Optimizing overbreak prediction based on geological parameters comparing multiple regression analysis and artificial neural network

Hyongdoo Jang, Erkan Topal

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)

Abstract

Underground mining becomes more efficient due to the technological advancements of drilling and blasting methods and the developing of highly productive mining methods that facilitate easier access to ore. In the perspective of maximizing productivity in underground mining by drilling and blasting methods, overbreak control is an essential component. The causing factors of overbreak can simply divided as blasting and geological parameters and all of the factors are nonlinearly correlated. In this paper, the blasting design of the tunnel was fixed as the standard blasting pattern and the research focus on effects of geological parameters to the overbreak phenomenon. 49 sets of rock mass rating (RMR) and overbreak data were applied to linear and nonlinear multiple regression analysis (LMRA and NMRA) and artificial neural network (ANN) to predict overbreak as input and output parameters, respectively. The performance of LMRA, NMRA, and optimized ANN models was evaluated by comparing coefficient correlations (R2) and their values are 0.694, 0.704 and 0.945, respectively, which means that the relatively high level of accuracy of the optimized ANN in comparison with LMRA and NMRA. The developed optimum overbreak predicting ANN model is suitable for establishing an overbreak warning and preventing system and it will utilize as a foundation reference for a practical drift blasting reconciliation at mines for operation improvements.

Original languageEnglish
Pages (from-to)161-169
Number of pages9
JournalTunnelling and Underground Space Technology
Volume38
DOIs
Publication statusPublished - Sep 1 2013

Keywords

  • Artificial neural network
  • Blasting
  • Multiple regression analysis
  • Overbreak
  • Underground mine

ASJC Scopus subject areas

  • Building and Construction
  • Geotechnical Engineering and Engineering Geology

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