Markov Chain Based Sustainable Risk Management Framework for Infrastructure Projects: A Dynamic Probabilistic Approach
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Infrastructure projects in developing economies face complex, interconnected socioeconomic and environmental risks that traditional static risk management frameworks inadequately address. This study develops a novel Markov Chain based Sustainable Risk Management (MCSRM) framework that integrates dynamic risk state transitions with sustainability principles, enabling predictive risk forecasting across project lifecycles. Utilizing data from eight infrastructure projects in Nigeria's Niger Delta region (n=442 stakeholders), we construct project specific transition probability matrices and employ Bayesian integration to address data scarcity. Results demonstrate that the MCSRM framework achieves 78% predictive accuracy (χ²=12.75, p=0.012) in forecasting risk state evolution, with steady state analysis revealing persistent moderate risk dominance (π₂=0.410.43) across all case studies. Comparative analysis shows the framework outperforms traditional models (Bayesian Networks, Monte Carlo simulations, Bow Tie analysis) in temporal risk tracking (F=4.21, p=0.016, η²=0.12). The Bayesian enhanced model addresses data constraints through expert elicitation synthesis (posterior probability convergence within 0.02 tolerance), demonstrating adaptability for resource limited contexts. Findings indicate that proactive intervention at moderate risk states reduces high-risk escalation probability by 29%, supporting long term project sustainability. This research contributes to dynamic risk management theory by bridging stochastic modeling with Triple Bottom Line sustainability frameworks, offering practitioners an evidence-based tool for adaptive decision making in volatile socio-environmental contexts.
Adeleke, A. Q., Bahaudin, A. Y., & Kamaruddeen, A. M. (2018). Organizational internal factors and construction risk management among Nigerian construction companies. Global Business Review, 19(4), 921938.
https://doi.org/10.1177/0972150918773630
AgyekumMensah, G., & Knight, A. D. (2016). The professionals' perspective on the causes of project delay in the construction industry. Engineering, Construction and Architectural Management, 24(5),
https://doi.org/10.1108/ECAM0320160085
Ale, B. J., Burnap, P., & Slater, D. H. (2015). On the origin of PCDS–(Probability consequence diagrams). Safety Science, 72, 229239. https://doi.org/10.1016/j.ssci.2014.09.003
Aven, T. (2016). Risk assessment and risk management: Review of recent advances on their foundation. European Journal of Operational Research, 253(1), 113.
https://doi.org/10.1016/j.ejor.2015.12.023
Aven, T., & Renn, O. (2010). Risk management and governance: Concepts, guidelines and applications. Springer.
https://doi.org/10.1007/9783642139260
Aziz, N. A. A., & Manab, N. A. (2020). Does enterprise risk management create value? Journal of Advanced Research in Dynamical and Control Systems, 12(2), 17581767.
https://doi.org/10.5373/JARDCS/V12I2/S20201223
Aziz, N. A. A., Manab, N. A., & Othman, S. N. (2016). Sustainable risk management (SRM): An extension of enterprise risk management (ERM). Journal of Business & Social Review in Emerging Economies, 2(1), 99106.
https://doi.org/10.26710/jbsee.v2i1.71
Barbu, V. S., & Limnios, N. (2008). SemiMarkov chains and hidden semiMarkov models toward applications. Springer.
https://doi.org/10.1007/9780387731735
Chapman, C., & Ward, S. (2021). How to manage project opportunity and risk: Why uncertainty management can be a much better approach than risk management (4th ed.). Wiley.
https://doi.org/10.1002/9781119596417
Ching, W. K., Huang, X., Ng, M. K., & Siu, T. K. (2013). Markov chains: Models, algorithms and applications (2nd ed.). Springer.
https://doi.org/10.1007/9781461463122
COREN. (2023). Annual statistical bulletin: Engineering profession in Nigeria. Council for the Regulation of Engineering in Nigeria.
Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE Publications.
De Ruijter, A., & Guldenmund, F. (2016). The bowtie method: A review. Safety Science, 88, 211218. https://doi.org/10.1016/j.ssci.2016.03.001
Ebekozien, A., Aigbavboa, C., & Samsurijan, M. S. (2024). Exploring the barriers to adopting sustainable construction in Nigeria's public housing sector. Sustainability, 16(2), 764.
https://doi.org/10.3390/su16020764
EdjossanSossou, A. M., Deck, O., Al Heib, M., & Verdel, T. (2020). A decision support methodology for assessing the sustainability of natural risk management strategies in urban areas. Natural Hazards and Earth System Sciences, 20(4), 10811098. https://doi.org/10.5194/nhess2010812020
Elkington, J. (1997). Cannibals with forks: The triple bottom line of 21st century business. Capstone Publishing.
Elkington, J. (2018). 25 years ago I coined the phrase "triple bottom line." Here's why it's time to rethink it. Harvard Business Review.
https://hbr.org/2018/06/25yearsagoicoinedthephrasetriplebottomlinehereswhyimgivinguponit
Fenton, N., & Neil, M. (2018). Risk assessment and decision analysis with Bayesian networks (2nd ed.). CRC Press. https://doi.org/10.1201/b21982
Flick, U. (2018). An introduction to qualitative research (6th ed.). SAGE Publications.
Flyvbjerg, B., Ansar, A., Budzier, A., Buhl, S., Cantarelli, C., Garbuio, M., Glenting, C., Holm, M. S., Lovallo, D., Lunn, D., Molin, E., Rønnest, A., Stewart, A., & van Wee, B. (2021). Five things you should know about cost overrun. Transportation Research Part A: Policy and Practice, 143, 245248.
https://doi.org/10.1016/j.tra.2020.12.009
Hillson, D. (2003). Effective opportunity management for projects: Exploiting positive risk.
CRC Press.
https://doi.org/10.1201/9780203913918
Hillson, D., & Simon, P. (2020). Practical project risk management: The ATOM methodology (3rd ed.). Management Concepts Press.
Howard, R. A. (1971). Dynamic probabilistic systems: Markov models (Vol. 1). Wiley.
Kemeny, J. G., & Snell, J. L. (2020). Finite Markov chains (2nd ed.). Springer. (Original work published 1976).
https://doi.org/10.1007/9781468494556
Khakzad, N., Khan, F., & Amyotte, P. (2013). Dynamic safety analysis of process systems by mapping bowtie into Bayesian network. Process Safety and Environmental Protection, 91(12), 4653. https://doi.org/10.1016/j.psep.2012.01.005
King, N. (2012). Doing template analysis. In G. Symon & C. Cassell (Eds.), Qualitative organizational research: Core methods and current challenges (pp. 426450). SAGE Publications. https://doi.org/10.4135/9781526435620.n24
Krippendorff, K. (2013). Content analysis: An introduction to its methodology (3rd ed.). SAGE Publications.
Lagergren, J. (2021). Markov models in evolutionary genomics. In A. Carrieri, R. Utro, & D. Parida (Eds.), Statistical genomics (pp. 193208). Springer. https://doi.org/10.1007/9781071609477_13
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159174.
https://doi.org/10.2307/2529310
Linstone, H. A., & Turoff, M. (Eds.). (2011). The Delphi method: Techniques and applications. New Jersey Institute of Technology. (Original work published 1975)
Manab, N. A., Aziz, N. A. A., & Othman, S. N. (2020). Sustainable risk management (SRM): The development of corporate governance framework. Test Engineering and Management, 82,
Mba, J. C., Meludu, N. T., & Alaneme, G. U. (2019). Application of Markov chain model in forecasting oil spill in the Niger Delta. International Journal of Applied Engineering Research, 14(6), 14131422.
Mousavi, S. M. (2015). Risk identification and assessment in highway construction projects. International Journal of Construction Engineering and Management, 4(6), 262272.
https://doi.org/10.5923/j.ijcem.20150406.06
Nobanee, H., Dilshad, M. N., Al Dhanhani, H. M., Al Neyadi, M. R., Al Qubaisi, S. K., & Al Shamsi, S. S. (2021). Sustainability and risk management: A systematic review. Risks, 9(8), 137.
https://doi.org/10.3390/risks9080137
Norris, J. R. (1998). Markov chains. Cambridge University Press.
https://doi.org/10.1017/CBO9780511810633
Obare, J. O., & Muraya, M. M. (2019). Markov chain model application in the assessment of risks and uncertainties in highway construction projects in Kenya. Journal of Construction Engineering and Project Management, 9(2), 1323.
https://doi.org/10.6106/JCEPM.2019.9.2.013
Omotehinse, A. O., & Omoyi, G. B. (2022). Markov chain theoretic approach to modelling industrial safety in the Nigerian oil and gas industry. Heliyon, 8(8), e10143.
https://doi.org/10.1016/j.heliyon.2022.e10143
Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann.
Peng, D., & Tiong, R. L. K. (2016). Managing model risk in infrastructure PPP projects: Systematic literature review and directions for future research. Journal of Infrastructure Systems, 22(4), 04016024.
https://doi.org/10.1061/(ASCE)IS.1943555X.0000313
PMI. (2017). A guide to the project management body of knowledge (PMBOK® Guide) (6th ed.). Project Management Institute.
Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257286. https://doi.org/10.1109/5.18626
Regona, M., Yigitcanlar, T., Xia, B., & Li, R. Y. M. (2022). Opportunities and adoption challenges of AI in the construction industry: A PRISMA review. Journal of Open Innovation: Technology, Market, and Complexity, 8(1), 45.
https://doi.org/10.3390/joitmc8010045
Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th ed.). Pearson Education.
Talukhaba, A., & Mutiso, S. (2022). Sustainability considerations in project risk management practices in Kenya's construction industry. Sustainability, 14(23), 15676.
https://doi.org/10.3390/su142315676
Ugwu, O. O., Ugochukwu, S. C., Ikechukwu, R. A., & Ekennia, H. O. (2023). Evaluation of risk management practices in the Nigerian construction industry. Journal of Engineering, Project, and Production Management, 13(1), 4558. https://doi.org/10.32738/JEPPM20230005
van Aken, J. E. (2004). Management research based on the paradigm of the design sciences: The quest for fieldtested and grounded technological rules. Journal of Management Studies, 41(2),
https://doi.org/10.1111/j.14676486.2004.00430.x
Vose, D. (2008). Risk analysis: A quantitative guide (3rd ed.). Wiley.
https://doi.org/10.1002/9780470512845
Wang, S. Q., & Strong, D. M. (2014). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 533.
https://doi.org/10.1080/07421222.1996.11518099
Ward, S., & Chapman, C. (2003). Transforming project risk management into project uncertainty management. International Journal of Project Management, 21(2), 97105.
https://doi.org/10.1016/S02637863(01)000801
World Bank. (2023). Infrastructure for development: Meeting the challenge. World Bank Group. https://www.worldbank.org/en/topic/infrastructure
YazoCabuya, E. J., Ibeas, A., & HerreraCuartas, J. A. (2024). Integration of sustainability and risk management in organizations: A multicriteria decision approach. Sustainability, 16(3), 1089.
https://doi.org/10.3390/su16031089
Zhang, L., Wu, X., Skibniewski, M. J., Zhong, J., & Lu, Y. (2021). Bayesiannetworkbased safety risk analysis in construction projects. Reliability Engineering & System Safety, 131, 2939. https://doi.org/10.1016/j.ress.2014.06.006
Zhou, Y., Mao, C., & Zheng, H. (2016). Dynamic modeling of risk propagation in subway construction projects using a coupled Markov chain and Bayesian network. Journal of Construction Engineering and Management, 142(11), 04016063. https://doi.org/10.1061/(ASCE)CO.19437862.0001173
