TY - JOUR
T1 - The Use of Data Mining Techniques in Rockburst Risk Assessment
AU - Ribeiro e Sousa, Luis
AU - Miranda, Tiago
AU - Leal e Sousa, Rita
AU - Tinoco, Joaquim
N1 - Publisher Copyright:
© 2017 THE AUTHORS
PY - 2017/8/1
Y1 - 2017/8/1
N2 - 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.
AB - 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.
KW - Bayesian networks
KW - Data mining
KW - In situ database
KW - Rockburst
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U2 - 10.1016/J.ENG.2017.04.002
DO - 10.1016/J.ENG.2017.04.002
M3 - Article
AN - SCOPUS:85036672371
SN - 2095-8099
VL - 3
SP - 552
EP - 558
JO - Engineering
JF - Engineering
IS - 4
ER -