Megahed Khaled, Mahmoud Nabil Said, Abd-Rabou Saad Elden Mostafa
Department of Structural Engineering, Mansoura University, PO BOX 35516, Mansoura, Egypt.
Sci Rep. 2024 Oct 31;14(1):26245. doi: 10.1038/s41598-024-74990-5.
Extensive studies support using steel tubes to enhance the structural integrity of rubber aggregate concrete (RBAC), namely RBAC-filled steel tubes (RCFST). However, current design codes for assessing the axial compressive behaviour of circular stub RCFST (CS-RCFST) columns are limited. Furthermore, there is a scarcity of studies focused on ensuring the structural safety of these columns. Based on an extensive experimental database comprising 145 columns, this study explores machine learning (ML) capabilities for predicting the axial strength of CS-RCFST columns, using six typical machine-learning models, i.e., symbolic regression (SR), XGBoost, CatBoost, random forest, LightGBM, and Gaussian process regression models. The hyperparameter tuning of the introduced ML models is performed using the Bayesian Optimization technique. The comparison results show that the CatBoost model is the most reliable and accurate ML model (R = 0.999 and 0.993 for the training and testing sets, respectively). In addition, a simple and practical design expression for CS-RCFST columns has been developed with acceptable accuracy based on the SR model (an average test-to-prediction ratio of 0.99 and CoV of 0.132). Meanwhile, the axial strength predicted by ML models was compared with two prominent practice codes (i.e., AISC360 and EC4). The comparison results indicated that the ML models could introduce a highly reliable and accurate approach over current design standards for strength prediction. Furthermore, a reliability analysis is conducted on two different ML models to evaluate the reliability of utilising ML models in practical design applications. This assessment involves identifying the statistical properties associated with the compressive strength of RBAC, as well as introducing the required resistance design factors aligned with the target reliability recommended by code standards.
大量研究支持使用钢管来增强橡胶集料混凝土（RBAC）的结构完整性，即钢管内填充橡胶集料混凝土（RCFST）。然而，目前用于评估圆形短柱RCFST（CS-RCFST）柱轴向压缩性能的设计规范有限。此外，专注于确保这些柱结构安全的研究也很匮乏。基于一个包含145根柱的广泛实验数据库，本研究使用六种典型的机器学习模型，即符号回归（SR）、XGBoost、CatBoost、随机森林、LightGBM和高斯过程回归模型，探索机器学习（ML）预测CS-RCFST柱轴向强度的能力。所引入的ML模型的超参数调整采用贝叶斯优化技术。比较结果表明，CatBoost模型是最可靠、最准确的ML模型（训练集和测试集的R分别为0.999和0.993）。此外，基于SR模型开发了一个简单实用的CS-RCFST柱设计表达式，其精度可接受（平均测试与预测比为0.99，变异系数为0.132）。同时，将ML模型预测的轴向强度与两个著名的实践规范（即AISC360和EC4）进行了比较。比较结果表明，与当前强度预测设计标准相比，ML模型可以引入一种高度可靠和准确的方法。此外，对两种不同的ML模型进行了可靠性分析，以评估在实际设计应用中使用ML模型的可靠性。该评估包括确定与RBAC抗压强度相关的统计特性，以及引入与规范标准推荐的目标可靠性一致的所需抗力设计系数。
