Dynamic Bayesian Network for Predicting Tunnel-Collapse Risk in the Case of Incomplete Data
Publication: Journal of Performance of Constructed Facilities
Volume 36, Issue 4
Abstract
Collapse is one of the most dangerous aspects of drilling–blasting construction in highway tunnels. To accurately control tunnel-collapse risk, a multistate dynamic Bayesian network (DBN) evaluation method for highway tunnel collapse based on parameter learning was proposed. First, by analyzing the risk mechanism of tunnel construction, the initial BN model was established based on the causal relationship between risk factors and construction risk in hydrogeological conditions, construction technology, and construction management. Next, the construction process was discretized into finite time slices. In consideration of the fuzzy uncertainty of nodes, node polymorphism was introduced to construct a multistate DBN. Then, 50 typical tunnel-collapse cases were taken as sample data, and the conditional probability distribution of initial BN was derived using parameter learning based on the expectation-maximization (EM) algorithm. Using DBN reasoning and sensitivity analysis, the dynamic risk probability and the dominant factors of tunnel collapse were predicted. Finally, the DBN model was fed back with the measured cumulative values and velocity of the crown settlement, which updated the dynamic risk probability assessment results. In analyzing the collapse probability of Jinzhupa tunnel passing through the angular unconformity contact zone as an example, the results demonstrated that dynamic risk assessment results combined with monitoring data could better reflect the reality of construction contingencies, providing real-time risk management guidance.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was financially supported by The National Natural Science Foundation of China (Grant Nos. 52068004 and 51978179).
References
Bieniawski, Z. T. 1973. “Engineering classification of jointed rock masses.” Trans. South Afr. Inst. Civ. Eng. 15 (12): 335–343.
Chu, H. D., G. L. Xu, N. Yasufuku, Z. Yu, P. L. Liu, and J. F. Wang. 2017. “Risk assessment of water inrush in karst tunnels based on two-class fuzzy comprehensive evaluation method.” Arabian J. Geosci. 10 (7): 1–12. https://doi.org/10.1007/s12517-017-2957-5.
Dempster, A. P., N. M. Laird, and D. B. Rubin. 1977. “Maximum likelihood from incomplete data via the EM algorithm.” R. Stat. Soc. London, Ser. B 39 (1): 1–22. https://doi.org/10.1111/j.2517-6161.1977.tb01600.x.
Deng, X. H., T. Xu, and R. Wang. 2018. “Risk evaluation model of highway tunnel portal construction based on BP fuzzy neural network.” Comput. Intell. Neurosci. 2018 (Aug): 1–16. https://doi.org/10.1155/2018/8547313.
Druzdzel, M. J., and L. C. van der Gaag. 2000. “Building probabilistic networks: ‘Where do the numbers come from?’” IEEE Trans. Knowl. Data Eng. 12 (4): 481–486. https://doi.org/10.1109/TKDE.2000.868901.
Einstein, H. H. 1996. “Risk and risk analysis in rock engineering.” Tunnelling 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.
Far, M. S., H. W. Huang, Y. D. Xue, and M. L. Zhou. 2019. “A discussion of ‘a simplified prediction method for evaluating tunnel displacement induced by laterally adjacent excavations’ by Zheng et al.” Comput. Geotech. 109 (May): 293–296. https://doi.org/10.1016/j.compgeo.2019.01.008.
Friedman, N., K. Murphy, and S. Russell. 2013. “Learning the structure of dynamic probabilistic networks.” Preprint, submitted January 30, 2013. http://arxiv.org/abs/1301.7374.
Hamidi, J. K., K. Shahriar, B. Rezai, J. Rostami, and H. Bejari. 2010. “Risk assessment based selection of rock TBM for adverse geological conditions using Fuzzy-AHP.” Bull. Eng. Geol. Environ. 69 (4): 523–532. https://doi.org/10.1007/s10064-009-0260-8.
Heckerman, D. 2008. “A tutorial on learning with bayesian networks.” In Vol. 156 of Innovations in Bayesian Networks, edited by D. E. Holmes and L. E. Jain. Berlin: Springer. https://doi.org/10.1007/978-3-540-85066-3_3.
Hyun, K. C., S. Min, H. Choi, J. Park, and I. M. Lee. 2015. “Risk analysis using fault-tree analysis (FTA) and analytic hierarchy process (AHP) applicable to shield TBM tunnels.” Tunnelling Underground Space Technol. 49 (Jun): 121–129. https://doi.org/10.1016/j.tust.2015.04.007.
Janzing, D., D. Balduzzi, M. Grosse-Wentrup, and B. Schölkopf. 2013. “Quantifying causal influences.” Ann. Stat. 41 (5): 2324–2358. https://doi.org/10.1214/13-AOS1145.
Jiang, A. N., and L. F. Jin. 2013. “Failure risk analysis of rich water area tunnel based on support vector machine and particle swarm optimization.” Disaster Adv. 6 (1): 487–497.
Kazaras, K., K. Kirytopoulos, and A. Rentizelas. 2012. “Introducing the STAMP method in road tunnel safety assessment.” Saf. Sci. 50 (9): 1806–1817. https://doi.org/10.1016/j.ssci.2012.04.013.
Leu, S. S., and T. J. W. Adi. 2011. “Probabilistic prediction of tunnel geology using a Hybrid Neural-HMM.” Eng. Appl. Artif. Intell. 24 (4): 658–665. https://doi.org/10.1016/j.engappai.2011.02.010.
Marcot, B. G., J. D. Steventon, G. D. Sutherland, and R. K. McCann. 2006. “Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation.” Can. J. For. Res. 36 (12): 3063–3074. https://doi.org/10.1139/x06-135.
Pamukcu, C. 2015. “Analysis and management of risks experienced in tunnel construction.” Acta Montan. Slovaca 20 (4): 271–281. https://doi.org/10.3390/ams20040271.
Pan, Y., S. W. Ou, L. M. Zhang, W. J. Zhang, X. G. Wu, and H. Li. 2019. “Modeling risks in dependent systems: A Copula-Bayesian approach.” Reliab. Eng. Syst. Saf. 188 (Aug): 416–431. https://doi.org/10.1016/j.ress.2019.03.048.
Qian, Q. 2012. “Challenges faced by underground projects construction safety and countermeasures.” Chin. J. Rock Mech. Eng. 31 (10): 1945–1956. https://doi.org/10.3969/j.issn.1000-6915.2012.10.001.
Song, Q., Y. G. Xue, G. K. Li, M. X. Su, D. H. Qiu, F. M. Kong, and B. H. Zhou. 2021. “Using Bayesian network and Intuitionistic fuzzy analytic hierarchy process to assess the risk of water inrush from fault in subsea tunnel.” Geomech. Eng. 27 (6): 605–614. https://doi.org/10.12989/gae.2021.27.6.605.
Spackova, O., and D. Straub. 2013. “Dynamic Bayesian network for probabilistic modeling of tunnel excavation processes.” Comput.-Aided Civ. Infrastruct. Eng. 28 (1): 1–21. https://doi.org/10.1111/j.1467-8667.2012.00759.x.
Stelzenmuller, V., J. Lee, E. Garnacho, and S. I. Rogers. 2010. “Assessment of a Bayesian belief network—GIS framework as a practical tool to support marine planning.” Mar. Pollut. Bull. 60 (10): 1743–1754. https://doi.org/10.1016/j.marpolbul.2010.06.024.
Sun, J., B. Liu, Z. Chu, D. Ren, and Y. Song. 2018a. “Type classification and main characteristics of tunnel collapses.” China Railway Sci. 39 (6): 44–51. https://doi.org/10.3969/j.issn.1001-4632.2018.06.07.
Sun, J. L., B. G. Liu, Z. F. Chu, L. Chen, and X. Li. 2018b. “Tunnel collapse risk assessment based on multistate fuzzy Bayesian networks.” Qual. Reliab. Eng. Int. 34 (8): 1646–1662. https://doi.org/10.1002/qre.2351.
Wang, X. L., J. X. Lai, J. L. Qiu, W. Xu, L. X. Wang, and Y. B. Luo. 2020. “Geohazards, reflection and challenges in Mountain tunnel construction of China: A data collection from 2002 to 2018.” Geomatics Nat. Hazards Risk 11 (1): 766–785. https://doi.org/10.1080/19475705.2020.1747554.
Wong, T. T., and P. Y. Yeh. 2020. “Reliable accuracy estimates from k-fold cross validation.” IEEE Trans. Knowl. Data Eng. 32 (8): 1586–1594. https://doi.org/10.1109/TKDE.2019.2912815.
Wu, X. G., H. T. Liu, L. M. Zhang, M. J. Skibniewski, Q. L. Deng, and J. Y. Teng. 2015. “A dynamic Bayesian network based approach to safety decision support in tunnel construction.” Reliab. Eng. Syst. Saf. 134 (Feb): 157–168. https://doi.org/10.1016/j.ress.2014.10.021.
Xiong, Z. M., J. T. Guo, Y. P. Xia, H. Lu, M. Y. Wang, and S. S. Shi. 2018. “A 3D multi-scale geology modeling method for tunnel engineering risk assessment.” Tunnelling Underground Space Technol. 73 (Mar): 71–81. https://doi.org/10.1016/j.tust.2017.12.003.
Xue, Y. G., Z. Q. Li, D. H. Qiu, W. M. Yang, L. W. Zhang, Y. F. Tao, and K. Zhang. 2019. “Prediction model for subway tunnel collapse risk based on Delphi-ideal point method and geological forecast.” Soil Mech. Found. Eng. 56 (3): 191–199. https://doi.org/10.1007/s11204-019-09589-4.
Xue, Y. G., X. L. Zhang, S. C. Li, D. H. Qiu, M. X. Su, L. P. Li, Z. Q. Li, and Y. F. Tao. 2018. “Analysis of factors influencing tunnel deformation in loess deposits by data mining: A deformation prediction model.” Eng. Geol. 232 (Jan): 94–103. https://doi.org/10.1016/j.enggeo.2017.11.014.
Yan, H. Y., C. Gao, H. Elzarka, K. Mostafa, and W. B. Tang. 2019. “Risk assessment for construction of urban rail transit projects.” Saf. Sci. 118 (Oct): 583–594. https://doi.org/10.1016/j.ssci.2019.05.042.
Yao, H., Y. Zhang, J. Li, and H. Wang. 2014. “Research on sensitivity analysis for dynamic Bayesian networks.” J. Comput. Res. Dev. 51 (3): 536–547. https://doi.org/10.7544/issn1000-1239.2014.20120609.
Yu, J., D. H. Zhong, B. Y. Ren, D. W. Tong, and K. Hong. 2017. “Probabilistic risk analysis of diversion tunnel construction simulation.” Comput.-Aided Civ. Infrastruct. Eng. 32 (9): 748–771. https://doi.org/10.1111/mice.12276.
Zhang, G. H., Y. Y. Jiao, L. B. Chen, H. Wang, and S. C. Li. 2016a. “Analytical model for assessing collapse risk during mountain tunnel construction.” Can. Geotech. J. 53 (2): 326–342. https://doi.org/10.1139/cgj-2015-0064.
Zhang, K., W. B. Zheng, C. Xu, and S. G. Chen. 2019. “An improved extension system for assessing risk of water inrush in tunnels in carbonate karst terrain.” KSCE J. Civ. Eng. 23 (5): 2049–2064. https://doi.org/10.1007/s12205-019-0756-0.
Zhang, L. M., L. Y. Ding, X. G. Wu, and M. J. Skibniewski. 2017. “An improved Dempster-Shafer approach to construction safety risk perception.” Knowl.-Based Syst. 132 (Sep): 30–46. https://doi.org/10.1016/j.knosys.2017.06.014.
Zhang, L. M., X. G. Wu, Y. W. Qin, M. J. Skibniewski, and W. L. Liu. 2016b. “Towards a fuzzy Bayesian network based approach for safety risk analysis of tunnel-induced pipeline damage.” Risk Anal. 36 (2): 278–301. https://doi.org/10.1111/risa.12448.
Zhou, R., W. P. Fang, and J. S. Wu. 2020. “A risk assessment model of a sewer pipeline in an underground utility tunnel based on a Bayesian network.” Tunnelling Underground Space Technol. 103 (Sep): 103473. https://doi.org/10.1016/j.tust.2020.103473.
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Journal of Performance of Constructed Facilities
Volume 36 • Issue 4 • August 2022
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© 2022 American Society of Civil Engineers.
History
Received: Jan 28, 2022
Accepted: Apr 4, 2022
Published online: May 31, 2022
Published in print: Aug 1, 2022
Discussion open until: Oct 31, 2022
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