Isleem Haytham F, Qiong Tang, Alsaadawi Mostafa M, Elshaarawy Mohamed Kamel, Mansour Dina M, Abdullah Faruque, Mandor Ahmed, Sor Nadhim Hamah, Jahami Ali
School of Applied Technologies, Qujing Normal University, Qujing, 655011, Yunnan, China.
Structural Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
Sci Rep. 2024 Aug 12;14(1):18647. doi: 10.1038/s41598-024-68360-4.
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.
本文研究了椭圆形截面的混杂纤维增强塑料（FRP）混凝土 - 钢柱的性能。该研究通过文献收集信息并进行参数研究来开展，得到了116个数据点。此外，还开发了多个机器学习预测模型，以准确估计FRP管破裂时约束混凝土的极限应变和极限荷载。所提出的模型采用了决策树（DT）、随机森林（RF）、自适应提升（ADAB）、分类提升（CATB）和极端梯度提升（XGB）等机器学习技术。最后，对这些模型进行了可视化和定量验证与评估。得出的结论是，CATB和XGB是突出的模型，具有高精度和强泛化能力。CATB模型略胜一筹，因为其在测试期间的错误率始终较低，这表明在考虑准确性和抗过拟合稳健性时，它是该数据集的最佳模型。
