Predicting axial load capacity in elliptical fiber reinforced polymer concrete steel double skin columns using machine learning.

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(2)), 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.