Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/7476
Title: Numerical and machine learning modeling of GFRP confined concrete-steel hollow elliptical columns
Authors: Haytham F. Isleem
Tang Qiong
Mostafa M. Alsaadawi
Mohamed Kamel Elshaarawy
Dina M. Mansour
Faruque Abdullah
Ahmed Mandor
Nadhim Hamah Sor
Hussein Jahami, Ali 
Affiliations: Department of Civil and Environmental Engineering 
Keywords: Elliptical columns
Machine learning
Finite element method
ABAQUS
GFRP
Hybrid columns
Issue Date: 2024-08-12
Part of: Scientific Reports
Volume: 14
Issue: 1
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.
URI: https://scholarhub.balamand.edu.lb/handle/uob/7476
DOI: 10.1038/s41598-024-68360-4
Open URL: Link to full text
Type: Journal Article
Appears in Collections:Department of Civil and Environmental Engineering

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