account for the composite action between the steel tube and the concrete core. To address this limitation, this study proposes a hybrid model that integrates XGBoost with the Pied Kingfisher Optimizer (PKO), a nature-inspired algorithm, to enhance the accuracy of shear strength prediction for CFST columns. Additionally, quantile regression is employed to construct prediction intervals for the ultimate shear force, while the Asymmetric Squared Error Loss (ASEL) function is incorporated to mitigate overestimation errors. The computational results demonstrate that the PKO-XGBoost model delivers superior predictive accuracy, achieving a Mean Absolute Percentage Error (MAPE) of 4.431% and R² of 0.9925 on the test set. Furthermore, the ASEL-PKO-XGBoost model substantially reduces overestimation errors to 28.26%, with negligible impact on predictive performance. Additionally, based on the Genetic Algorithm (GA) and existing equation models, a strength equation model is developed, achieving markedly higher accuracy than existing models (R² = 0.934). Lastly, web-based Graphical User Interfaces (GUIs) were developed to enable real-time prediction.