Yu Focai, Isleem Haytham F, Almoghayer Walaa J K, Shahin Ramy I, Yehia Saad A, Khishe Mohammad, Elshaarawy Mohamed Kamel
School of Art and Design, Yunnan Light and Textile Industry VocationalCollege, Kunming City, 650300, Yunnan Province, China.
Department of Computer Science, University of York, York, YO10 5DD, UK.
Sci Rep. 2025 Apr 15;15(1):12899. doi: 10.1038/s41598-025-97258-y.
The current study investigates the application of artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), in predicting the ultimate load-carrying capacity and ultimate strain ofboth hollow and solid hybrid elliptical fiber-reinforced polymer (FRP)-concrete-steel double-skin tubular columns (DSTCs) under axial loading. Implemented AI techniques include five ML models - Gene Expression Programming (GEP), Artificial Neural Network (ANN), Random Forest (RF), Adaptive Boosting (ADB), and eXtreme Gradient Boosting (XGBoost) - and one DL model - Deep Neural Network (DNN).Due to the scarcity of experimental data on hybrid elliptical DSTCs, an accurate finite element (FE) model was developed to provide additional numerical insights. The reliability of the proposed nonlinear FE model was validated against existing experimental results. The validated model was then employed in a parametric study to generate 112 data points.The parametric study examined the impact of concrete strength, the cross-sectional size of the inner steel tube, and FRP thickness on the ultimate load-carrying capacity and ultimate strain of both hollow and solid hybrid elliptical DSTCs.The effectiveness of the AI application was assessed by comparing the models' predictions with FE results.Among the models, XGBoost and RF achieved the best performance in both training and testing with respect to the determination coefficient (R), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) values. The study provided insights into the contributions of individual features to predictions using the SHapley Additive exPlanations (SHAP) approach. The results from SHAP, based on the best prediction performance of the XGBoost model, indicate that the area of the concrete core has the most significant effect on the load-carrying capacity of hybrid elliptical DSTCs, followed by the unconfined concrete strength and the total thickness of FRP multiplied by its elastic modulus. Additionally, a user interface platform was developed to streamline the practical application of the proposed AI models in predicting the axial capacity of DSTCs.
当前的研究探讨了人工智能（AI）技术，包括机器学习（ML）和深度学习（DL），在预测空心和实心混合椭圆纤维增强聚合物（FRP）-混凝土-钢双壁圆管柱（DSTC）在轴向荷载作用下的极限承载能力和极限应变方面的应用。所应用的人工智能技术包括五个机器学习模型——基因表达式编程（GEP）、人工神经网络（ANN）、随机森林（RF）、自适应增强（ADB）和极端梯度提升（XGBoost）——以及一个深度学习模型——深度神经网络（DNN）。由于混合椭圆DSTC的实验数据稀缺，因此开发了一个精确的有限元（FE）模型以提供更多的数值见解。所提出的非线性有限元模型的可靠性通过与现有实验结果进行验证。经验证的模型随后用于参数研究，以生成112个数据点。参数研究考察了混凝土强度、内钢管的横截面尺寸以及FRP厚度对空心和实心混合椭圆DSTC的极限承载能力和极限应变的影响。通过将模型预测结果与有限元结果进行比较，评估了人工智能应用的有效性。在这些模型中，XGBoost和RF在训练和测试中，就决定系数（R）、均方根误差（RMSE）和平均绝对误差（MAE）值而言，表现最佳。该研究使用SHapley加法解释（SHAP）方法，深入了解了各个特征对预测的贡献。基于XGBoost模型的最佳预测性能，SHAP的结果表明，混凝土核心区的面积对混合椭圆DSTC的承载能力影响最大，其次是无约束混凝土强度以及FRP总厚度与其弹性模量的乘积。此外，还开发了一个用户界面平台，以简化所提出的人工智能模型在预测DSTC轴向承载力方面的实际应用。
