Risk Assessment of Tunnel Construction Based on Improved Cloud Model
Publication: Journal of Performance of Constructed Facilities
Volume 34, Issue 3
Abstract
Objective and scientific risk assessment is of great significance for tunnel construction. The intrinsic link between risk level and influencing factors are poorly understood, and our ability to assess the risks of tunnel construction remains limited. In this study, we improved the normal cloud model to combine fuzziness and randomness into risk assessment to make the result more accurate. First, we constructed the risk index system for tunnel construction; second, we calculated the weight of the risk index by using the AHP method. Then we determined the improved cloud model eigenvalue through investigation and analysis and put the risk index data into a forward cloud generator to obtain the fuzzy comprehensive evaluation matrix. Following this, we combined weights with the matrix to receive the membership degree of different risk levels. Finally, the final risk level was obtained by analyzing the purposely designed membership radar chart. We applied this assessment model to evaluate the construction risk of the Tiger Mountain tunnel in section . The study shows that the improved cloud model delivers a more scientific and reasonable risk assessment by using the fuzzy random theory, which introduces a new concept for risk assessment in tunnel construction.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request. A risk assessment program based on an improved cloud model is available. The data contained in Figs. 11 and 12 and Tables 9 and 10 can be obtained by this program.
Acknowledgments
This work was financially supported by the Program of the National Natural Science Foundation of China (Grant Nos. 51679131 and 51709159), the Key Research and Development Project of Shandong Province (Grant Nos. 2017GSF220014 and 2019GSF111030), the National Natural Science Foundation of China (Grant No. 41672281), the Fundamental Research Funds of Shandong University (Grant No. 2017JC001), the CRSRI Open Research Program (Program SN: CKWV2018468/KY), the State Key Laboratory of Mining Disaster Prevention and Control Co-founded by Shandong Province and the Ministry of Science and Technology (MDPC201802), and the Education Commission Science and Technology research project of Chongqing (KJ1712304). The authors are grateful to the students from Shandong University for their help during the physical model test, the editors, and reviewers for their valuable advisements to improve the quality of this paper.
References
Banaeian, N., H. Mobli, B. Fahimnia, I. E. Nielsen, and M. Omid. 2018. “Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry.” Comput. Oper. Res. 89 (Jan): 337–347. https://doi.org/10.1016/j.cor.2016.02.015.
Baudry, G., C. Macharis, and T. Vallee. 2018. “Range-based multi-actor multi-criteria analysis: A combined method of multi-actor multi-criteria analysis and Monte Carlo simulation to support participatory decision making under uncertainty.” Eur. J. Oper. Res. 264 (1): 257–269. https://doi.org/10.1016/j.ejor.2017.06.036.
Beard, A. N. 2010. “Tunnel safety, risk assessment and decision-making.” Tunnelling Underground Space Technol. 25 (1): 91–94. https://doi.org/10.1016/j.tust.2009.07.006.
Bieniawski, Z. T. 1974. “Geomechanics classification of rock masses and its application in tunneling.” In Vol. 11 of Proc., Int. Congress on Rock Mechanics, 27–32. Denver: International Journal of Rock Mechanics and Mining Sciences and Geomechanics.
Bieniawski, Z. T. 1989. Engineering rock mass classification. New York: Wiley.
Butscher, C., M. Wunderle, L. Kunemund, and P. Blum. 2018. “Prediction and assessment of karst-related risks at the tunnel Albabstieg.” Geotechnik. 41 (2): 124–138. https://doi.org/10.1002/gete.201700018.
Chen, J. J., F. Zhou, J. S. Yang, and B. C. Liu. 2009. “Fuzzy analytic hierarchy process for risk evaluation of collapse during construction of mountain tunnel.” Rock Soil Mech. 30 (8): 2365–2370. https://doi.org/10.16285/j.rsm.2009.08.017.
Einstein, H. H. 1996. “Risk and risk analysis in rock engineering.” Tunneling Underground Space Technol. 11 (2): 141–155. https://doi.org/10.1016/0886-7798(96)00014-4.
Eskesen, S. D., P. Tengborg, J. Kampmann, and T. H. Veicherts. 2004. “Guidelines for tunnelling risk management: International tunnelling association, Working Group No. 2.” Tunnelling Underground Space Technol. 19 (3): 217–237. https://doi.org/10.1016/j.tust.2004.01.001.
Ferreira, L., D. Borenstein, M. B. Righi, and A. T. de Almeida Filho. 2018. “A fuzzy hybrid integrated framework for portfolio optimization in private banking.” Expert Syst. Appl. 92 (Feb): 350–362. https://doi.org/10.1016/j.eswa.2017.09.055.
Grant, M., and M. G. Stewart. 2015. “Probabilistic risk assessment for improvised explosive device attacks that cause significant building damage.” J. Perform. Constr. Facil. 29 (5): B4014009. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000694.
Kazaras, K., M. Konstandinidou, Z. Nivolianitou, and K. Kirytopoulos. 2013. “Enhancing road tunnel risk assessment with a fuzzy system based on the CREAM methodology.” Chem. Eng. Trans. 31: 349–354. https://doi.org/10.3303/CET1331059.
Kemblowski, M. W., B. Grzyl, A. Kristowski, and A. Siemaszko. 2017. “Risk modelling with Bayesian networks—Case study: Construction of tunnel under the Dead Vistula River in Gdansk.” Procedia Eng. 196: 585–591. https://doi.org/10.1016/j.proeng.2017.08.046.
Khan, M. M. U. H., M. Vaezi, and A. Kumar. 2018. “Optimal siting of solid waste-to-value-added facilities through a GIS-based assessment.” Sci. Total Environ. 610–611 (Jan): 1065–1075. https://doi.org/10.1016/j.scitotenv.2017.08.169.
Li, D. Y., H. J. Meng, and X. M. Shi. 1995. “Membership clouds and membership cloud generators.” Comput. Res. Dev. 32 (6): 15–20.
Li, L. P., T. Lei, S. C. Li, Z. H. Xu, Y. G. Xue, and S. S. Shi. 2015. “Dynamic risk assessment of water inrush in tunneling and software development.” Geomech. Eng. 9 (1): 57–81. https://doi.org/10.12989/gae.2015.9.1.057.
McFcat-Smith, I. 2000. “Risk assessment for tunneling in adverse geological conditions in Asia.” In Proc., ICTUS. Rotterdam, Netherlands: A.A. Balkema.
Palmisano, F., C. Vitone, and F. Cotecchia. 2018. “Assessment of landslide damage to buildings at the urban scale.” J. Perform. Constr. Facil. 32 (4): 04018055. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001201.
Seyedmohammadi, J., F. Sarmadian, and A. A. Jafarzadeh. 2018. “Application of SAW, TOPSIS and fuzzy TOPSIS models in cultivation priority planning for maize, rapeseed and soybean crops.” Geoderma. 310 (Jan): 178–190. https://doi.org/10.1016/j.geoderma.2017.09.012.
Špačková, O., E. Novotná, M. Šejnoha, and J. Šejnoha. 2013. “Probabilistic models for tunnel construction risk assessment.” Adv. Eng. Software 62–63: 72–84. https://doi.org/10.1016/j.advengsoft.2013.04.002.
Xu, Q. W., and K. L. Xu. 2018. “Risk assessment of rail haulage accidents in inclined tunnels with Bayesian network and bow-tie model.” Curr. Sci. 114 (12): 2530–2538. https://doi.org/10.18520/cs/v114/i12/2530-2538.
Zhang, Q., and X. P. Wang. 2016. “The comparison of some fuzzy operators used in fuzzy comprehensive evaluation models.” Fuzzy Syst. Math. 30 (3): 165–171.
Zhou, Z. Q., S. C. Li, L. P. Li, S. S. Shi, and Z. H. Xu. 2015. “An optimal classification method for risk assessment of water inrush in Karst tunnel based on grey system theory.” Geomech. Eng. 8 (5): 631–647. https://doi.org/10.12989/gae.2015.8.5.631.
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Published In
Journal of Performance of Constructed Facilities
Volume 34 • Issue 3 • June 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Oct 28, 2018
Accepted: Oct 14, 2019
Published online: Mar 17, 2020
Published in print: Jun 1, 2020
Discussion open until: Aug 17, 2020
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