TY - GEN
T1 - Rockburst Risk Assessment Based on Soft Computing Algorithms
AU - Tinoco, Joaquim
AU - e Sousa, Luis Ribeiro
AU - Miranda, Tiago
AU - e Sousa, Rita Leal
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - A key aspect that affect many deep underground mines over the world is the rockburst phenomenon, which can have a strong impact in terms of costs and lives. Accordingly, it is important their understanding in order to support decision makers when such events occur. One way to obtain a deeper and better understanding of the mechanisms of rockburst is through laboratory experiments. Hence, a database of rockburst laboratory tests was compiled, which was then used to develop predictive models for rockburst maximum stress and rockburst risk indexes through the application of soft computing techniques. The next step is to explore data gathered from in situ cases of rockburst. This study focusses on the analysis of such in situ information in order to build influence diagrams, enumerate the factors that interact in the occurrence of rockburst, and understand the relationships between them. In addition, the in situ rockburst data were also analyzed using different soft computing algorithms, namely artificial neural networks (ANNs). 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. One of the main observations taken from the study is that a considerable percentage of accidents occur as a result of excessive loads, generally at depths greater than 1000 m. In addition, it was also observed that soft computing algorithms can give an important contribution on determination of rockburst level, based on geologic and construction-related parameters.
AB - A key aspect that affect many deep underground mines over the world is the rockburst phenomenon, which can have a strong impact in terms of costs and lives. Accordingly, it is important their understanding in order to support decision makers when such events occur. One way to obtain a deeper and better understanding of the mechanisms of rockburst is through laboratory experiments. Hence, a database of rockburst laboratory tests was compiled, which was then used to develop predictive models for rockburst maximum stress and rockburst risk indexes through the application of soft computing techniques. The next step is to explore data gathered from in situ cases of rockburst. This study focusses on the analysis of such in situ information in order to build influence diagrams, enumerate the factors that interact in the occurrence of rockburst, and understand the relationships between them. In addition, the in situ rockburst data were also analyzed using different soft computing algorithms, namely artificial neural networks (ANNs). 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. One of the main observations taken from the study is that a considerable percentage of accidents occur as a result of excessive loads, generally at depths greater than 1000 m. In addition, it was also observed that soft computing algorithms can give an important contribution on determination of rockburst level, based on geologic and construction-related parameters.
KW - Neural networks
KW - Risk assessment
KW - Rockburst
KW - Soft computing
UR - http://www.scopus.com/inward/record.url?scp=85111167531&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-73616-3_54
DO - 10.1007/978-3-030-73616-3_54
M3 - Conference contribution
AN - SCOPUS:85111167531
SN - 9783030736156
T3 - Lecture Notes in Civil Engineering
SP - 703
EP - 714
BT - 18th International Probabilistic Workshop, IPW 2020
A2 - Matos, José C.
A2 - Lourenço, Paulo B.
A2 - Oliveira, Daniel V.
A2 - Branco, Jorge
A2 - Proske, Dirk
A2 - Silva, Rui A.
A2 - Sousa, Hélder S.
T2 - 18th International Probabilistic Workshop, IPW 2020
Y2 - 12 May 2021 through 14 May 2021
ER -