Rockburst laboratory tests database - Application of data mining techniques


Autoria(s): He Manchao; Sousa, Luís Ribeiro e; Miranda, Tiago F. S.; Zhu Gualong
Data(s)

2015

Resumo

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.

State Key Laboratory for Geomechanics and Deep Underground Engineering (Beijing) and China University of Mining and Technology

State National Basic Research Program of China (973 Project no. 2010CB226800)

Risk Assessment Activities Applied to Slope Stability, Rockburst and Soft Rocks at the State Key Laboratory for Geomechanics and Deep Underground Engineering (Beijing) of China University of Mining and Technology.

Identificador

He, 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

0013-7952

http://hdl.handle.net/1822/38493

10.1016/j.enggeo.2014.12.008

Idioma(s)

por

Publicador

Elsevier

Direitos

info:eu-repo/semantics/restrictedAccess

Palavras-Chave #Rockburst #Experimental tests #Data mining #Rockburst index
Tipo

info:eu-repo/semantics/article