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Volume 2017 (2017), Article ID 3271246, 15 pages
Research Article

Quantitative Evaluation and Case Study of Risk Degree for Underground Goafs with Multiple Indexes considering Uncertain Factors in Mines

School of Resources and Safety Engineering, Central South University, Changsha 410083, China

Correspondence should be addressed to Longjun Dong

Received 23 June 2017; Accepted 14 September 2017; Published 16 October 2017

Academic Editor: Xiaoqing Shi

Copyright © 2017 Longjun Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The accidents caused by underground goafs are frequent and destructive due to irregular geometric shapes and complex spatial distributions, which caused severe damage to the environment and public health. Based on the theories of uncertainty measurement evaluation (WME) and analytic hierarchy process (AHP), the comprehensive risk evaluation of underground goafs was carried out using multiple indexes. Considering the hydrogeological conditions, mining status, and engineering parameters of underground goafs, the evaluation index system was established to evaluate the risk degrees considering quantified uncertain factors. The single index measurement values were solved by the semiridge measurement function. The weights for evaluation vectors were calculated through the entropy theory and AHP. Finally, the risk level was evaluated according to the credible degree recognition criterion (CDRC) and the maximum membership principle. The risk levels of 37 underground goafs in Dabaoshan mine were evaluated using 4 coupled methods. The order for underground goafs risk degrees was ranked and classified on account of the uncertainty important degree. According to the ranked order, the reasonability of 4 coupled methods was evaluated quantitatively. Results show that the UME-CDRC can be applied in the practical engineering, which provides an efficient guidance to both reduce the accident risk and improve the mining environment.