Data-Driven Approach for Intelligent Classification of Tunnel Surrounding Rock Using Integrated Fractal and Machine Learning Methods
The degree of rock mass discontinuity is crucial for evaluating surrounding rock quality, yet its accurate and rapid measurement at construction sites remains challenging. This study utilizes fractal dimension to characterize the geometric characteristics of rock mass discontinuity and develops a da...
| Main Authors: | , , , , , , , , |
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| Format: | Journal Article |
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MDPI
2024
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| Online Access: | http://hdl.handle.net/20.500.11937/96385 |
| _version_ | 1848766145400668160 |
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| author | Ma, Junjie Li, Tianbin Shirani Faradonbeh, Roohollah Sharifzadeh, Mostafa Wang, Jianfeng Huang, Yuyang Materials, Chunchi Peng, Feng Zhang, Hang |
| author_facet | Ma, Junjie Li, Tianbin Shirani Faradonbeh, Roohollah Sharifzadeh, Mostafa Wang, Jianfeng Huang, Yuyang Materials, Chunchi Peng, Feng Zhang, Hang |
| author_sort | Ma, Junjie |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The degree of rock mass discontinuity is crucial for evaluating surrounding rock quality, yet its accurate and rapid measurement at construction sites remains challenging. This study utilizes fractal dimension to characterize the geometric characteristics of rock mass discontinuity and develops a data-driven surrounding rock classification (SRC) model integrating machine learning algorithms. Initially, the box-counting method was introduced to calculate the fractal dimension of discontinuity from the excavation face image. Subsequently, crucial parameters affecting surrounding rock quality were analyzed and selected, including rock strength, the fractal dimension of discontinuity, the discontinuity condition, the in-situ stress condition, the groundwater condition, and excavation orientation. This study compiled a database containing 246 railway and highway tunnel cases based on these parameters. Then, four SRC models were constructed, integrating Bayesian optimization (BO) with support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) algorithms. Evaluation indicators, including 5-fold cross-validation, precision, recall, F1-score, micro-F1-score, macro-F1-score, accuracy, and the receiver operating characteristic curve, demonstrated the GBDT-BO model’s superior robustness in learning and generalization compared to other models. Furthermore, four additional excavation face cases validated the intelligent SRC approach’s practicality. Finally, the synthetic minority over-sampling technique was employed to balance the training set. Subsequent retraining and evaluation confirmed that the imbalanced dataset does not adversely affect SRC model performance. The proposed GBDT-BO model shows promise for predicting surrounding rock quality and guiding dynamic tunnel excavation and support. |
| first_indexed | 2025-11-14T11:46:29Z |
| format | Journal Article |
| id | curtin-20.500.11937-96385 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:46:29Z |
| publishDate | 2024 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-963852025-01-09T06:03:09Z Data-Driven Approach for Intelligent Classification of Tunnel Surrounding Rock Using Integrated Fractal and Machine Learning Methods Ma, Junjie Li, Tianbin Shirani Faradonbeh, Roohollah Sharifzadeh, Mostafa Wang, Jianfeng Huang, Yuyang Materials, Chunchi Peng, Feng Zhang, Hang The degree of rock mass discontinuity is crucial for evaluating surrounding rock quality, yet its accurate and rapid measurement at construction sites remains challenging. This study utilizes fractal dimension to characterize the geometric characteristics of rock mass discontinuity and develops a data-driven surrounding rock classification (SRC) model integrating machine learning algorithms. Initially, the box-counting method was introduced to calculate the fractal dimension of discontinuity from the excavation face image. Subsequently, crucial parameters affecting surrounding rock quality were analyzed and selected, including rock strength, the fractal dimension of discontinuity, the discontinuity condition, the in-situ stress condition, the groundwater condition, and excavation orientation. This study compiled a database containing 246 railway and highway tunnel cases based on these parameters. Then, four SRC models were constructed, integrating Bayesian optimization (BO) with support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) algorithms. Evaluation indicators, including 5-fold cross-validation, precision, recall, F1-score, micro-F1-score, macro-F1-score, accuracy, and the receiver operating characteristic curve, demonstrated the GBDT-BO model’s superior robustness in learning and generalization compared to other models. Furthermore, four additional excavation face cases validated the intelligent SRC approach’s practicality. Finally, the synthetic minority over-sampling technique was employed to balance the training set. Subsequent retraining and evaluation confirmed that the imbalanced dataset does not adversely affect SRC model performance. The proposed GBDT-BO model shows promise for predicting surrounding rock quality and guiding dynamic tunnel excavation and support. 2024 Journal Article http://hdl.handle.net/20.500.11937/96385 10.3390/fractalfract8120677 http://creativecommons.org/licenses/by/4.0/ MDPI fulltext |
| spellingShingle | Ma, Junjie Li, Tianbin Shirani Faradonbeh, Roohollah Sharifzadeh, Mostafa Wang, Jianfeng Huang, Yuyang Materials, Chunchi Peng, Feng Zhang, Hang Data-Driven Approach for Intelligent Classification of Tunnel Surrounding Rock Using Integrated Fractal and Machine Learning Methods |
| title | Data-Driven Approach for Intelligent Classification of Tunnel Surrounding Rock Using Integrated Fractal and Machine Learning Methods |
| title_full | Data-Driven Approach for Intelligent Classification of Tunnel Surrounding Rock Using Integrated Fractal and Machine Learning Methods |
| title_fullStr | Data-Driven Approach for Intelligent Classification of Tunnel Surrounding Rock Using Integrated Fractal and Machine Learning Methods |
| title_full_unstemmed | Data-Driven Approach for Intelligent Classification of Tunnel Surrounding Rock Using Integrated Fractal and Machine Learning Methods |
| title_short | Data-Driven Approach for Intelligent Classification of Tunnel Surrounding Rock Using Integrated Fractal and Machine Learning Methods |
| title_sort | data-driven approach for intelligent classification of tunnel surrounding rock using integrated fractal and machine learning methods |
| url | http://hdl.handle.net/20.500.11937/96385 |