Study on risk control of water inrush in tunnel construction period considering uncertainty
Abstract
Water inrush risk is a bottleneck problem affecting the safety and smooth construction of tunnel engineering works, so the risk control of water inrush is important, however, geological uncertainty and artificial uncertainty always accompany tunnel construction. Uncertainty will not only affect the accuracy of water inrush risk assessment results, but also affect the reliability of water inrush risk decision-making results. How to control the influence of uncertainty on water inrush risk is key to solving the problem of water inrush risk control. Based on the definition of improved risk, a risk analysis model of water inrush based on a fuzzy Bayesian network is constructed. The main factors affecting the risk of water inrush are determined by sensitivity analysis, and possible schemes in risk control of water inrush are proposed. Based on the characteristics of risk control of water inrush in a tunnel, a multi-attribute group decision-making model is constructed to determine the optimal water inrush risk control scheme, so that the optimal scheme for reducing uncertainty in risk control of water inrush is determined. Finally, this system is applied to Shiziyuan Tunnel. The results show that the proposed risk control system for reducing uncertainty of water inrush is efficacious.
First published online 21 August 2019
Keyword : water inrush risk, uncertainty, risk control system, fuzzy Bayesian network, multi-attribute decision making
How to Cite
Wen, Z., Xia, Y., Ji, Y., Liu, Y., Xiong, Z., & Lu, H. (2019). Study on risk control of water inrush in tunnel construction period considering uncertainty. Journal of Civil Engineering and Management, 25(8), 757-772. https://doi.org/10.3846/jcem.2019.10394
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Shang, X. G., & Jiang, W. S. (1997). A note on fuzzy information measures. Pattern Recognition Letters, 18(5), 425-432.
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Ying, H., & Rui-Hua, H. (2008). Risk attributes theory: Decision making under risk. European Journal of Operational Research, 186(1), 243-260. https://doi.org/10.1016/j.ejor.2007.01.012
Zadeh, L. A. (1965). Fuzzy sets. Information & Control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zhang, L., Skibniewski, M. J., Wu, X., Chen, Y., & Deng, Q. (2014). A probabilistic approach for safety risk analysis in metro construction. Safety Science, 63(3), 8-17. https://doi.org/10.1016/j.ssci.2013.10.016
Zhang, L., Wu, X., Skibniewski, M. J., Zhong, J., & Lu, Y. (2014). Bayesian-network-based safety risk analysis in construction projects. Reliability Engineering & System Safety, 131(3), 2939. https://doi.org/10.1016/j.ress.2014.06.006
Zhang, Q. S., & Jiang, S. Y. (2008). A note on information entropy measures for vague sets and its applications. Information Sciences, 178(21), 4184-4191. https://doi.org/10.1016/j.ins.2008.07.003
Ale, B. J. M. (2002). Risk assessment practices in The Netherlands. Safety Science, 40(1), 105-126. https://doi.org/10.1016/S0925-7535(01)00044-3
Atanassov, K. T. (1989). More on intuitionistic fuzzy sets. Elsevier North-Holland, Inc. https://doi.org/10.1016/0165-0114(92)90083-G
Aven, T., & Renn, O. (2009). On risk defined as an event where the outcome is uncertain. Journal of Risk Research, 12(1), 1-11. https://doi.org/10.1080/13669870802488883
Bao, T., Xie, X., Long, P., & Wei, Z. (2017). MADM method based on prospect theory and evidential reasoning approach with unknown attribute weights under intuitionistic fuzzy environment. Expert Systems with Applications, 88, 305-317. https://doi.org/10.1016/j.eswa.2017.07.012
Blavatskyy, P. R. (2014). A theory of decision-making under risk as a tradeoff between expected utility, expected utility deviation and expected utility skewness. Social Science Electronic Publishing. https://doi.org/10.2139/ssrn.2505828
Chen, S. M., & Han, W. H. (2018). A new multiattribute decision making method based on multiplication operations of interval-valued intuitionistic fuzzy values and linear programming methodology. Information Sciences, 429, 421-432. https://doi.org/10.1016/j.ins.2017.11.018
Chen, Z.-S., Chin, K.-S., Ding, H., & Li, Y.-L. (2016). Triangular intuitionistic fuzzy random decision making based on combination of parametric estimation, score functions, and prospect theory. Journal of Intelligent & Fuzzy Systems, 30(6), 3567-3581. https://doi.org/10.3233/IFS-162101
Detyniecki, M., & Yager, R. R. (2000). Ranking fuzzy numbers using a-weighted valuations. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 8(5), 573-591. https://doi.org/10.1142/S021848850000040X
Dong, X., Lu, H., Xia, Y., & Xiong, Z. (2016). Decision-making model under risk assessment based on entropy. Entropy, 18(11), 404. https://doi.org/10.3390/e18110404
Eleyedatubo, A. G, Wall, A., & Wang, J. (2010). Marine and offshore safety assessment by incorporative risk modeling in a fuzzy-Bayesian network of an induced mass assignment paradigm. Risk Analysis, 28(1), 95-112. https://doi.org/10.1111/j.1539-6924.2008.01004.x
Fischer, K., & Kleine, A. (2007). Remarks on “A measure of risk and a decision-making model based on expected utility and entropy” by Jiping Yang and Wanhua Qiu (EJOR 164 (2005), 792-799). European Journal of Operational Research, 182(1), 469-474. https://doi.org/10.1016/j.ejor.2006.07.033
Fraldi, M., & Guarracino, F. (2010). Analytical solutions for collapse mechanisms in tunnels with arbitrary cross sections. International Journal of Solids and Structures, 47(2), 216-223. https://doi.org/10.1016/j.ijsolstr.2009.09.028
Hao, Y., Rong, X., Ma, L., Fan, P., & Lu, H. (2016). Uncertainty analysis on risk assessment of water inrush in karst tunnels. Mathematical Problems in Engineering, Article ID 2947628. https://doi.org/10.1155/2016/2947628
Heckerman, D., Mamdani, A., & Wellman, M. P. (1995). Realworld applications of Bayesian networks. ACM. https://doi.org/10.1145/203330.203334
Jousselme, A.-L., Grenier, D., & Bossé, É. (2001). A new distance between two bodies of evidence. Information Fusion, 2(2), 91-101. https://doi.org/10.1016/S1566-2535(01)00026-4
Kahraman, C., Onar, S. C., & Oztaysi, B. (2015). Fuzzy multicriteria decision-making: A literature review. International Journal of Computational Intelligence Systems, 8(4), 637-666. https://doi.org/10.1080/18756891.2015.1046325
Kaplan, S., & Garrick, B. J. (1981). On the quantitative definition of risk. Risk Analysis, 1(1), 11-27. https://doi.org/10.1111/j.1539-6924.1981.tb01350.x
Karwowski, W., & Mital, A. (1986). Applications of approximate reasoning in risk analysis. Advances in Human Factors/Ergonomics, 6, 227-243. https://doi.org/10.1016/B978-0-444-42723-6.50020-9
Li, S. C., Wu, J., Xu, Z. H., & Li, L. P. (2017). Unascertained measure model of water and mud inrush risk evaluation in karst tunnels and its engineering application. KSCE Journal of Civil Engineering, 21(4), 1170-1182. https://doi.org/10.1007/s12205-016-1569-z
Li, S.-c., Zhou, Z.-q., Li, L.-p., Xu, Z.-h., Zhang, Q.-q., & Shi, S.-s. (2013). Risk assessment of water inrush in karst tunnels based on attribute synthetic evaluation system. Tunnelling and Underground Space Technology, 38, 50-58. https://doi.org/10.1016/j.tust.2013.05.001
Li, X., & Li, Y. (2014). Research on risk assessment system for water inrush in the karst tunnel construction based on GIS: Case study on the diversion tunnel groups of the Jinping II Hydropower Station. Tunnelling & Underground Space Technology, 40(2), 82-191. https://doi.org/10.1016/j.tust.2013.10.005
Liu, J., Liao, X., & Yang, J. B. (2015). A group decision-making approach based on evidential reasoning for multiple criteria sorting problem with uncertainty. European Journal of Operational Research, 246(3), 858-873. https://doi.org/10.1016/j.ejor.2015.05.027
Ma, M., & Jiyao, A. N. (2015). Combination of evidence with different weighting factors a novel probabilistic-based dissimilarity measure approach. Journal of Sensors, Article ID 509385. https://doi.org/10.1155/2015/509385
Melchers, R E. (2001). On the ALARP approach to risk management. Reliability Engineering & System Safety, 71(2), 201-208. https://doi.org/10.1016/S0951-8320(00)00096-X
Rassafi, A. A., Ganji, S. S., & Pourkhani, H. (2017). Road safety assessment under uncertainty using a multi attribute decision analysis based on Dempster–Shafer theory. KSCE Journal of Civil Engineering, 22(8), 3137-3152. https://doi.org/10.1007/s12205-017-1854-5
Shang, X. G., & Jiang, W. S. (1997). A note on fuzzy information measures. Pattern Recognition Letters, 18(5), 425-432.
Smarandache, F., Dezert, J., & Tacnet, J. M. (2011). Fusion of sources of evidence with different importances and reliabilities. In 2010 13th International Conference on Information Fusion (pp. 1-8). IEEE. https://doi.org/10.1109/ICIF.2010.5712071
Špačková, O., & Straub, D. (2012). Dynamic Bayesian network for probabilistic modeling of tunnel excavation processes. Computer‐Aided Civil & Infrastructure Engineering, 28(1), 1-21. https://doi.org/10.1111/j.1467-8667.2012.00759.x
Staveren, M. T. V. (2009). Extending to geotechnical risk management. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 3(3), 10. https://doi.org/10.1080/17499510902788835
Tang, C., Wang, J., & Zhang, J. (2010). Preliminary engineering application of microseismic monitoring technique to rockburst prediction in tunneling of Jinping II project. Journal of Rock Mechanics and Geotechnical Engineering, 2(3), 193-208. https://doi.org/10.3724/SP.J.1235.2010.00193
Uusitalo, L. (2007). Advantages and challenges of Bayesian networks in environmental modelling. Ecological Modelling, 203(3-4), 312-318. https://doi.org/10.1016/j.ecolmodel.2006.11.033
Wang, Y., Jing, H., Yu, L., Su, H., & Luo, N. (2017). Set pair analysis for risk assessment of water inrush in karst tunnels. Bulletin of Engineering Geology & the Environment, 76(3), 1199-1207. https://doi.org/10.1007/s10064-016-0918-y
Xia, Y., Xiong, Z., Dong, X., & Lu, H. (2017). Risk assessment and decision-making under uncertainty in tunnel and underground engineering. Entropy, 19(10), 549. https://doi.org/10.3390/e19100549
Xia, Y., Xiong, Z., Wen, Z., Lu, H., & Dong, X. (2018). Entropybased risk control of geological disasters in mountain tunnels under uncertain environment. Entropy, 20(7), 503. https://doi.org/10.3390/e20070503
Xu, J., Wan, S. P., & Dong, J. Y. (2016). Aggregating decision information into Atanassov’s intuitionistic fuzzy numbers for heterogeneous multi-attribute group decision making. Applied Soft Computing, 41(C), 331-351. https://doi.org/10.1016/j.asoc.2015.12.045
Yang, J. B., & Xu, D. L. (2013). Evidential reasoning rule for evidence combination. Artificial Intelligence, 205, 1-29. https://doi.org/10.1016/j.artint.2013.09.003
Yang, J. P., & Qiu, W. (2005). A measure of risk and a decisionmaking model based on expected utility and entropy. European Journal of Operational Research, 164(3), 792-799. https://doi.org/10.1016/j.ejor.2004.01.031
Ye, J. (2007). Improved method of multicriteria fuzzy decisionmaking based on vague sets. Computer-Aided Design, 39(2), 164-169. https://doi.org/10.1016/j.cad.2006.11.005
Ying, H., & Rui-Hua, H. (2008). Risk attributes theory: Decision making under risk. European Journal of Operational Research, 186(1), 243-260. https://doi.org/10.1016/j.ejor.2007.01.012
Zadeh, L. A. (1965). Fuzzy sets. Information & Control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zhang, L., Skibniewski, M. J., Wu, X., Chen, Y., & Deng, Q. (2014). A probabilistic approach for safety risk analysis in metro construction. Safety Science, 63(3), 8-17. https://doi.org/10.1016/j.ssci.2013.10.016
Zhang, L., Wu, X., Skibniewski, M. J., Zhong, J., & Lu, Y. (2014). Bayesian-network-based safety risk analysis in construction projects. Reliability Engineering & System Safety, 131(3), 2939. https://doi.org/10.1016/j.ress.2014.06.006
Zhang, Q. S., & Jiang, S. Y. (2008). A note on information entropy measures for vague sets and its applications. Information Sciences, 178(21), 4184-4191. https://doi.org/10.1016/j.ins.2008.07.003