Please use this identifier to cite or link to this item:

TitleThe use of data mining techniques in rockburst risk assessment
Author(s)Sousa, Luis Ribeiro e
Miranda, Tiago F. S.
Sousa, Rita Leal e
Tinoco, Joaquim Agostinho Barbosa
Data Mining
Bayesian networks
In situ database
Issue dateJul-2017
CitationRibeiro e Sousa, L. , Miranda, T., Leal e Sousa, R. , & Tinoco, J. (2017). The Use of Data Mining Techniques in Rockburst Risk Assessment. Engineering, 3(4), 552-558.
Abstract(s)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.
Publisher version
Appears in Collections:ISISE - Artigos em Revistas Internacionais

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu Currículo DeGóis