Machine Learning Surrogate for Seismic Response of a Wooden House: A Comparison of SHAP, Sobol, and Morris Sensitivity Analyses

Understanding the influence of structural parameters on the seismic response of wooden houses is essential for improving structural performance and model reliability. However, conducting extensive parametric studies using nonlinear time-history analysis is computationally expensive. To address this issue, this study proposes a machine learning (ML) surrogate framework for efficiently evaluating the seismic response of a wooden house and interpreting the importance of structural parameters. A dataset consisting of 289 nonlinear structural simulations was used to train the surrogate model, enabling efficient evaluation of parameter importance through multiple sensitivity analysis methods. A Gradient Boosting regression model was developed to approximate the results of nonlinear structural analyses. The surrogate model predicted the maximum inter-story drift with high accuracy, achieving a coefficient of determination of R2 = 0.90. Using the trained surrogate model, six sensitivity analysis methods were applied: SHAP, Structural Perturbation, Drop-column Importance, Permutation Importance, Sobol sensitivity analysis, and the Morris method. The results showed that most sensitivity analysis methods consistently identified wall-related parameters, particularly W1, W3, and W4, as the dominant factors influencing structural response. This tendency was observed in both elastic and nonlinear response ranges, although the influence of these parameters became more pronounced under nonlinear conditions. While the Morris method produced slightly different sensitivity magnitudes due to its screening-based formulation, it still identified the same dominant parameters as the other approaches. The results demonstrate that the proposed ML surrogate framework, combined with explainable AI techniques, can effectively identify key structural parameters governing the seismic response of wooden structures. This approach provides a computationally efficient tool for structural sensitivity analysis and may support improved structural modeling and seismic performance evaluation.