首页|China Academy of Building Research Researcher Reveals New Findings on Machine Learning (Machine Learning Prediction Model for Boundary Transverse Reinforcement of Shear Walls)
China Academy of Building Research Researcher Reveals New Findings on Machine Learning (Machine Learning Prediction Model for Boundary Transverse Reinforcement of Shear Walls)
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New study results on artificial intelligence have been published. According to news originating from Beijing, People's Republic of China, by NewsRx editors, the research stated, "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." Funders for this research include Beijing Natural Science Foundation; Special Funding of China Academy of Building Research. Our news journalists obtained a quote from the research from China Academy of Building Research: "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."
China Academy of Building ResearchBeijingPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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