Evaluation of rockburst occurrence and intensity in underground structures using decision tree approach

Ebrahim Ghasemi, Hasan Gholizadeh, Amoussou Coffi Adoko

Research output: Contribution to journalArticle

Abstract

Based on reported statistics, rockburst phenomenon is the main cause of many casualties and accidents occurred during the construction of deep underground structures. Therefore, its prediction in initial stages of design has a remarkable role on enhancement of safety. In this paper, two models have been developed for rockburst evaluation using the C5.0 decision tree classifier. The first model has been applied for prediction of rockburst occurrence and the second model for prediction of rockburst intensity. These models have been developed based on a database including 174 rockburst case histories. In both models, stress coefficient, rock brittleness coefficient, and the elastic strain energy index are the predictive variables. These models are easy to use and do not require extensive knowledge. Based on decision rules derived from these models, the rockburst occurrence and intensity can be evaluated easily. The results revealed that the proposed approach is a useful and robust technique for long-term prediction of rockburst.

Original languageEnglish
JournalEngineering with Computers
DOIs
Publication statusAccepted/In press - Jan 1 2019

Fingerprint

Rock bursts
Underground structures
Decision trees
Decision tree
Evaluation
Prediction
Model
Strain Energy
Coefficient
Decision Rules
Brittleness
Strain energy
Accidents
Classifiers
Enhancement
Safety
Classifier
Rocks
Statistics

Keywords

  • C5.0 classifier
  • Decision tree
  • Long-term prediction
  • Rockburst
  • Underground structures

ASJC Scopus subject areas

  • Software
  • Modelling and Simulation
  • Engineering(all)
  • Computer Science Applications

Cite this

Evaluation of rockburst occurrence and intensity in underground structures using decision tree approach. / Ghasemi, Ebrahim; Gholizadeh, Hasan; Adoko, Amoussou Coffi.

In: Engineering with Computers, 01.01.2019.

Research output: Contribution to journalArticle

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