Please use this identifier to cite or link to this item:
https://scholarhub.balamand.edu.lb/handle/uob/7476
DC Field | Value | Language |
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dc.contributor.author | Haytham F. Isleem | en_US |
dc.contributor.author | Tang Qiong | en_US |
dc.contributor.author | Mostafa M. Alsaadawi | en_US |
dc.contributor.author | Mohamed Kamel Elshaarawy | en_US |
dc.contributor.author | Dina M. Mansour | en_US |
dc.contributor.author | Faruque Abdullah | en_US |
dc.contributor.author | Ahmed Mandor | en_US |
dc.contributor.author | Nadhim Hamah Sor | en_US |
dc.contributor.author | Hussein Jahami, Ali | en_US |
dc.date.accessioned | 2024-08-21T10:05:57Z | - |
dc.date.available | 2024-08-21T10:05:57Z | - |
dc.date.issued | 2024-08-12 | - |
dc.identifier.uri | https://scholarhub.balamand.edu.lb/handle/uob/7476 | - |
dc.description.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. | en_US |
dc.language.iso | eng | en_US |
dc.subject | Elliptical columns | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Finite element method | en_US |
dc.subject | ABAQUS | en_US |
dc.subject | GFRP | en_US |
dc.subject | Hybrid columns | en_US |
dc.title | Numerical and machine learning modeling of GFRP confined concrete-steel hollow elliptical columns | en_US |
dc.type | Journal Article | en_US |
dc.identifier.doi | 10.1038/s41598-024-68360-4 | - |
dc.contributor.affiliation | School of Applied Technologies, Qujing Normal University, Qujing 655011, Yunnan, China | en_US |
dc.contributor.affiliation | School of Applied Technologies, Qujing Normal University, Qujing 655011, Yunnan, China | en_US |
dc.contributor.affiliation | Structural Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt | en_US |
dc.contributor.affiliation | Civil Engineering Department, Faculty of Engineering, Horus University-Egypt, New Damietta 34517, Egypt | en_US |
dc.contributor.affiliation | Structural Engineering and Construction Management Department, Faculty of Engineering and Technology, Future University in Egypt (FUE), Cairo, Egypt | en_US |
dc.contributor.affiliation | Building Engineering & Construction Management, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh | en_US |
dc.contributor.affiliation | Department of Civil and Water Engineering, Laval University, Quebec City, Quebec G1V 0A6, Canada | en_US |
dc.contributor.affiliation | Department of Civil Engineering, University of Garmian, Kalar, Kurdistan Region 46021, Iraq | en_US |
dc.contributor.affiliation | Department of Civil and Environmental Engineering | en_US |
dc.description.volume | 14 | en_US |
dc.description.issue | 1 | en_US |
dc.date.catalogued | 2024-08-21 | - |
dc.description.status | Published | en_US |
dc.identifier.openURL | https://www.nature.com/articles/s41598-024-68360-4 | en_US |
dc.relation.ispartoftext | Scientific Reports | en_US |
crisitem.author.parentorg | Faculty of Engineering | - |
Appears in Collections: | Department of Civil and Environmental Engineering |
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