AI-aided short-term decision making of rockburst damage scale in underground engineering
Rockbursts pose severe risks to underground engineering projects, including mining and tunnelling, where sudden rock failures can lead to substantial infrastructure damage and loss of human lives. An accurate assessment of rockburst damage is essential for safety and effective risk mitigation. This...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2025-08-01
|
| Series: | Underground Space |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2467967425000418 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850070474505060352 |
|---|---|
| author | Chukwuemeka Daniel Shouye Cheng Xin Yin Zakaria Mohamed Barrie Yucong Pan Quansheng Liu Feng Gao Minsheng Li Xing Huang |
| author_facet | Chukwuemeka Daniel Shouye Cheng Xin Yin Zakaria Mohamed Barrie Yucong Pan Quansheng Liu Feng Gao Minsheng Li Xing Huang |
| author_sort | Chukwuemeka Daniel |
| collection | DOAJ |
| description | Rockbursts pose severe risks to underground engineering projects, including mining and tunnelling, where sudden rock failures can lead to substantial infrastructure damage and loss of human lives. An accurate assessment of rockburst damage is essential for safety and effective risk mitigation. This study investigates the effectiveness of ensemble machine learning models optimized through Bayesian optimization (BO) in predicting rockburst damage scales. Nine classifier algorithms, including random forest (RF), were evaluated using a dataset of 254 samples. The research considered factors such as stress conditions, support system capacity, excavation span, geological characteristics, seismic magnitude, peak particle velocity, and rock density as input variables. The rockburst damage scale, categorized into four severity levels based on displaced rock mass, served as the target variable. Among the models evaluated, BO-RF model demonstrated the highest predictive accuracy and generalization capability, achieving 92% testing accuracy. BO-RF model also ranked top in a multi-criteria evaluation framework. This devised ranking system underscores the importance of evaluating model performance on both training and unseen testing data to ensure robust generalization. The findings underscore the effectiveness of BO-RF in enhancing rockburst risk assessment and providing reliable predictive insights for underground engineering applications. |
| format | Article |
| id | doaj-art-0ab7e7d8b5de41258d117f824f37ed1b |
| institution | DOAJ |
| issn | 2467-9674 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Underground Space |
| spelling | doaj-art-0ab7e7d8b5de41258d117f824f37ed1b2025-08-20T02:47:32ZengKeAi Communications Co., Ltd.Underground Space2467-96742025-08-012336237810.1016/j.undsp.2025.02.005AI-aided short-term decision making of rockburst damage scale in underground engineeringChukwuemeka Daniel0Shouye Cheng1Xin Yin2Zakaria Mohamed Barrie3Yucong Pan4Quansheng Liu5Feng Gao6Minsheng Li7Xing Huang8School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China; Department of Civil Engineering, Pan African University Institute for Basic Sciences, Technology and Innovation, Jomo Kenyatta University of Agriculture and Technology, Nairobi P.O. Box 62000-00200, KenyaResearch Institute of Mine Construction, Tiandi Science and Technology Company Limited, Beijing 100013, China; State Key Laboratory of Intelligent Coal Mining and Strata Control, Beijing 100013, ChinaSchool of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China; School of Civil Engineering, Wuhan University, Wuhan 430072, China; Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong 999077, China; Corresponding author at: School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China.Department of Civil Engineering, Pan African University Institute for Basic Sciences, Technology and Innovation, Jomo Kenyatta University of Agriculture and Technology, Nairobi P.O. Box 62000-00200, KenyaSchool of Civil Engineering, Wuhan University, Wuhan 430072, ChinaSchool of Civil Engineering, Wuhan University, Wuhan 430072, ChinaResearch Institute of Mine Construction, Tiandi Science and Technology Company Limited, Beijing 100013, China; State Key Laboratory of Intelligent Coal Mining and Strata Control, Beijing 100013, ChinaResearch Institute of Mine Construction, Tiandi Science and Technology Company Limited, Beijing 100013, China; State Key Laboratory of Intelligent Coal Mining and Strata Control, Beijing 100013, ChinaState Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, ChinaRockbursts pose severe risks to underground engineering projects, including mining and tunnelling, where sudden rock failures can lead to substantial infrastructure damage and loss of human lives. An accurate assessment of rockburst damage is essential for safety and effective risk mitigation. This study investigates the effectiveness of ensemble machine learning models optimized through Bayesian optimization (BO) in predicting rockburst damage scales. Nine classifier algorithms, including random forest (RF), were evaluated using a dataset of 254 samples. The research considered factors such as stress conditions, support system capacity, excavation span, geological characteristics, seismic magnitude, peak particle velocity, and rock density as input variables. The rockburst damage scale, categorized into four severity levels based on displaced rock mass, served as the target variable. Among the models evaluated, BO-RF model demonstrated the highest predictive accuracy and generalization capability, achieving 92% testing accuracy. BO-RF model also ranked top in a multi-criteria evaluation framework. This devised ranking system underscores the importance of evaluating model performance on both training and unseen testing data to ensure robust generalization. The findings underscore the effectiveness of BO-RF in enhancing rockburst risk assessment and providing reliable predictive insights for underground engineering applications.http://www.sciencedirect.com/science/article/pii/S2467967425000418Underground engineeringRockburst damage scaleShort-term decision makingEnsemble learningBayesian optimization |
| spellingShingle | Chukwuemeka Daniel Shouye Cheng Xin Yin Zakaria Mohamed Barrie Yucong Pan Quansheng Liu Feng Gao Minsheng Li Xing Huang AI-aided short-term decision making of rockburst damage scale in underground engineering Underground Space Underground engineering Rockburst damage scale Short-term decision making Ensemble learning Bayesian optimization |
| title | AI-aided short-term decision making of rockburst damage scale in underground engineering |
| title_full | AI-aided short-term decision making of rockburst damage scale in underground engineering |
| title_fullStr | AI-aided short-term decision making of rockburst damage scale in underground engineering |
| title_full_unstemmed | AI-aided short-term decision making of rockburst damage scale in underground engineering |
| title_short | AI-aided short-term decision making of rockburst damage scale in underground engineering |
| title_sort | ai aided short term decision making of rockburst damage scale in underground engineering |
| topic | Underground engineering Rockburst damage scale Short-term decision making Ensemble learning Bayesian optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2467967425000418 |
| work_keys_str_mv | AT chukwuemekadaniel aiaidedshorttermdecisionmakingofrockburstdamagescaleinundergroundengineering AT shouyecheng aiaidedshorttermdecisionmakingofrockburstdamagescaleinundergroundengineering AT xinyin aiaidedshorttermdecisionmakingofrockburstdamagescaleinundergroundengineering AT zakariamohamedbarrie aiaidedshorttermdecisionmakingofrockburstdamagescaleinundergroundengineering AT yucongpan aiaidedshorttermdecisionmakingofrockburstdamagescaleinundergroundengineering AT quanshengliu aiaidedshorttermdecisionmakingofrockburstdamagescaleinundergroundengineering AT fenggao aiaidedshorttermdecisionmakingofrockburstdamagescaleinundergroundengineering AT minshengli aiaidedshorttermdecisionmakingofrockburstdamagescaleinundergroundengineering AT xinghuang aiaidedshorttermdecisionmakingofrockburstdamagescaleinundergroundengineering |