Tunnel squeezing prediction based on partially missing dataset and optimized machine learning models
Accurate prediction of tunnel squeezing, one of the common geological hazards during tunnel construction, is of great significance for ensuring construction safety and reducing economic losses. To achieve precise prediction of tunnel squeezing, this study constructed six reliable machine learning (M...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2025.1511413/full |
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| author | Peng Guan Guangzhao Ou Feng Liang Weibang Luo Qingyong Wang Chengyuan Pei Xuan Che |
| author_facet | Peng Guan Guangzhao Ou Feng Liang Weibang Luo Qingyong Wang Chengyuan Pei Xuan Che |
| author_sort | Peng Guan |
| collection | DOAJ |
| description | Accurate prediction of tunnel squeezing, one of the common geological hazards during tunnel construction, is of great significance for ensuring construction safety and reducing economic losses. To achieve precise prediction of tunnel squeezing, this study constructed six reliable machine learning (ML) classification models for this purpose, including Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), and K-Nearest Neighbors (KNN). The parameters of these 6 ML models were optimized using the Whale Optimization Algorithm (WOA) in conjunction with five-fold cross-validation. A total of 305 tunnel squeezing sample data were collected to train and test the models. KNN and Synthetic Minority Over-sampling Technique (SMOTE) methods were employed to handle the missing and imbalanced data sets. An input feature system for tunnel squeezing prediction was established, comprising tunnel burial depth (H), tunnel diameter (D), strength-to-stress ratio (SSR), and support stiffness (K). The XGBoost model optimized with WOA demonstrated the highest prediction accuracy of 0.9681. The SHAP method was utilized to interpret the XGBoost model, indicating that the contribution rank of the input features to tunnel squeezing prediction was SSR > K > D > H, with average SHAP values of 2.93, 1.49, 0.82, and 0.69, respectively. The XGBoost model was applied to predict tunnel squeezing in 10 sections of the Qinghai Huzhu Beishan Tunnel. The prediction results were highly consistent with the actual outcomes. |
| format | Article |
| id | doaj-art-6325bed7898945469164913b96e5f904 |
| institution | Kabale University |
| issn | 2296-6463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj-art-6325bed7898945469164913b96e5f9042025-01-29T06:45:47ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632025-01-011310.3389/feart.2025.15114131511413Tunnel squeezing prediction based on partially missing dataset and optimized machine learning modelsPeng Guan0Guangzhao Ou1Feng Liang2Weibang Luo3Qingyong Wang4Chengyuan Pei5Xuan Che6Faculty of Engineering, China University of Geosciences, Wuhan, ChinaSchool of Engineering Management, Hunan University of Finance and Economics, Changsha, ChinaEngineering Economics and Immigration Branch, Xinjiang Water Conservancy Development and Construction Group Co., Ltd., Urumqi, ChinaEngineering Economics and Immigration Branch, Xinjiang Water Conservancy Development and Construction Group Co., Ltd., Urumqi, ChinaXinjiang Water Conservancy Development and Construction Group Co., Ltd., Urumqi, ChinaXinjiang Water Conservancy Development and Construction Group Co., Ltd., Urumqi, ChinaFaculty of Engineering, China University of Geosciences, Wuhan, ChinaAccurate prediction of tunnel squeezing, one of the common geological hazards during tunnel construction, is of great significance for ensuring construction safety and reducing economic losses. To achieve precise prediction of tunnel squeezing, this study constructed six reliable machine learning (ML) classification models for this purpose, including Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), and K-Nearest Neighbors (KNN). The parameters of these 6 ML models were optimized using the Whale Optimization Algorithm (WOA) in conjunction with five-fold cross-validation. A total of 305 tunnel squeezing sample data were collected to train and test the models. KNN and Synthetic Minority Over-sampling Technique (SMOTE) methods were employed to handle the missing and imbalanced data sets. An input feature system for tunnel squeezing prediction was established, comprising tunnel burial depth (H), tunnel diameter (D), strength-to-stress ratio (SSR), and support stiffness (K). The XGBoost model optimized with WOA demonstrated the highest prediction accuracy of 0.9681. The SHAP method was utilized to interpret the XGBoost model, indicating that the contribution rank of the input features to tunnel squeezing prediction was SSR > K > D > H, with average SHAP values of 2.93, 1.49, 0.82, and 0.69, respectively. The XGBoost model was applied to predict tunnel squeezing in 10 sections of the Qinghai Huzhu Beishan Tunnel. The prediction results were highly consistent with the actual outcomes.https://www.frontiersin.org/articles/10.3389/feart.2025.1511413/fulltunnel squeezing predictionmachine learningwhale optimization algorithmmodel interpretationmissing dataset |
| spellingShingle | Peng Guan Guangzhao Ou Feng Liang Weibang Luo Qingyong Wang Chengyuan Pei Xuan Che Tunnel squeezing prediction based on partially missing dataset and optimized machine learning models Frontiers in Earth Science tunnel squeezing prediction machine learning whale optimization algorithm model interpretation missing dataset |
| title | Tunnel squeezing prediction based on partially missing dataset and optimized machine learning models |
| title_full | Tunnel squeezing prediction based on partially missing dataset and optimized machine learning models |
| title_fullStr | Tunnel squeezing prediction based on partially missing dataset and optimized machine learning models |
| title_full_unstemmed | Tunnel squeezing prediction based on partially missing dataset and optimized machine learning models |
| title_short | Tunnel squeezing prediction based on partially missing dataset and optimized machine learning models |
| title_sort | tunnel squeezing prediction based on partially missing dataset and optimized machine learning models |
| topic | tunnel squeezing prediction machine learning whale optimization algorithm model interpretation missing dataset |
| url | https://www.frontiersin.org/articles/10.3389/feart.2025.1511413/full |
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