Vol. 1 No. 1 (2026)
Bayesian Network Models for Risk Assessment in Road Infrastructure Projects
Abstract
Road infrastructure projects in Sub-Saharan Africa are characterised by persistent and substantial cost overruns (mean overrun ratio 1.46 across 22 South Sudan projects reviewed in this study), schedule delays (mean duration ratio 1.52), and quality deficiencies that collectively reduce the economic return on public investment and undermine donor confidence. Traditional risk assessment methods — risk scoring matrices, deterministic sensitivity analysis, and scalar Monte Carlo simulation — treat risk factors as independent, fail to propagate new evidence systematically, and cannot quantify the joint probability of cascading multi-risk scenarios. This paper develops, calibrates, and applies a Bayesian Network (BN) model for comprehensive probabilistic risk assessment of road infrastructure projects in a post-conflict, resource-constrained context. The BN comprises 15 nodes and 27 directed edges encoding causal relationships among exogenous root causes (climate variability, geological conditions), controllable root causes (design quality, contractor capability), intermediate risk factors (budget availability, material supply, site conditions, labour productivity), risk events (construction delay, cost overrun, quality deficiency, safety incident), and project outcomes (project failure, pavement performance, cost to complete). Conditional probability tables (CPTs) are estimated using a combination of Bayesian parameter learning from the 22-project dataset, expert elicitation following the Sheffield method, and published meta-analytic priors from the infrastructure cost overrun literature. The model is implemented in R using the bnlearn package and validated through leave-one-out cross-validation (log-loss = 0.35, Brier score = 0.13, AUROC = 0.89). Evidence propagation analy
Read the Full Article
The HTML galley is loaded below for inline reading and better discovery.
How to Cite
Keywords
Research Snapshot
Desktop reading viewReferences
- AfDB. (2022). Transport Infrastructure Assessment: Highway Corridors in South Sudan. African Development Bank, Abidjan. RDGS/2022/004.
- Ahiaga-Dagbui, D. D., Love, P. E. D., Smith, S. D., & Ackermann, F. (2017). Toward a systemic view to reduce the incidence of construction cost overruns. Journal of Construction Engineering and Management, 143(7), 04017020.
- Aziz, R. F. (2013). Ranking of delay factors in construction projects after Egyptian revolution. Alexandria Engineering Journal, 52(3), 387–406.
- Cox, L. A. (2008). What's wrong with risk matrices? Risk Analysis, 28(2), 497–512.
- Duijm, N. J. (2015). Recommendations on the use and design of risk matrices. Safety Science, 76, 21–31.
- Embrechts, P., McNeil, A., & Straumann, D. (2002). Correlation and dependence in risk management: properties and pitfalls. In M. Dempster (Ed.), Risk Management: Value at Risk and Beyond. Cambridge University Press.
- Fang, C., Marle, F., & Xie, M. (2017). Applying causal reasoning to project risk analysis. Project Management Journal, 48(1), 56–73.
- Flyvbjerg, B., Holm, M. S., & Buhl, S. L. (2018). Underestimating costs in public works projects: error or lie? Journal of the American Planning Association, 68(3), 279–295.
- ISO 31000:2018. Risk Management — Guidelines. International Organization for Standardization, Geneva.
- Jensen, F. V., & Nielsen, T. D. (2007). Bayesian Networks and Decision Graphs (2nd ed.). Springer, New York.
- Khakzad, N., Khan, F., & Amyotte, P. (2013). Quantitative risk analysis of offshore drilling operations: a Bayesian approach. Safety Science, 57, 108–117.
- Koller, D., & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge.
- Love, P. E. D., Ahiaga-Dagbui, D., Welde, M., & Odeck, J. (2019). Light rail transit cost performance: opportunities for learning. Transportation Research Part A, 119, 473–487.
- Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco.
- Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press.
- Scutari, M. (2010). Learning Bayesian networks with the bnlearn R package. Journal of Statistical Software, 35(3), 1–22.
- Sheffield Elicitation Framework (SHELF). (2019). University of Sheffield, Department of Probability and Statistics. http://www.tonyohagan.co.uk/shelf
- Špačková, O., & Straub, D. (2012). Dynamic Bayesian network for probabilistic modeling of tunnel excavation processes. Computer-Aided Civil and Infrastructure Engineering, 28(1), 1–21.
- MoRB. (2022). South Sudan Primary Road Network Statistical Yearbook 2022. Ministry of Roads and Bridges, Juba.
- MoRB. (2023). Annual Project Performance Report 2023. Project Management Unit, Juba.
- World Bank. (2021). South Sudan Infrastructure Diagnostics: Road and Bridge Sector. World Bank, Washington D.C.
- Zhang, L., Wu, X., Qin, Y., Skibniewski, M. J., & Liu, W. (2016). Towards a fuzzy Bayesian network-based approach for safety risk analysis of tunnel-induced pipeline damage. Risk Analysis, 36(2), 278–301.