Ensemble-learning model based ultimate moment prediction of reinforced concrete members strengthened by UHPC.

Data-driven model development brings new approaches to solve conventional civil engineering problems, which are usually considered and answered by experimental, analytical, and numerical methods. This study aims to develop an ensemble learning model (i.e., XGBoost: eXtreme Gradient Boosting) to predict ultimate moment of reinforced concrete (RC) members strengthened by a newly developed concrete technology – ultrahigh performance concrete (UHPC). The study considered two scenarios, incorporating eighteen and seventeen features, with one feature modification involving the transformation of width and height to the cross-sectional area of RC members. Incorporating three substrate damage levels, two substrate surface treatments preceding UHPC strengthening, and five UHPC layouts as model features, the hyperparameters of the XGBoost model are fined-tuned through random search and k-fold cross-validation techniques. Model performance is evaluated using metrics of mean-absolute error (MAE), mean-square error (MSE), root-mean-square error (RMSE), and coefficient of determination (R 2). The XGBoost model, trained on a dataset of170 instances, achieves an R 2 accuracy of 92.5% in the training set and 81.9% in the unseen testing set. Based on the feature significance score, the top four most influential features were reinforcement ratio in UHPC layer (ρ u) , longitudinal reinforcement ratio in RC member (ρ sl ) , UHPC thickness (hu), and RC member height (h), collectively contributing more than half of the model's predictive power. Feature engineering does not yield significant benefits. In comparison to other tree-based ensemble learning models (Random Forest, AdaBoost, Gradient Boosting, and Bagging), the developed XGBoost model, demonstrates superior overall prediction performance. This research has practical implications for the design and analysis of UHPC strengthened RC members. The model's accuracy is expected to improve with additional data collection to address imbalances in feature distributions within the current limited dataset. • An ensemble model-XGBoost is developed to predict ultimate moment of RC members strengthened by UHPC for the first time. • Eighteen features were considered in the machine learning models. • XGBoost model shows better prediction than Random Forest, AdaBoost, Gradient Boosting and Bagging. • Reinforcement ratios in UHPC layer and RC member, UHPC thickness, and RC thickness are top four influential features. [ABSTRACT FROM AUTHOR]