Journal of
Geology and Mining Research

  • Abbreviation: J. Geol. Min. Res.
  • Language: English
  • ISSN: 2006-9766
  • DOI: 10.5897/JGMR
  • Start Year: 2009
  • Published Articles: 172

Full Length Research Paper

Tunnels stability analysis using binary and multinomial logistic regression (LR)

R. Rafiee
  • R. Rafiee
  • Department of Mining, Oil and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran.
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M. Ataei
  • M. Ataei
  • Department of Mining, Oil and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran.
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M. Kamali
  • M. Kamali
  • Department of Mining, Oil and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran.
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  •  Accepted: 29 April 2013
  •  Published: 30 April 2013

Abstract

 

One of the most serious problems in tunneling projects are falling rock blocks. By considering this fact, the importance of stability predicting using some input parameters can be obviously understood. Among the existing rock mass classification systems for underground structures, rock mass rating (RMR) and Q are probably the most widely used ones this is rather unlikely to change, at least in the near future, frequently used and more available in tunneling projects, therefore establishing a proper and valid stability method based on such items would be useful. Since none of them (RMR and Q) can reflect the tunnel stability condition entirely and each has some lacks in rock mass properties defining, therefore both of them were used in this analysis which can provide the whole perspective of rock mass condition and stability. For this aim, data (RMR, Q, and hydraulic radius) from 104 cases of eight tunnel projects were gathered. By observing the stability condition in each tunnel, the data were classified in three categories: stable, potentially unstable and unstable. Two models next were defined and the related formulas were found using binary and multinomial logistic regression, at last the best predictor model would be selected by using the percent of correctly predicted cases in each model. The results of this paper show that the logistic regression (LR) is a robust tool to establish and develop predicting model for tunneling projects and can assist engineers to predict the stability condition of tunnels.

 

Key words: Logistic regression, tunneling, rock mass classification, stability.