- Title
- Rock-burst occurrence prediction based on optimized naïve bayes models
- Creator
- Ke, Bo; Khandelwal, Manoj; Asteris, Panagiotis; Skentou, Athanasia; Mamou, Anna; Armaghani, Danial
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/178773
- Identifier
- vital:15470
- Identifier
-
https://doi.org/10.1109/ACCESS.2021.3089205
- Identifier
- ISBN:2169-3536 (ISSN)
- Abstract
- Rock-burst is a common failure in hard rock related projects in civil and mining construction and therefore, proper classification and prediction of this phenomenon is of interest. This research presents the development of optimized naïve Bayes models, in predicting rock-burst failures in underground projects. The naïve Bayes models were optimized using four weight optimization techniques including forward, backward, particle swarm optimization, and evolutionary. An evolutionary random forest model was developed to identify the most significant input parameters. The maximum tangential stress, elastic energy index, and uniaxial tensile stress were then selected by the feature selection technique (i.e., evolutionary random forest) to develop the optimized naïve Bayes models. The performance of the models was assessed using various criteria as well as a simple ranking system. The results of this research showed that particle swarm optimization was the most effective technique in improving the accuracy of the naïve Bayes model for rock-burst prediction (cumulative ranking = 21), while the backward technique was the worst weight optimization technique (cumulative ranking = 11). All the optimized naïve Bayes models identified the maximum tangential stress as the most significant parameter in predicting rock-burst failures. The results of this research demonstrate that particle swarm optimization technique may improve the accuracy of naïve Bayes algorithms in predicting rock-burst occurrence. © 2013 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Access Vol. 9, no. (2021), p. 91347-91360
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2013 IEEE
- Rights
- Open Access
- Subject
- 08 Information and Computing Sciences; 09 Engineering; 10 Technology; Evolutionary random forest; Naïve Bayes algorithm; Particle swarm optimization; Rock-burst occurrence; Weight optimization
- Full Text
- Reviewed
- Funder
- The authors would like to acknowledge financial support for the dissemination of this work from the Special Account for Research of School of Pedagogical and Technological
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