An evidence-based risk decision support approach for metro tunnel construction
Abstract
The risk-informed decision-making of metro tunnel project is often faced with the problem of inadequate utilization of available information. In order to address the epistemic uncertainty problem caused by insufficient utilization of information in decision-making, this paper proposes a risk decision support approach for metro tunnel construction based on Continuous Time Bayesian Network (CTBN) technique. CTBN can factor the state space of variables in tunnel projects and perform evidence-based reasoning, which enables the diverse information of expert opinions, project-specific parameters, historical data and engineering anomalies to be the evidence to support decision-making. A concise CTBN model development method based on Dynamic Fault Trees is presented to replace the cumbersome model learning process. The proposed approach can utilize multi-source information as evidence to provide multi-form decision support both in the pre-construction stage and construction stage of the tunnel construction project, and the results can support the decisions on judging the acceptability of the risk, developing response strategies for risk factors and diagnosing the causes of the hazardous event. A case study on the water leakage risk of tunnel construction in China is presented to illustrate the feasibility of the approach. The case study shows that the approach can assist in making informed decisions, so as to improve the engineering safety.
Keyword : Continuous Time Bayesian Network, evidence, risk-informed decision-making, tunnel construction, knowledge, multi-source information
How to Cite
Guo, Y., Zheng, J., Zhang, R., & Yang, Y. (2022). An evidence-based risk decision support approach for metro tunnel construction. Journal of Civil Engineering and Management, 28(5), 377–396. https://doi.org/10.3846/jcem.2022.16807
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Liu, Y., Xia, Y., Lu, H., & Xiong, Z. (2019). Risk control technology for water inrush during the construction of deep, long tunnels. Mathematical Problems in Engineering, Article ID 3070576. https://doi.org/10.1155/2019/3070576
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Ayhan, B. U., & Tokdemir, O. B. (2019). Predicting the outcome of construction incidents. Safety Science, 113, 91–104. https://doi.org/10.1016/j.ssci.2018.11.001
Beeson, S., & Andrews, J. D. (2003). Importance measures for non-coherent-system analysis. IEEE Transactions on Reliability, 52(3), 301–310. https://doi.org/10.1109/TR.2003.816397
Blockley, D. (1999). Risk based structural safety methods in context. Structural Safety, 21(4), 335–348. https://doi.org/10.1016/S0167-4730(99)00028-4
Boudali, H., Crouzen, P., & Stoelinga, M. (2009). A rigorous, compositional, and extensible framework for dynamic fault tree analysis. IEEE Transactions on Dependable and Secure Computing, 7(2), 128–143. https://doi.org/10.1109/TDSC.2009.45
Cao, D. (2011). Novel models and algorithms for systems reliability modeling and optimization [PhD dissertation]. Wayne State University.
Cao, B. T., Freitag, S., & Meschke, G. (2018). A fuzzy surrogate modelling approach for real-time predictions in mechanised tunnelling. International Journal of Reliability and Safety, 12(1–2), 187–217. https://doi.org/10.1504/IJRS.2018.092521
Cárdenas, I. C., Al‐jibouri, S. S., Halman, J. I., & Tol, F. A. V. (2012). Capturing and integrating knowledge for managing risks in tunnel works. Risk Analysis: An International Journal, 33(1), 92–108. https://doi.org/10.1111/j.1539-6924.2012.01829.x
Choi, H. H., Cho, H. N., & Seo, J. W. (2004). Risk assessment methodology for underground construction projects. Journal of Construction Engineering and Management, 130(2), 258–272. https://doi.org/10.1061/(ASCE)0733-9364(2004)130:2(258)
Codetta-Raiteri, D. (2005). Extended fault trees analysis supported by stochastic petri nets. Università degli Studi di Torino.
Codetta-Raiteri, D., & Portinale, L. (2017). Generalized Continuous Time Bayesian Networks as a modelling and analysis formalism for dependable systems. Reliability Engineering & System Safety, 167, 639–651. https://doi.org/10.1016/j.ress.2017.04.014
Cooke, R. M., & Goossens, L. H. (2004). Expert judgement elicitation for risk assessments of critical infrastructures. Journal of Risk Research, 7(6), 643–656. https://doi.org/10.1080/1366987042000192237
Eskesen, S. D., Tengborg, P., Kampmann, J., & Veicherts, T. H. (2004). Guidelines for tunnelling risk management: International tunnelling association, working group No. 2. Tunnelling and Underground Space Technology, 19(3), 217–237. https://doi.org/10.1016/j.tust.2004.01.001
Espiritu, J. F., Coit, D. W., & Prakash, U. (2007). Component criticality importance measures for the power industry. Electric Power Systems Research, 77(5–6), 407–420. https://doi.org/10.1016/j.epsr.2006.04.003
Ferdous, R., Khan, F., Sadiq, R., Amyotte, P., & Veitch, B. (2013). Analyzing system safety and risks under uncertainty using a bow-tie diagram: An innovative approach. Process Safety and Environmental Protection, 91(1–2), 1–18. https://doi.org/10.1016/j.psep.2011.08.010
Forrester, T., Harris, M., Senecal, J., & Sheppard, J. (2019). Continuous Time Bayesian Networks in prognosis and health management of centrifugal pumps. Proceedings of the Annual Conference of the PHM Society, 11(1). https://doi.org/10.36001/phmconf.2019.v11i1.778
Gatti, E., Luciani, D., & Stella, F. (2012). A continuous time Bayesian network model for cardiogenic heart failure. Flexible Services and Manufacturing Journal, 24(4), 496–515. https://doi.org/10.1007/s10696-011-9131-2
Gitinavard, H. (2019). Strategic evaluation of sustainable projects based on hybrid group decision analysis with incomplete information. Journal of Quality Engineering and Production Optimization, 4(2), 17–30.
Hadikusumo, B. H. W., & Rowlinson, S. (2004). Capturing safety knowledge using design-for-safety-process tool. Journal of Construction Engineering and Management, 130(2), 281–289. https://doi.org/10.1061/(ASCE)0733-9364(2004)130:2(281)
Hallowell, M. R., & Gambatese, J. A. (2009). Qualitative research: Application of the Delphi method to CEM research. Journal of Construction Engineering and Management, 136(1), 99–107. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000137
Hong, E. S., Lee, I. M., Shin, H. S., Nam, S. W., & Kong, J. S. (2009). Quantitative risk evaluation based on event tree analysis technique: Application to the design of shield TBM. Tunnelling and Underground Space Technology, 24(3), 269–277. https://doi.org/10.1016/j.tust.2008.09.004
Hu, Q., & Huang, H. (2014). The state of the art of risk management standards on tunnels and underground works in China. In Second International Conference on Vulnerability and Risk Analysis and Management (ICVRAM) and the Sixth International Symposium on Uncertainty, Modeling, and Analysis (ISUMA) (pp. 419–426), Liverpool, UK. https://doi.org/10.1061/9780784413609.043
Hyun, K. C., Min, S., Choi, H., Park, J., & Lee, I. M. (2015). Risk analysis using fault-tree analysis (FTA) and analytic hierarchy process (AHP) applicable to shield TBM tunnels. Tunnelling and Underground Space Technology, 49, 121–129. https://doi.org/10.1016/j.tust.2015.04.007
Jensen, F. V., & Nielsen, T. D. (2007). Causal and Bayesian networks. In Bayesian networks and decision graphs. Information science and statistics. Springer. https://doi.org/10.1007/978-0-387-68282-2_2
Li, P., Wang, F., Zhang, C., & Li, Z. (2019). Face stability analysis of a shallow tunnel in the saturated and multilayered soils in short-term condition. Computers and Geotechnics, 107, 25–35. https://doi.org/10.1016/j.compgeo.2018.11.011
Liu, W., Zhao, T., Zhou, W., & Tang, J. (2018). Safety risk factors of metro tunnel construction in China: an integrated study with EFA and SEM. Safety Science, 105, 98–113. https://doi.org/10.1016/j.ssci.2018.01.009
Liu, Y., Xia, Y., Lu, H., & Xiong, Z. (2019). Risk control technology for water inrush during the construction of deep, long tunnels. Mathematical Problems in Engineering, Article ID 3070576. https://doi.org/10.1155/2019/3070576
Mohammadi, H., & Azad, A. (2021). Prediction of ground settlement and the corresponding risk induced by tunneling: An application of rock engineering system paradigm. Tunnelling and Underground Space Technology, 110, 103828. https://doi.org/10.1016/j.tust.2021.103828
Mousavi, S. M., & Gitinavard, H. (2019). An extended multi-attribute group decision approach for selection of outsourcing services activities for information technology under risks. International Journal of Applied Decision Sciences, 12(3), 227–241. https://doi.org/10.1504/IJADS.2019.100437
Nielsen, K. R. (2004). Risk management: Lessons from six continents. In Pipeline Division Specialty Congress 2004, San Diego, California, United States. https://doi.org/10.1061/40745(146)18
Nodelman, U., Shelton, C. R., & Koller, D. (2002). Continuous time Bayesian networks. In Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (pp. 378–387). Morgan Kaufmann Publishers Inc.
Nodelman, U., Shelton, C. R., & Koller, D. (2012). Learning continuous time Bayesian networks. In Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI2003), Acapulco, Mexico.
Nývlt, O., Prívara, S., & Ferkl, L. (2011). Probabilistic risk assessment of highway tunnels. Tunnelling and Underground Space Technology, 26(1), 71–82. https://doi.org/10.1016/j.tust.2010.06.010
Perreault, L., Thornton, M., & Sheppard, J. W. (2015). Deriving prognostic continuous time Bayesian networks from fault trees. In 2015 IEEE AUTOTESTCON (pp. 152–161). IEEE. https://doi.org/10.1109/AUTEST.2015.7356482
Qu, X., Meng, Q., Yuanita, V., & Wong, Y. (2011). Design and implementation of a quantitative risk assessment software tool for Singapore road tunnels. Expert Systems with Applications, 38(11), 13827–13834. https://doi.org/10.1016/j.eswa.2011.04.186
Shelton, C. R., Fan, Y., Lam, W., Lee, J., & Xu, J. (2010). Continuous time Bayesian network reasoning and learning engine. Journal of Machine Learning Research, 11, 1137–1140.
Sherehiy, B., & Karwowski, W. (2006). Knowledge management for occupational safety, health, and ergonomics. Human Factors and Ergonomics in Manufacturing & Service Industries, 16(3), 309–319. https://doi.org/10.1002/hfm.20054
Sousa, R. L., & Einstein, H. H. (2012). Risk analysis during tunnel construction using Bayesian Networks: Porto Metro case study. Tunnelling and Underground Space Technology, 27(1), 86–100. https://doi.org/10.1016/j.tust.2011.07.003
Špačková, O. (2012). Risk management of tunnel construction projects [PhD thesis]. Czech technical University in Prague.
Špačková, O., Novotná, E., Šejnoha, M., & Šejnoha, J. (2013a). Probabilistic models for tunnel construction risk assessment. Advances in Engineering Software, 62, 72–84. https://doi.org/10.1016/j.advengsoft.2013.04.002
Špačková, O., Šejnoha, J., & Straub, D. (2013b). Probabilistic assessment of tunnel construction performance based on data. Tunnelling and Underground Space Technology, 37, 62–78. https://doi.org/10.1016/j.tust.2013.02.006
Stella, F., & Amer, Y. (2012). Continuous time Bayesian network classifiers. Journal of Biomedical Informatics, 45(6), 1108–1119. https://doi.org/10.1016/j.jbi.2012.07.002
Sturlaugson, L., & Sheppard, J. W. (2016). Uncertain and negative evidence in continuous time Bayesian networks. International Journal of Approximate Reasoning, 70, 99–122. https://doi.org/10.1016/j.ijar.2015.12.013
Tchankova, L. (2002). Risk identification–basic stage in risk management. Environmental Management and Health, 13(3), 290–297. https://doi.org/10.1108/09566160210431088
Wang, F., Ding, L., Luo, H., & Love, P. E. (2014). Probabilistic risk assessment of tunneling-induced damage to existing properties. Expert Systems with Applications, 41(4), 951–961. https://doi.org/10.1016/j.eswa.2013.06.062
Wang, Z., & Chen, C. (2017). Fuzzy comprehensive Bayesian network-based safety risk assessment for metro construction projects. Tunnelling and Underground Space Technology, 70, 330–342. https://doi.org/10.1016/j.tust.2017.09.012
Wang, X., Li, Z., Wang, H., Rong, Q., & Liang, R. Y. (2016). Probabilistic analysis of shield-driven tunnel in multiple strata considering stratigraphic uncertainty. Structural Safety, 62, 88–100. https://doi.org/10.1016/j.strusafe.2016.06.007
Wu, H., Huang, R., Sun, W., Shen, S., Xu, Y., Liu, Y., & Du, S. (2014). Leaking behavior of shield tunnels under the Huangpu River of Shanghai with induced hazards. Natural Hazards, 70(2), 1115–1132. https://doi.org/10.1007/s11069-013-0863-z
Wu, X., Liu, H., Zhang, L., Skibniewski, M. J., Deng, Q., & Teng, J. (2015). A dynamic Bayesian network based approach to safety decision support in tunnel construction. Reliability Engineering & System Safety, 134, 157–168. https://doi.org/10.1016/j.ress.2014.10.021
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
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