Machine Learning Prediction Model for Boundary Transverse Reinforcement of Shear Walls

Abstract

Due to their roles as efficient lateral force-resisting systems, reinforced concrete shear walls exert a tremendous degree of influence on the overall seismic performance of buildings. The ability to predict the boundary transverse reinforcement of shear walls is critical to the seismic design process, as well as in the overall evaluation and retrofitting of existing buildings. Contemporary empirical models attain low predictive accuracy, with an inability to capture nonlinearity between boundary transverse reinforcement and different influencing variables. This study proposes a boundary transverse reinforcement prediction model for shear walls with boundary elements based on the demand of ductility. Using the extreme gradient boosting machine learning algorithm and 501 samples, some 52 input variables are considered, and a subset with six features is selected, monitored, and analyzed using both internal methods (gain and cover) and external methods. The results (R2=0.884) display superior predictive capacity compared with existing models. Interpretation and error analysis are performed. Safety analysis is conducted to obtain references for use in practical engineering. Overall, this study presents a more accurate tool for use in seismic design and provides references for the evaluation and retrofitting of existing buildings. Our contributions hold significant implications for enhancing the safety and resilience of reinforced concrete structures.

Keywords

• demand of ductility
• boundary transverse reinforcement
• machine learning
• experimental database
• safety analysis