Engineering Commons^™

Ensemble-Learning Model Based Ultimate Moment Prediction Of Reinforced Concrete Members Strengthened By Uhpc, Woubishet Zewdu Taffese, Yanping Zhu, Genda Chen

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

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 …