Rockburst Risk Assessment Based on Soft Computing Algorithms

Joaquim Tinoco, Luis Ribeiro e Sousa, Tiago Miranda, Rita Leal e Sousa

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publication18th International Probabilistic Workshop, IPW 2020
EditorsJosé C. Matos, Paulo B. Lourenço, Daniel V. Oliveira, Jorge Branco, Dirk Proske, Rui A. Silva, Hélder S. Sousa
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783030736156
StatePublished - 2021
Event18th International Probabilistic Workshop, IPW 2020 - Virtual, Online
Duration: May 12 2021May 14 2021

Publication series

NameLecture Notes in Civil Engineering
Volume153 LNCE
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565


Conference18th International Probabilistic Workshop, IPW 2020
CityVirtual, Online


  • Neural networks
  • Risk assessment
  • Rockburst
  • Soft computing

ASJC Scopus subject areas

  • Civil and Structural Engineering


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