Risk margin model of underground engineering based on possibility theory
RONG Xiaoli1, WEN Zhu1*, HAO Yiqing2, LU Hao3, XIONG Ziming3
1. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China; 2. The Rocket Army's Sergeant School of PLA, Qingzhou 262500, Shandong, China; 3. College of Defense Engineering, The Army Engineering University of PLA, Nanjing 210007, Jiangsu, China
Abstract: Underground engineering has complicated environment and unforeseen factors, so there are generally high risks. The traditional risk assessment method was mainly based on the loss theory of probability theory, which had limitations in practical projects. The essence of risk assessment was quantitative analysis and evaluation of uncertainty. The risk assessment of water inrush in the construction of karst tunnels in underground engineering as an example, and a risk margin model was conducted based on the possibility theory. It was found that under the condition of lack of information and people's cognitive differences, the margin model under the possibility theory was more in line with the actual situation of the project, which could provide quantitative basis for the decision-making and management of the project.
戎晓力,文祝,郝以庆,卢浩,熊自明. 基于可能性理论的地下工程风险裕度模型[J]. 隧道与地下工程灾害防治, 2019, 1(2): 83-91.
RONG Xiaoli, WEN Zhu, HAO Yiqing, LU Hao, XIONG Ziming. Risk margin model of underground engineering based on possibility theory. Hazard Control in Tunnelling and Underground Engineering, 2019, 1(2): 83-91.
钱七虎. 迎接我国城市地下空间开发高潮[J]. 岩土工程学报, 1998, 20(1):112-113. QIAN Qihu. Greeting the high tide of the development of urban underground space in China[J]. Chinese Journal of Geotechnical Engineering, 1998, 20(1):112-113.
[2]
王梦恕. 二十一世纪是城市地下空间开发利用的年代[J]. 生命与灾害, 2006(增刊1):14-15. WANG Mengshu. Twenty-first century is the age of urban underground space development and utilization[J]. Life & Disaster, 2006(Suppl.1):14-15.
[3]
钱七虎, 戎晓力. 中国地下工程安全风险管理的现状、问题及相关建议[J]. 岩石力学与工程学报, 2008, 27(4):649-655. QIAN Qihu, RONG Xiaoli. State, issues and relevant recommendations for security risk management of China's underground engineering[J]. Chinese Journal of Rock Mechanics and Engineering, 2008, 27(4):649-655.
[4]
ESKESEN S D, TENGBORG P, KAMPMANN J, et al. Guidelines for tunnelling risk management: International Tunnelling Association, Working Group No. 2[J]. Tunnelling & Underground Space Technology, 2004, 19(3):217-237.
[5]
PAN N F. Fuzzy AHP approach for selecting the suitable bridge construction method[J]. Automation in Construction, 2008, 17(8):958-965.
[6]
SOUSA R L, EINSTEIN H H. Risk analysis during tunnel construction using Bayesian Networks: Porto Metro case study[J]. Tunnelling & Underground Space Technology Incorporating Trenchless Technology Research, 2012, 27(1):86-100.
[7]
BROWN E T. Risk assessment and management in underground rock engineering—an overview[J]. Journal of Rock Mechanics and Geotechnical Engineering, 2012, 4(3):193-204.
[8]
EINSTEIN H H. Risk and risk analysis in rock engineering[J]. Tunnelling & Underground Technology, 1996, 11(2):141-155.
[9]
ZHANG L, SKIBNIEWSKI M J, WU X, et al. A probabilistic approach for safety risk analysis in metro construction[J]. Safety Science, 2014, 63(3):8-17.
[10]
HELTON J C. Quantification of margins and uncertainties:conceptual and computational basis[J]. Reliability Engineering & System Safety, 2011, 96(9):959-964.
[11]
HEMEZ F M, ATAMTURKTUR S. The dangers of sparse sampling for the quantification of margin and uncertainty[J]. Reliability Engineering & System Safety, 2011, 96(9):1220-1231.
[12]
HELTON J C. Conceptual and computational basis for the quantification of margins and uncertainty[M]. United States: Sandia National Laboratories, 2009.
[13]
HELTON J C, JOHNSON J D. Quantification of margins and uncertainties:alternative representations of epistemic uncertainty[J]. Reliability Engineering & System Safety, 2011, 96(9):959-964.
[14]
HAO Y, RONG X, LU H, et al. Quantification ofmargins and uncertainties for the risk of water inrush in a karst tunnel: representations of epistemic uncertainty with probability[J]. Arabian Journal for Science & Engineering, 2017, 43(1-3):1-14.
[15]
DECOOMAN G. Possibility theory part I: measure- and integral-theoretic groundwork; Part II: conditional possibility; Part III: possibilistic independence[J]. International Journal of General Systems, 1997, 25(4):291-371.
[16]
ZADEH LA. Fuzzy sets as a basis for a theory of possibility[J]. Fuzzy Sets and Systems, 1978, 1:3-28.
[17]
HALPERN JY, FAGIN R. Two views of belief: belief as generalized probability and belief as evidence[J]. Artificial Intelligence, 1992, 54: 275-317.
[18]
HELTON J C, JOHNSON J D, OBERKAMPF W L, et al. Representation of analysis results involving aleatory and epistemic uncertainty[J]. International Journal of General Systems, 2010, 39(6):605-646.
[19]
李利平. 高风险岩溶隧道突水灾变演化机理及其应用研究[D]. 济南: 山东大学, 2009. LI Liping. Study on catastrophe evolution mechanism of karst water inrush and its engineering application of high risk karst tunnel[D]. Jinan: Shandong University, 2009.
[20]
郭佳奇. 岩溶隧道防突厚度及突水机制研究[D]. 北京: 北京交通大学, 2011. GUO Jiaqi. Study on against-inrush thickness and waterburst mechanism of karst tunnel[D]. Beijing: Beijing Jiaotong University, 2011.
[21]
BAUDRIT C, DUBOIS D. Practical representations of incomplete probabilistic knowledge[J]. Computational Statistics & Data Analysis, 2006, 51(1):86-108.
[22]
BAUDRIT C, DUBOIS D, PERROT N. Representing parametric probabilistic models tainted with imprecision[J]. Fuzzy Sets & Systems, 2008, 159(15):1913-1928.
[23]
MOFARRAH A. Fuzzy based health risk assessment of heavy metals introduced into the marine environment[J]. Water Quality Exposure & Health, 2011, 3(1):25-36.
[24]
BAUDRIT C, DUBOIS D, GUYONNET D. Joint propagation and exploitation of probabilistic and possibilistic information in risk assessment[J]. IEEE Transactions on Fuzzy Systems, 2006, 14(5):593-608.
2019, Vol. 1 
