ArticleScientific reports2024
Prediction of the axial compression capacity of stub CFST columns using machine learning techniques.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Explainable Ensemble Machine Learning for the Prediction and Optimization of Pozzolanic Concrete Compressive Strength.Polymers · 2026Article
- An Interpretable Ensemble Machine Learning Framework for Predicting the Ultimate Flexural Capacity of BFRP-Reinforced Concrete Beams.Polymers · 2026Article
- A comparative machine and deep learning approach for predicting ultimate bearing capacity of shallow foundations in cohesionless soil.Scientific reports · 2025Article
- Predicting flexural strength of hybrid FRP-steel reinforced beams using symbolic regression and ML techniques.Scientific reports · 2025Article
- Prediction of the axial compression capacity of ECC-CES columns using adaptive sampling and machine learning techniques.Scientific reports · 2025Article
- Symbolic regression for strength prediction of eccentrically loaded concrete-filled steel tubular columns.Scientific reports · 2025Article
- Experimental and analytical study on axial behaviour of square corrugated concrete filled single and double skin tube stub columns.Scientific reports · 2025Article
- Strength prediction of ECC-CES columns under eccentric compression using adaptive sampling and ML techniques.Scientific reports · 2025Article
- Circular rubber aggregate CFST stub columns under axial compression: prediction and reliability analysis.Scientific reports · 2024Article
- STM-based symbolic regression for strength prediction of RC deep beams and corbels.Scientific reports · 2024Article
- Prediction and reliability analysis of shear strength of RC deep beams.Scientific reports · 2024Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Concrete-filled steel tubular (CFST) columns have extensive applications in structural engineering due to their exceptional load-bearing capability and ductility. However, existing design code standards often yield different design capacities for the same column properties, introducing uncertainty for engineering designers. Moreover, conventional regression analysis fails to accurately predict the intricate relationship between column properties and compressive strength. To address these issues, this study proposes the use of two machine learning (ML) models-Gaussian process regression (GPR) and symbolic regression (SR). These models accept a variety of input variables, encompassing geometric and material properties of stub CFST columns, to estimate their strength. An experimental database of 1316 specimens was compiled from various research papers, including circular, rectangular, and double-skin stub CFST columns. In addition, a dimensionless output variable, referred to as the strength index, is introduced to enhance model performance. To validate the efficiency of the introduced models, predictions from these models are compared with those from two established standard codes and various ML algorithms, including support vector regression optimized with particle swarm optimization (PSVR), artificial neural networks, XGBoost (XGB), CatBoost (CATB), Random Forest, and LightGBM models. Through performance metrics, the CATB, GPR, PSVR and XGB models emerge as the most accurate and reliable models from the evaluation results. In addition, simple and practical design equations for the different types of CFST columns have been proposed based on the SR model. The developed ML models and proposed equations can predict the compressive strength of stub CFST columns with reliable and accurate results, making them valuable tools for structural engineering. Furthermore, the Shapley additive interpretation (SHAP) technique is employed for feature analysis. The results of the feature analysis reveal that section slenderness ratio and concrete strength parameters negatively impact the compressive strength index.
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