Parameter Identification Method using Quality Engineering and Interpretable Machine Learning for Full-Scale Shaking Table Tests of Wooden Houses

Abstract

Parameter Identification of seismic performance is important for accurate estimation of seismic performance and health monitoring of structures. In this study, we proposed a parameter identification method using quality engineering and interpretable machine learning "SHAP". Interpretable AI provided insight into the impact of the parameters on the analysis results. Understanding the importance of parameters leads to narrowing the range of parameter and efficient parameter identification. In this study, the method was validated on displacement response data obtained from a full-scale shake table experiment on 3-story wooden house. After data assimilation, the analysis results were closer to the experimental results, and the good results after data assimilation indicated their effectiveness. This method is also expected to be useful to support trial and error for the review process of analysis model.