Development of the Optuna-NGBoost-SHAP model for estimating ground settlement during tunnel excavation
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...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-10-01
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| Series: | Underground Space |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2467967425000595 |
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| author | Yuxin Chen Mohammad Hossein Kadkhodaei Jian Zhou |
| author_facet | Yuxin Chen Mohammad Hossein Kadkhodaei Jian Zhou |
| author_sort | Yuxin Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-36bd378bb5e244cd846569f2cb5532d9 |
| institution | DOAJ |
| issn | 2467-9674 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Underground Space |
| spelling | doaj-art-36bd378bb5e244cd846569f2cb5532d92025-08-20T03:13:59ZengKeAi Communications Co., Ltd.Underground Space2467-96742025-10-0124607810.1016/j.undsp.2025.03.006Development of the Optuna-NGBoost-SHAP model for estimating ground settlement during tunnel excavationYuxin Chen0Mohammad Hossein Kadkhodaei1Jian Zhou2School of Resources and Safety Engineering, Central South University, Changsha 410083, ChinaIsfahan University of Technology, Department of Mining Engineering, Isfahan 8415683111, Iran; Corresponding author.School of Resources and Safety Engineering, Central South University, Changsha 410083, ChinaThis 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.http://www.sciencedirect.com/science/article/pii/S2467967425000595Ground settlementHyperparameter optimizationNGBoostEstimationInterpretable machine learning |
| spellingShingle | Yuxin Chen Mohammad Hossein Kadkhodaei Jian Zhou Development of the Optuna-NGBoost-SHAP model for estimating ground settlement during tunnel excavation Underground Space Ground settlement Hyperparameter optimization NGBoost Estimation Interpretable machine learning |
| title | Development of the Optuna-NGBoost-SHAP model for estimating ground settlement during tunnel excavation |
| title_full | Development of the Optuna-NGBoost-SHAP model for estimating ground settlement during tunnel excavation |
| title_fullStr | Development of the Optuna-NGBoost-SHAP model for estimating ground settlement during tunnel excavation |
| title_full_unstemmed | Development of the Optuna-NGBoost-SHAP model for estimating ground settlement during tunnel excavation |
| title_short | Development of the Optuna-NGBoost-SHAP model for estimating ground settlement during tunnel excavation |
| title_sort | development of the optuna ngboost shap model for estimating ground settlement during tunnel excavation |
| topic | Ground settlement Hyperparameter optimization NGBoost Estimation Interpretable machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2467967425000595 |
| work_keys_str_mv | AT yuxinchen developmentoftheoptunangboostshapmodelforestimatinggroundsettlementduringtunnelexcavation AT mohammadhosseinkadkhodaei developmentoftheoptunangboostshapmodelforestimatinggroundsettlementduringtunnelexcavation AT jianzhou developmentoftheoptunangboostshapmodelforestimatinggroundsettlementduringtunnelexcavation |