Zobraziť v EDS

AI-aided short-term decision making of rockburst damage scale in underground engineering

Uložené v:
Podrobná bibliografia
Názov: AI-aided short-term decision making of rockburst damage scale in underground engineering
Autori: Chukwuemeka Daniel, Shouye Cheng, Xin Yin, Zakaria Mohamed Barrie, Yucong Pan, Quansheng Liu, Feng Gao, Minsheng Li, Xing Huang
Zdroj: Underground Space, Vol 23, Iss, Pp 362-378 (2025)
Informácie o vydavateľovi: Elsevier BV, 2025.
Rok vydania: 2025
Predmety: Rockburst damage scale, Ensemble learning, Short-term decision making, TA703-712, Engineering geology. Rock mechanics. Soil mechanics. Underground construction, Underground engineering, Bayesian optimization
Popis: 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.
Druh dokumentu: Article
Jazyk: English
ISSN: 2467-9674
DOI: 10.1016/j.undsp.2025.02.005
Prístupová URL adresa: https://doaj.org/article/0ab7e7d8b5de41258d117f824f37ed1b
Rights: CC BY NC ND
Prístupové číslo: edsair.doi.dedup.....d920593175fb8de276eac5880df3665a
Databáza: OpenAIRE
Popis
Abstrakt: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.
ISSN:24679674
DOI:10.1016/j.undsp.2025.02.005