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Engineering    2017, Vol. 3 Issue (4) : 552 -558     https://doi.org/10.1016/J.ENG.2017.04.002
Research |
The Use of Data Mining Techniques in Rockburst Risk Assessment
Luis Ribeiro e Sousa1(),Tiago Miranda2,Rita Leal e Sousa3,Joaquim Tinoco2
1. State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
2. Institute for Sustainability and Innovation in Structural Engineering, Department of Civil Engineering, University of Minho, Campus de Azurém, Guimarães 4800-058, Portugal
3. Masdar Institute of Science and Technology, Masdar City, Abu Dhabi, UAE
Abstract
Abstract  

Rockburst is an important phenomenon that has affected many deep underground mines around the world. An understanding of this phenomenon is relevant to the management of such events, which can lead to saving both costs and lives. Laboratory experiments are one way to obtain a deeper and better understanding of the mechanisms of rockburst. In a previous study by these authors, a database of rockburst laboratory tests was created; in addition, with the use of data mining (DM) techniques, models to predict rockburst maximum stress and rockburst risk indexes were developed. In this paper, we focus on the analysis of a database of in situ cases of rockburst in order to build influence diagrams, list the factors that interact in the occurrence of rockburst, and understand the relationships between these factors. The in situ rockburst database was further analyzed using different DM techniques ranging from artificial neural networks (ANNs) to naive Bayesian classifiers. The aim was to predict the type of rockburst—that is, the rockburst level—based on geologic and construction characteristics of the mine or tunnel. Conclusions are drawn at the end of the paper.

Keywords Rockburst      Data mining      Bayesian networks      In situ database     
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Corresponding Authors: Luis Ribeiro e Sousa   
Online First Date: 21 August 2017    Issue Date: 13 September 2017
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Luis Ribeiro e Sousa
Tiago Miranda
Rita Leal e Sousa
Joaquim Tinoco
Cite this article:   
Luis Ribeiro e Sousa,Tiago Miranda,Rita Leal e Sousa, et al. The Use of Data Mining Techniques in Rockburst Risk Assessment[J]. Engineering, 2017, 3(4): 552 -558 .
URL:  
http://engineering.org.cn/EN/10.1016/J.ENG.2017.04.002     OR     http://engineering.org.cn/EN/Y2017/V3/I4/552
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