- Title
- Prediction of rockburst using supervised machine learning
- Creator
- Kishore, Tharun; Khandelwal, Manoj
- Date
- 2022
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/189060
- Identifier
- vital:17374
- Identifier
-
https://doi.org/10.1007/978-981-16-9770-8_7
- Identifier
- ISBN:2366-2557 (ISSN); 9789811697692 (ISBN)
- Abstract
- The rockburst is one of the serious mining hazards, that cause injury, death, damage to mining equipment, and leads to financial problems in mining constructions. At the same time, mining is the major and essential resource of mineral commodities, that all mining locations uncover important for sustaining and enhancing their needs of dwelling. Moreover, mining contributes a significant portion of the GDP (Gross Domestic Production) growth of nations such as China, Australia, Russia, and the USA. These countries play major roles in exporting mineral commodities, which has been creating a huge demand to find out conventional methods for predicting the rockburst occurrence during mining. Moreover, determining rockburst occurrence using formulation or machine-based equipment has not been ideal in showcasing the result, due to changing geological parameters and the mining environment. In recent years, various soft computing and machine learning tools have been developed significantly, which help us to improve predicting models more accurate. In this research, original Rockburst data set have been collected in the last 20 years. Then standard empirical formulation method which is currently used in mining and supervised machine learning models has been used to predict rockburst occurrence. Finally, an interacting machine learning model developed in a way to predict the Rockburst occurrence result in three Indicators (high, medium and low Rockburst) are presented. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Relation
- International Conference on Geotechnical challenges in Mining, Tunneling and Underground structures, ICGMTU 2021, Virtual, online, 20-21 December 2021, Lecture Notes in Civil Engineering Vol. 228, p. 133-154
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2022, The Author(s),
- Subject
- Random-forest classifier; Rockburst prediction; Supervised machine learning
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