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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: | School of Applied Technologies, Qujing Normal University, Qujing 655011, Yunnan, China School of Applied Technologies, Qujing Normal University, Qujing 655011, Yunnan, China Structural Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt Civil Engineering Department, Faculty of Engineering, Horus University-Egypt, New Damietta 34517, Egypt Structural Engineering and Construction Management Department, Faculty of Engineering and Technology, Future University in Egypt (FUE), Cairo, Egypt Building Engineering & Construction Management, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh Department of Civil and Water Engineering, Laval University, Quebec City, Quebec G1V 0A6, Canada Department of Civil Engineering, University of Garmian, Kalar, Kurdistan Region 46021, Iraq 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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