article · Journal Of Big Data
Predicting the axial load capacity of elliptical double steel columns is essential for safe structural design. Multiple machine learning models, including Artificial Neural Networks, Gene Expression Programming, Support Vector Regression, Random Forest, and AdaBoost, were evaluated using a dataset of 119 finite element models derived from previous experimental work. Among the evaluated techniques, AdaBoost achieved the highest predictive accuracy, yielding a coefficient of determination of 0.996 and a mean absolute percentage error of 0.013 during training. The methodology was validated using cross-validation, feature importance analysis, and direct comparisons with physical testing data. To make the findings usable in practice, a dedicated graphical user interface was built for the AdaBoost model, giving structural engineers a direct computational tool to predict column performance and guide design optimisation under diverse loading conditions.
Accurately determining how much weight elliptical double steel columns can bear normally requires demanding laboratory tests or complex finite element simulations. Demonstrating that machine learning can reliably predict these structural capacities saves time and computational resources, helping engineers assess and optimise modern structural components quickly and with high precision.
This research targets structural engineers and design software developers needing rapid load assessment tools for elliptical double steel columns. Having packaged the validated AdaBoost algorithm into a functional graphical user interface, the technology is at an applied and tested stage, positioning it well for adoption into engineering consultancy workflows or commercial computer-aided design software.
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Abstract This paper presents a comprehensive investigation into the prediction of axial load capacity (P) for elliptical double steel columns (EDSCs) using a diverse set of machine learning models (MLMs). These include Artificial Neural Network (ANN), Gene Expression Programming (GEP), Support Vector Regression (SVR), Random Forest (RF), and AdaBoost. Among the models, AdaBoost demonstrated superior performance, achieving an R 2 of 0.996 and a MAPE of 0.013 during training, outperforming other models under identical conditions. Using a dataset of 119 finite element models derived from prior experimental research, the study validates the proposed solution through k-fold cross-validation, feature importance analysis, and detailed comparisons with experimental data. A Graphical User Interface (GUI) was developed specifically for the AdaBoost model due to its superior accuracy and efficiency, offering engineers a practical and accessible tool for axial load prediction in EDSC design. This research highlights the significance of using advanced machine learning techniques for structural engineering applications, providing valuable insights for the optimization of EDSC performance and design under varying conditions.
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DOI: 10.1186/s40537-025-01081-1
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