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Tunneling Risk Visualization Using BIM and Dynamic Bayesian Network

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Proceedings of the 26th International Symposium on Advancement of Construction Management and Real Estate (CRIOCM 2021)

Part of the book series: Lecture Notes in Operations Research ((LNOR))

Abstract

Ground tunneling construction increases the uncertainty of project risk, which cannot be proactively managed. Therefore, dynamic risk prediction and management become extremely significant for tunneling projects. This study proposes to combine the probability analysis of Dynamic Bayesian Network (DBN) with building information modeling (BIM) to manage risk during tunneling. Firstly, the tunnel BIM model is created based on the actual survey data. Secondly, the parameter related to risk factors stored in BIM is extracted into the DBN for risk prediction, including accurate geological information and relevant data of tunnel segments. Then the DBN is used to calculate the risk levels of different factors and to update the risk probability. The calculated risk level can be visualized in the BIM model through Dynamo, and different pipe sections are colored according to their predicted risk levels. An illustrative example is used to illustrate this method. Through the risk prediction analysis of the case, the feasibility of the method proposed in this paper is verified.

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References

  1. Underground Developments. Urban redevelopment Authority (2017)

    Google Scholar 

  2. Catalogue of Nortable Tunnel Failure, The Government of Hong Kong Special Administrative Region (2015)

    Google Scholar 

  3. Waltz, G.: Metro line leak causes chaos in Warsaw(2012). http://redaktorext.polskieradio.pl/1/9/Artykul/110064,Metro-line-leak-causes-chaos-in-Warsaw-\

  4. Li, S., et al.: Forecast of karst-fractured groundwater and defective geological conditions. Chin. J. Rock Mech. Eng. 26(2), 217–225 (2007)

    Google Scholar 

  5. Shi, A., Zhu, G., Yu, R., Wang, Y., Xia, T.: A randomized phase I/II trial to compare weekly usage with triple weekly usage of paclitaxel in concurrent radiochemotherapy for patients with locally advanced non-small cell lung cancer. Chin. J. Lung Cancer 14(3), 227–232 (2011)

    Google Scholar 

  6. Heckerman, D., Geiger, D., Chickering, D.M.: Learning Bayesian networks: The combination of knowledge and statistical data. Mach. Learn. 20(3), 197–243 (1995)

    Article  Google Scholar 

  7. Langseth, H., Portinale, L.: Bayesian networks in reliability. Reliab. Eng. Syst. Saf. 92(1), 92–108 (2007)

    Article  Google Scholar 

  8. Dean, T., Kirman, J., Kanazawa, K.: Probabilistic network representations of continuous-time stochastic processes for applications in planning and control. In: Hendler, J. (ed) Proceedings of the Artificial Intelligence Planning Systems, pp. 273–274. Morgan Kaufmann, San Francisco (CA) (1992)

    Google Scholar 

  9. Sousa, R., Miranda, T.: Deep underground engineering and the used of Artificial Intelligence Techniques. Int. J. Earth Environ. Sci. (2018)

    Google Scholar 

  10. Spackova, O., Sejoha, J., Straub, D.: Probability assessment of tunnel constryction performance based on data (2013)

    Google Scholar 

  11. Wu, X., Liu, H., Zhang, L., Skibniewski, M.J., Deng, Q., Teng, J.: A dynamic Bayesian network based approach to safety decision support in tunnel construction. Reliab. Eng. Syst. Saf. 134, 157–168 (2015)

    Article  Google Scholar 

  12. Mayouf, M., Cox, S., Boyd, D.: Exploring different information needs in building information modelling using soft systems (2014)

    Google Scholar 

  13. Maltezos, J., Harman, T.: Construction of the Ventilation Tower at Tottenham Court Road. Laing O’Rourke, UK (2018)

    Google Scholar 

  14. Zhang, L., Wu, X., Ding, L., Skibniewski, M.J., Lu, Y.: Bim-based risk identification system in tunnel construction. J. Civ. Eng. Manag. 22(4), 529–539 (2016)

    Article  Google Scholar 

  15. Niu, S., Pan, W., Zhao, Y.: A BIM-GIS integrated web-based visualization system for low energy building design. Procedia Eng. 121, 2184–2192 (2015)

    Article  Google Scholar 

  16. Sloot, R.N.F., Heutink, A., Voordijk, J.T.: Assessing usefulness of 4D BIM tools in risk mitigation strategies. Autom. Constr. 106, 102881 (2019)

    Article  Google Scholar 

  17. Providakis, S., Rogers, C.D.F., Chapman, D.N.: Predictions of settlement risk induced by tunnelling using BIM and 3D visualization tools. Tunn. Undergr. Space Technol. 92, 103049 (2019)

    Article  Google Scholar 

  18. Chen, X.-S., Liu, C.-C., Wu, I.C.: A BIM-based visualization and warning system for fire rescue. Adv. Eng. Inform. 37, 42–53 (2018)

    Article  Google Scholar 

  19. Natephra, W., Motamedi, A., Yabuki, N., Fukuda, T.: Integrating 4D thermal information with BIM for building envelope thermal performance analysis and thermal comfort evaluation in naturally ventilated environments. Build. Environ. 124, 194–208 (2017)

    Article  Google Scholar 

  20. Röck, M., Hollberg, A., Habert, G., Passer, A.: LCA and BIM: Visualization of environmental potentials in building construction at early design stages. Build. Environ. 140, 153–161 (2018)

    Article  Google Scholar 

  21. Juszczyk, M., Tomana, A., Bartoszek, M.: Current issues of BIM-based design change management, analysis and visualization. Procedia Eng. 164, 518–525 (2016)

    Article  Google Scholar 

  22. Rolfsen, C.N., Merschbrock, C.: Acceptance of construction scheduling visualizations: Bar-charts, flowline-charts, or perhaps BIM? Procedia Eng. 164, 558–566 (2016)

    Article  Google Scholar 

  23. McArthur, J.J., Shahbazi, N., Fok, R., Raghubar, C., Bortoluzzi, B., An, A.: Machine learning and BIM visualization for maintenance issue classification and enhanced data collection. Adv. Eng. Inform. 38, 101–112 (2018)

    Article  Google Scholar 

  24. Xie, X.F., Wu, Q.H., Liu, K.: Analysis on excavation face stability of deep shield tunnel considering effect of pore water pressure. J. Highway Transp. Res. Dev. 34, 94–100 (2017)

    Google Scholar 

  25. Balasubramanian, A.: Geotechnical investigation for tunnelling, University of Mysore (2016)

    Google Scholar 

  26. Schnatter, S.F.: Bayesian model discrimination and bayes factors for linear gaussian state space models. Roy. Stat. Soc. 57, 237–246 (1995)

    Google Scholar 

  27. Hu, J., Zhang, L., Ma, L., Liang, W.: An Integrated Safety Prognosis Model for Complex System Based on Dynamic Bayesian Network and Ant Colony Algorithm. Cooperative Research Centre for Integrated Engineering Asset Management, Beijing (2010)

    Google Scholar 

  28. Kim, J., Yan, S.Y., Fumagalli, A., Oki, E., Yamanaka, N.: An analytical model of spectrum fragmentation in a two-service elastic optical link. In: Proceedings of the 2015 IEEE Global Communications Conference. IEEE, New York (2015)

    Google Scholar 

  29. Zhang, L., Wu, X., Liu, H., Skibniewski, M.J., Deng, Q., Teng, J.: A Dynamic Bayesian Network Based Approach to Safety Decision Support in Tunnel Construction. Institute of Theoretical and Applied Informatics Polish Academy of Science, Gliwice, Poland (2014)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Sino-Australia Joint Research Center in BIM and Smart Construction, Shenzhen University, Shenzhen, China

    Ting Deng, DongDong Tang, Shuaishuai Jin & Yi Tan

Authors
  1. Ting Deng
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  2. DongDong Tang
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  3. Shuaishuai Jin
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  4. Yi Tan
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Corresponding author

Correspondence to Yi Tan .

Editor information

Editors and Affiliations

  1. Department of Construction Management, Tsinghua University, Beijing, China

    Hongling Guo

  2. Department of Construction Management, Tsinghua University, Beijing, China

    Dongping Fang

  3. Department of Real Estate and Construction, University of Hong Kong, Hongkong, China

    Weisheng Lu

  4. Department of Construction Management, Zhejiang University of Finance and Economics, Hangzhou, China

    Yi Peng

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Deng, T., Tang, D., Jin, S., Tan, Y. (2022). Tunneling Risk Visualization Using BIM and Dynamic Bayesian Network. In: Guo, H., Fang, D., Lu, W., Peng, Y. (eds) Proceedings of the 26th International Symposium on Advancement of Construction Management and Real Estate. CRIOCM 2021. Lecture Notes in Operations Research. Springer, Singapore. https://doi.org/10.1007/978-981-19-5256-2_7

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