Numerical and machine learning modeling of GFRP confined concrete-steel hollow elliptical columns

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Авг. 12, 2024

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 confined ultimate strain load concrete at rupture tube. Decision Tree (DT), Random Forest (RF), Adaptive Boosting (ADAB), Categorical (CATB), eXtreme Gradient (XGB) techniques utilized for proposed models. Finally, these visually quantitatively verified evaluated. It concluded that CATB XGB are standout models, offering high accuracy strong generalization capabilities. model is slightly superior due its consistently lower error rates during testing, indicating it best this dataset when considering both robustness against overfitting.

Язык: Английский

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Dimensionality analysis in assessing the unconfined strength of lime-treated soil using machine learning approaches

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Фев. 1, 2025

Язык: Английский

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Artificial intelligence models for predicting unconfined compressive strength of mixed soil types: focusing on clay and sand

Asian Journal of Civil Engineering, Год журнала: 2025, Номер unknown

Опубликована: Март 14, 2025

Язык: Английский

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Prediction of swelling pressure of expansive soil using machine learning methods

Asian Journal of Civil Engineering, Год журнала: 2024, Номер unknown

Опубликована: Окт. 21, 2024

Язык: Английский

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