What question did this study set out to answer?

The primary aim is to create a machine learning surrogate for efficient seismic response evaluation of wooden houses and interpret structural parameter significance.

March 28, 2026Open Access

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

Key Points

The primary aim is to create a machine learning surrogate for efficient seismic response evaluation of wooden houses and interpret structural parameter significance.
Developed a gradient boosting regression model using data from 289 nonlinear structural simulations.
Implemented six sensitivity analysis methods, including SHAP, Sobol, and Morris, to evaluate parameter importance.
Compared sensitivity analysis results across different methods to identify dominant structural parameters.
Achieved a high coefficient of determination (R2 = 0.90) for predicting maximum inter-story drift.
Most sensitivity methods consistently identified wall-related parameters (W1, W3, W4) as dominant factors.
Sensitivity results were robust across both elastic and nonlinear response conditions, with the Morris method showing variation in magnitude but similar parameter identification.

Abstract

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.

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