Using Bayesian Networks for Selecting Risk-Response Strategies in Construction Projects
Publication: Journal of Construction Engineering and Management
Volume 148, Issue 8
Abstract
Construction projects are known as high-risk projects due to their complex and dynamic nature. The probability of success of construction projects is increased with efficient management of key risks. Understanding and selecting risk-response strategies is an essential step in the risk management process, which plays a crucial role in reducing the probability and impact of risks. Moreover, risk interdependencies play a pivotal role in deciding about risk-response strategies. On the other hand, the implementation of any response may impose a cost on the project, which the project team should be aware of in all phases of the construction project. Existing risk-assessment methods often do not consider the risk interdependencies and the role of risk-response strategies in reducing project risk exposure. Besides, the lack of data in construction projects hampers the use of risk-assessment methods. Bayesian networks (BNs) can reduce some of these limitations. The research aims are to provide a Bayesian-based model to assess the probability of project failure considering risk interdependencies and the role of risk-response strategies in reducing project risk exposure. The cost of implementing risk-response strategies to help decision makers in selecting from different responses is also calculated. Moreover, in order to validate the model and explain the procedure of implementing it practically, it was implemented in a construction project case study. The result of the model implementation for the case study showed that by allocating about 2% more money on implementing risk-response strategies than in the basic scenario (without entering observations), the probability of project failure was reduced about 60%. Moreover, when the total cost of implementing risk-response strategies was increased dramatically, the probability of project failure did not necessarily decrease because the most expensive risk-response strategy may not the best one. The proposed model provides the best combination of risk responses by minimizing the probability of project failure and the cost of implementing risk responses.
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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.
References
Adams, F. K. 2006. “Expert elicitation and Bayesian analysis of construction contract risks: An investigation.” Construct. Manage. Econ. 24 (1): 81–96. https://doi.org/10.1080/01446190500310254.
Banaitienė, N., A. Banaitis, and A. Norkus. 2011. “Risk management in projects: Peculiarities of Lithuanian construction companies.” Int. J. Strategic Property Manage. 15 (1): 60–73. https://doi.org/10.3846/1648715X.2011.568675.
Chin, K. S., D. W. Tang, J. B. Yang, S. Y. Wong, and H. Wang. 2009. “Assessing new product development project risk by Bayesian network with a systematic probability generation methodology.” Expert Syst. Appl. 36 (6): 9879–9890. https://doi.org/10.1016/j.eswa.2009.02.019.
Constantinou, A. C., N. Fenton, W. Marsh, and L. Radlinski. 2016. “From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support.” Artif. Intell. Med. 67 (Feb): 75–93. https://doi.org/10.1016/j.artmed.2016.01.002.
Dawes, J. 2008. “Do data characteristics change according to the number of scale points used? An experiment using 5-point, 7-point and 10-point scales.” Int. J. Market Res. 50 (1): 61–104. https://doi.org/10.1177/147078530805000106.
Dehdasht, G., R. Mohamad Zin, M. S. Ferwati, M. Abdullahi, A. Keyvanfar, and R. McCaffer. 2017. “DEMATEL-ANP risk assessment in oil and gas construction projects.” Sustainability 9 (8): 1420. https://doi.org/10.3390/su9081420.
El-Sayegh, S. M. 2008. “Risk assessment and allocation in the UAE construction industry.” Int. J. Project Manage. 26 (4): 431–438. https://doi.org/10.1016/j.ijproman.2007.07.004.
Fang, C., F. Marle, M. Xie, and E. Zio. 2013. “An integrated framework for risk response planning under resource constraints in large engineering projects.” IEEE Trans. Eng. Manage. 60 (3): 627–639. https://doi.org/10.1109/TEM.2013.2242078.
Fenton, N., W. Marsh, M. Neil, P. Cates, S. Forey, and M. Tailor. 2004. “Making resource decisions for software projects.” In Proc., 26th Int. Conf. on Software Engineering, 397–406. New York: IEEE.
Fenton, N., and M. Neil. 2012. Risk assessment and decision analysis with Bayesian networks. Boca Raton, FL: CRC Press.
Ghasemi, F., M. H. M. Sari, V. Yousefi, R. Falsafi, and J. Tamošaitienė. 2018. “Project portfolio risk identification and analysis, considering project risk interactions and using Bayesian networks.” Sustainability 10 (5): 1609. https://doi.org/10.3390/su10051609.
Ghosh, M., G. Kabir, and M. A. A. Hasin. 2017. “Project time–cost trade-off: A Bayesian approach to update project time and cost estimates.” Int. J. Manage. Sci. Eng. Manage. 12 (3): 206–215. https://doi.org/10.1080/17509653.2016.1255857.
Heckerman, D. 2008. “A tutorial on learning with Bayesian networks.” In Innovations in Bayesian networks: Theory and applications, 33–82. Berlin: Springer. https://doi.org/10.1007/978-3-540-85066-3_3.
Khodakarami, V., and A. Abdi. 2014. “Project cost risk analysis: A Bayesian networks approach for modeling dependencies between cost items.” Int. J. Project Manage. 32 (7): 1233–1245. https://doi.org/10.1016/j.ijproman.2014.01.001.
Khodakarami, V., N. Fenton, and M. Neil. 2007. “Project Scheduling: Improved approach to incorporate uncertainty using Bayesian Networks.” Project Manage. J. 38 (2): 39–49. https://doi.org/10.1177/875697280703800205.
Kim, S. Y., N. Van Tuan, and S. O. Ogunlana. 2009. “Quantifying schedule risk in construction projects using Bayesian belief networks.” Int. J. Project Manage. 27 (1): 39–50. https://doi.org/10.1016/j.ijproman.2008.03.003.
Kirchherr, J., and K. Charles. 2018. “Enhancing the sample diversity of snowball samples: Recommendations from a research project on anti-dam movements in Southeast Asia.” PLoS One 13 (8): e0201710. https://doi.org/10.1371/journal.pone.0201710.
Liu, J. 2010. “Bayesian network inference on risks of construction schedule-cost.” In Vol. 2 of Proc., 2010 Int. Conf. of Information Science and Management Engineering, 15–18. New York: IEEE.
Namazian, A., S. H. Yakhchali, V. Yousefi, and J. Tamošaitienė. 2019. “Combining Monte Carlo simulation and Bayesian networks methods for assessing completion time of projects under risk.” Int. J. Environ. Res. Public Health 16 (24): 5024. https://doi.org/10.3390/ijerph16245024.
Neil, M., N. Fenton, and L. Nielson. 2000. “Building large-scale Bayesian networks.” Knowl. Eng. Rev. 15 (3): 257–284. https://doi.org/10.1017/S0269888900003039.
Nik, E. R., S. H. Zegordi, and A. Nazari. 2011. “A multi-objective optimization and fuzzy prioritization approach for project risk responses selection.” In Proc., 2011 IEEE Int. Conf. on Industrial Engineering and Engineering Management, 888–892. New York: IEEE.
Odimabo, O. O., C. Oduoza, and S. Suresh. 2017. “Methodology for project risk assessment of building construction projects using Bayesian belief networks.” Int. J. Constr. Eng. Manage. 6 (6): 221–234. https://doi.org/10.5923/j.ijcem.20170606.01.
PMI (Project Management Institute). 2016. Construction extension to the PMBOK guide. Newtown Square, PA: PMI.
PMI (Project Management Institute). 2017. A guide to the project management body of knowledge. 6th ed. Newtown Square, PA: PMI.
PMI (Project Management Institute). 2019. The standard for risk management in portfolios, programs, and projects. Newtown Square, PA: PMI.
Prakash, S., and A. Jokhan. 2017. “Monte Carlo for selecting risk response strategies.” In Australasian Transport Research Forum, 1–12. Paris, TX: Transport Research Forum.
Ranjbar, S., A. Zarei, M. Hasanlou, M. Akhoondzadeh, J. Amini, and M. Amani. 2021. “Machine learning inversion approach for soil parameters estimation over vegetated agricultural areas using a combination of water cloud model and calibrated integral equation model.” J. Appl. Remote Sens. 15 (1): 018503. https://doi.org/10.1117/1.JRS.15.018503.
Renooij, S. 2001. “Probability elicitation for belief networks: Issues to consider.” Knowl. Eng. Rev. 16 (3): 255–269. https://doi.org/10.1017/S0269888901000145.
Sharma, D. K., P. K. Mishra, and R. Lekhi. 2020. “A Bayesian network framework for comparing project delivery methods.” Int. J. Civ. Eng. 18 (5): 519–537. https://doi.org/10.1007/s40999-019-00480-9.
Uusitalo, L. 2007. “Advantages and challenges of Bayesian networks in environmental modeling.” Ecol. Modell. 203 (3–4): 312–318. https://doi.org/10.1016/j.ecolmodel.2006.11.033.
van Dorp, J. R. 2020. “A dependent project evaluation and review technique: A Bayesian network approach.” Eur. J. Oper. Res. 280 (2): 689–706. https://doi.org/10.1016/j.ejor.2019.07.051.
Wu, D., J. Li, T. Xia, C. Bao, Y. Zhao, and Q. Dai. 2018. “A multiobjective optimization method considering process risk correlation for project risk response planning.” Inf. Sci. 467 (Oct): 282–295. https://doi.org/10.1016/j.ins.2018.07.013.
Yet, B., A. Constantinou, N. Fenton, M. Neil, E. Luedeling, and K. Shepherd. 2016. “A Bayesian network framework for project cost, benefit and risk analysis with an agricultural development case study.” Expert Syst. Appl. 60 (Oct): 141–155. https://doi.org/10.1016/j.eswa.2016.05.005.
Zhang, Y. 2016. “Selecting risk response strategies considering project risk interdependence.” Int. J. Project Manage. 34 (5): 819–830. https://doi.org/10.1016/j.ijproman.2016.03.001.
Zhang, Y., and Z. P. Fan. 2014. “An optimization method for selecting project risk response strategies.” Int. J. Project Manage. 32 (3): 412–422. https://doi.org/10.1016/j.ijproman.2013.06.006.
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Published In
Journal of Construction Engineering and Management
Volume 148 • Issue 8 • August 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Oct 9, 2021
Accepted: Mar 7, 2022
Published online: May 26, 2022
Published in print: Aug 1, 2022
Discussion open until: Oct 26, 2022
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