Shi-ming, Wang and Jian, Zhou and Chuan-qi, Li and Armaghani, Danial Jahed and Xi-bing, Li and Mitri, Hani S. (2021) Rockburst prediction in hard rock mines developing bagging and boosting tree-based ensemble techniques. Journal of Central South University, 28 (2). pp. 527-542. ISSN 2095-2899, DOI https://doi.org/10.1007/s11771-021-4619-8.
Full text not available from this repository.Abstract
Rockburst prediction is of vital significance to the design and construction of underground hard rock mines. A rockburst database consisting of 102 case histories, i.e., 1998-2011 period data from 14 hard rock mines was examined for rockburst prediction in burst-prone mines by three tree-based ensemble methods. The dataset was examined with six widely accepted indices which are: the maximum tangential stress around the excavation boundary (MTS), uniaxial compressive strength (UCS) and uniaxial tensile strength (UTS) of the intact rock, stress concentration factor (SCF), rock brittleness index (BI), and strain energy storage index (EEI). Two boosting (AdaBoost.M1, SAMME) and bagging algorithms with classification trees as baseline classifier on ability to learn rockburst were evaluated. The available dataset was randomly divided into training set (2/3 of whole datasets) and testing set (the remaining datasets). Repeated 10-fold cross validation (CV) was applied as the validation method for tuning the hyper-parameters. The margin analysis and the variable relative importance were employed to analyze some characteristics of the ensembles. According to 10-fold CV, the accuracy analysis of rockburst dataset demonstrated that the best prediction method for the potential of rockburst is bagging when compared to AdaBoost.M1, SAMME algorithms and empirical criteria methods.
| Item Type: | Article |
|---|---|
| Funders: | National Natural Science Foundation of China (NSFC) [41807259] [51604109], Innovation-Driven Project of Central South University, China [2020CX040], Natural Science Foundation of Hunan Province [2018JJ3693] |
| Uncontrolled Keywords: | Rockburst; Hard rock; Prediction; Bagging; Boosting; Ensemble learning |
| Subjects: | T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 15 Aug 2022 06:16 |
| Last Modified: | 15 Aug 2022 06:16 |
| URI: | http://eprints.um.edu.my/id/eprint/28535 |
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