Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 2947628, 11 pages
http://dx.doi.org/10.1155/2016/2947628
Research Article

Uncertainty Analysis on Risk Assessment of Water Inrush in Karst Tunnels

1State Key Laboratory of Disaster Prevention & Mitigation of Explosion & Impact, College of National Defense Engineering, PLA University of Science and Technology, Nanjing 210007, China
2High-Tech Institute, Fan Gong-ting South Street on the 12th, Qing Zhou, Shan Dong, China
3State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology, Xuzhou 221116, China

Received 26 June 2016; Accepted 27 October 2016

Academic Editor: David González

Copyright © 2016 Yiqing Hao 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.

Abstract

An improved attribute recognition method is reviewed and discussed to evaluate the risk of water inrush in karst tunnels. Due to the complex geology and hydrogeology, the methodology discusses the uncertainties related to the evaluation index and attribute measure. The uncertainties can be described by probability distributions. The values of evaluation index and attribute measure were employed through random numbers generated by Monte Carlo simulations and an attribute measure belt was chosen instead of the linearity attribute measure function. Considering the uncertainties of evaluation index and attribute measure, the probability distributions of four risk grades are calculated using random numbers generated by Monte Carlo simulation. According to the probability distribution, the risk level can be analyzed under different confidence coefficients. The method improvement is more accurate and feasible compared with the results derived from the attribute recognition model. Finally, the improved attribute recognition method was applied and verified in Longmenshan tunnel in China.