Interpretable CEEMDAN-SMA-LSSVM hybrid model for predicting shield tunnel-induced settlement

Accurate and interpretable prediction of shield tunnel-induced settlement poses a significant challenge due to the complex interplay of various influencing factors. This paper proposes a novel interpretable hybrid model that combines complete ensemble empirical mode decomposition with adaptive noise...

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Bibliographic Details
Published in:	Journal of Rock Mechanics and Geotechnical Engineering Vol. 17; no. 10; pp. 6179 - 6194
Main Authors:	Meng, Shaoqiang, Shi, Zhenming, Gutierrez, Marte
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.10.2025
Subjects:	Geotechnical engineering Machine learning Shapley additive explanations (SHAP) Swarm intelligence optimization algorithm Geotechnical engineering Swarm intelligence optimization algorithm Shapley additive explanations (SHAP) Machine learning
ISSN:	1674-7755
Online Access:	Get full text
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Summary:	Accurate and interpretable prediction of shield tunnel-induced settlement poses a significant challenge due to the complex interplay of various influencing factors. This paper proposes a novel interpretable hybrid model that combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), slime mold algorithm (SMA), and least squares support vector machine (LSSVM) to enhance prediction accuracy and model transparency. The CEEMDAN method, optimized by SMA, decomposes settlement data into intrinsic mode functions (IMFs) and residuals, thereby reducing data noise. The LSSVM, also optimized by SMA, is then applied to predict each IMF and residual. The final settlement prediction is derived from the aggregation of these results. The model was rigorously validated using the Changsha (China) and Singapore Metro projects, demonstrating superior performance to traditional machine learning models. The evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2), underscore the model's effectiveness. The model achieved the lowest error rates and highest accuracy across these metrics. Notably, Shapley additive explanations (SHAP) provided insights into the model's decision-making process, identifying shield stoppage and moisture content as the most influential factors in settlement prediction. This study contributes to the advancement of the methodological framework for predicting tunnel settlement. It addresses the discrepancy between prediction accuracy and interpretability, providing a robust tool for practical engineering applications.
ISSN:	1674-7755
DOI:	10.1016/j.jrmge.2024.11.062