Geomechanics and Engineering

  • 제30권3호
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  • Pages.259-272
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  • 2022
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  • 2005-307X(pISSN)
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  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Decision based uncertainty model to predict rockburst in underground engineering structures using gradient boosting algorithms

  • Kidega, Richard (Department of Mining and Mineral Processing Engineering, School of Mines and Engineering, Taita Taveta University) ;
  • Ondiaka, Mary Nelima (Department of Mining and Mineral Processing Engineering, School of Mines and Engineering, Taita Taveta University) ;
  • Maina, Duncan (Department of Mining and Mineral Processing Engineering, School of Mines and Engineering, Taita Taveta University) ;
  • Jonah, Kiptanui Arap Too (Department of Mining and Mineral Processing Engineering, School of Mines and Engineering, Taita Taveta University) ;
  • Kamran, Muhammad (Department of Mining Engineering, Institute Technology of Bandung)
  • 투고 : 2022.03.31
  • 심사 : 2022.07.05
  • 발행 : 2022.08.10
⟨ 이전 논문 다음 논문 ⟩

초록

Rockburst is a dynamic, multivariate, and non-linear phenomenon that occurs in underground mining and civil engineering structures. Predicting rockburst is challenging since conventional models are not standardized. Hence, machine learning techniques would improve the prediction accuracies. This study describes decision based uncertainty models to predict rockburst in underground engineering structures using gradient boosting algorithms (GBM). The model input variables were uniaxial compressive strength (UCS), uniaxial tensile strength (UTS), maximum tangential stress (MTS), excavation depth (D), stress ratio (SR), and brittleness coefficient (BC). Several models were trained using different combinations of the input variables and a 3-fold cross-validation resampling procedure. The hyperparameters comprising learning rate, number of boosting iterations, tree depth, and number of minimum observations were tuned to attain the optimum models. The performance of the models was tested using classification accuracy, Cohen's kappa coefficient (k), sensitivity and specificity. The best-performing model showed a classification accuracy, k, sensitivity and specificity values of 98%, 93%, 1.00 and 0.957 respectively by optimizing model ROC metrics. The most and least influential input variables were MTS and BC, respectively. The partial dependence plots revealed the relationship between the changes in the input variables and model predictions. The findings reveal that GBM can be used to anticipate rockburst and guide decisions about support requirements before mining development.

키워드

과제정보

Richard Kidega, Mary Nelima Ondiaka, Duncan Maina, Kiptanui Arap Too Jonah are grateful to The German Academic Exchange Service (DAAD) In-Country/In-Region 2018/2020 scholarship, to study at Taita Taveta University Voi Kenya as a Centre of Excellence for Mining, Environmental Engineering & Resource Management (CEMEREM) Project scholar.

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