Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/38493

TitleRockburst laboratory tests database - Application of data mining techniques
Author(s)He Manchao
Sousa, Luís Ribeiro e
Miranda, Tiago F. S.
Zhu Gualong
KeywordsRockburst
Experimental tests
Data mining
Rockburst index
Issue date2015
PublisherElsevier
JournalEngineering Geology
CitationHe, M., e Sousa, L. R., Miranda, T., & Zhu, G. (2015). Rockburst laboratory tests database - Application of data mining techniques. Engineering Geology, 185, 116-130. doi: 10.1016/j.enggeo.2014.12.008
Abstract(s)Rockburst is characterized by a violent explosion of a block causing a sudden rupture in the rock and is quite common in deep tunnels. It is critical to understand the phenomenon of rockburst, focusing on the patterns of occurrence so these events can be avoided and/or managed saving costs and possibly lives. The failure mechanism of rockburst needs to be better understood. Laboratory experiments are undergoing at the Laboratory for Geomechanics and Deep Underground Engineering (SKLGDUE) of Beijing and the system is described. A large number of rockburst tests were performed and their information collected, stored in a database and analyzed. Data Mining (DM) techniques were applied to the database in order to develop predictive models for the rockburst maximum stress (σRB) and rockburst risk index (IRB) that need the results of such tests to be determined. With the developed models it is possible to predict these parameters with high accuracy levels using data from the rock mass and specific project.
TypeArticle
URIhttp://hdl.handle.net/1822/38493
DOI10.1016/j.enggeo.2014.12.008
ISSN0013-7952
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:ISISE - Artigos em Revistas Internacionais

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