ArticleScientific reports2024
Numerical and machine learning modeling of GFRP confined concrete-steel hollow elliptical columns.
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 4 papers.
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4 citing papers in PubMed.
- An optimized machine learning approach for reliable design of hybrid FRP-steel-concrete tubular columns.Scientific reports · 2026Article
- Predicting axial load capacity in elliptical fiber reinforced polymer concrete steel double skin columns using machine learning.Scientific reports · 2025Article
- An interpretable deep learning model for the accurate prediction of mean fragmentation size in blasting operations.Scientific reports · 2025Article
- Prediction of ultimate strength and strain in FRP wrapped oval shaped concrete columns using machine learning.Scientific reports · 2025Article
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
This article investigates the behavior of hybrid FRP Concrete-Steel columns with an elliptical cross section. The investigation was carried out by gathering information through literature and conducting a parametric study, which resulted in 116 data points. Moreover, multiple machine learning predictive models were developed to accurately estimate the confined ultimate strain and the ultimate load of confined concrete at the rupture of FRP tube. Decision Tree (DT), Random Forest (RF), Adaptive Boosting (ADAB), Categorical Boosting (CATB), and eXtreme Gradient Boosting (XGB) machine learning techniques were utilized for the proposed models. Finally, these models were visually and quantitatively verified and evaluated. It was concluded that the CATB and XGB are standout models, offering high accuracy and strong generalization capabilities. The CATB model is slightly superior due to its consistently lower error rates during testing, indicating it is the best model for this dataset when considering both accuracy and robustness against overfitting.
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