Development of the Optuna-NGBoost-SHAP model for estimating ground settlement during tunnel excavation

Abstract

This study aims to develop and evaluate a natural gradient boosting (NGBoost) model optimized with Optuna for estimating ground settlement during tunnel excavation, incorporating Shapley additive explanations (SHAP) to perform interpretability analysis on the model’s estimation results. The model’s predictive performance was comprehensively assessed using datasets from two earth pressure balance shield tunneling projects in Changsha and Zhengzhou, China. Comparative analyses demonstrated the superior accuracy and generalization capability of the Optuna-NGBoost-SHAP model (training set: R² = 0.9984, MAE = 0.1004, RMSE = 0.4193, MedAE = 0.0122; validation set: R² = 0.9001, MAE = 1.3363, RMSE = 3.2992, MedAE = 0.3042; test set: R² = 0.9361, MAE = 0.9961, RMSE = 2.5388, MedAE = 0.2147). SHAP value analysis quantitatively evaluated the contributions of input features to the model’s estimations, identifying geometric factors (distance from the shield machine to the monitoring section and cover depth) as the most important features. The findings provide robust decision support for safety management during tunnel construction and demonstrate the reliability and efficiency of the Optuna-NGBoost-SHAP framework in estimating complex ground settlement scenarios.

Keywords

Ground settlement / Hyperparameter optimization / NGBoost / Estimation / Interpretable machine learning

Data availability

The data used in this research, along with related information, are available on GitHub: https://github.com/CSUcyx/Optuna-NGBoost-SHAP.git.

CRediT authorship contribution statement

Yuxin Chen: Software, Writing - original draft, Methodology, Investigation. Mohammad Hossein Kadkhodaei: Resources, Writing - review & editing, Investigation, Validation, Conceptualization. Jian Zhou: Writing - review & editing, Formal analysis, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This research is partially supported by the National Natural Science Foundation of China (Grant Nos. 42177164 and 52474121). The authors also wish to thank Dr. Zhang Pin for generously providing the database used in this study.

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