Dynamic risk assessment of international engineering projects from the life cycle perspective

    Xu Duan Info
    Pengcheng Xiang Info
    Shuai Feng Info
DOI: https://doi.org/10.3846/jcem.2026.25212

Abstract

Compared to other types of engineering projects, the development of international engineering projects (IEPs) is particularly challenging due to the uncertainties and risks posed by external environments. The risks of IEPs arise from both external threats and inherent vulnerabilities. As projects progress, these risks evolve dynamically, posing significant challenges for risk management. Existing studies on IEPs have focused mainly on the static assessment of risks at a certain stage or types of risk, but few have considered the dynamic and interconnected nature of risks. This study employs a comprehensive risk assessment framework for IEPs using dynamic Bayesian network. Specifically, the fuzzy set method and Monte Carlo simulation create the prior probabilities of root nodes and conditional probabilities of intermediate and leaf nodes. Then, propagation analysis is conducted to uncover the dynamic evolution of risks throughout the project life cycle. The proposed framework can capture the evolution of critical risks in life cycle. Internal and external risks in the three phases of construction, operation, and maintenance have been identified. Based on the findings from a case study, targeted management strategies are proposed to address critical risks, providing practical guidance for project managers to optimize risk management practices.

Keywords:

international engineering projects, risk assessment, dynamic Bayesian network, Monte Carlo simulation

How to Cite

Duan, X., Xiang, P., & Feng, S. (2026). Dynamic risk assessment of international engineering projects from the life cycle perspective. Journal of Civil Engineering and Management, 32(2), 133–147. https://doi.org/10.3846/jcem.2026.25212

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March 13, 2026
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References

Andrić, J. M., Wang, J., Zou, P. X., Zhang, J., & Zhong, R. (2019). Fuzzy logic–based method for risk assessment of belt and road infrastructure projects. Journal of Construction Engineering and Management, 145(12), Article 04019082. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001721

Arabi, S., Eshtehardian, E., & Shafiei, I. (2022). Using Bayesian networks for selecting risk-response strategies in construction projects. Journal of Construction Engineering and Management, 148(8), Article 04022067. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002310

Bai, X. P., & Zhao, Y. H. (2021). A novel method for occupational safety risk analysis of high-altitude fall accident in architecture construction engineering. Journal of Asian Architecture and Building Engineering, 20(3), 314–325. https://doi.org/10.1080/13467581.2020.1796675

Bai, L., Kang, S., Zhang, K., Zhang, B., & Pan, T. (2024). Modeling for external stakeholder risk assessment of project portfolios. Engineering, Construction and Architectural Management, 31(2), 737–766. https://doi.org/10.1108/ECAM-01-2022-0010

Bai, L., Zhao, X., Kang, S., Ma, Y., & Zhang, B. (2025). Dynamic assessment of stakeholder conflict risk for R&D project portfolios. Engineering, Construction and Architectural Management, 32(4), 2342–2368. https://doi.org/10.1108/ECAM-03-2023-0301

Bouraima, M. B., Alimo, P. K., Agyeman, S., Sumo, P. D., Lartey-Young, G., Ehebrecht, D., & Qiu, Y. (2023). Africa's railway renaissance and sustainability: Current knowledge, challenges, and prospects. Journal of Transport Geography, 106, Article 103487. https://doi.org/10.1016/j.jtrangeo.2022.103487

Bugalia, N., Maemura, Y., & Ozawa, K. (2021). Characteristics of enhanced safety coordination between high-speed rail operators and manufacturers. Reliability Engineering & System Safety, 216, Article 107995. https://doi.org/10.1016/j.ress.2021.107995

Chang, T., Deng, X., Zuo, J., & Yuan, J. (2018). Political risks in Central Asian countries: Factors and strategies. Journal of Management in Engineering, 34(2), Article 04017059. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000588

Chen, Y., Zhu, L., Hu, Z., Chen, S., & Zheng, X. (2022). Risk propagation in multilayer heterogeneous network of coupled system of large engineering project. Journal of Management in Engineering, 38(3), Article 04022003. https://doi.org/10.1061/(ASCE)ME.1943-5479.0001022

Duan, X., Zhao, X., Liu, J., Zhang, S., & Luo, D. (2021). Dynamic risk assessment of the overseas oil and gas investment environment in the big data era. Frontiers in Energy Research, 9, Article 638437. https://doi.org/10.3389/fenrg.2021.638437

Duan, X., Hao, J., & Niu, Y. (2023). Research on intelligent decision-making method of investment scheme of high-speed railway construction project. IEIE Transactions on Smart Processing & Computing, 12(6), 483–494. https://doi.org/10.5573/IEIESPC.2023.12.6.483

Elbarkouky, M. M., Fayek, A. R., Siraj, N. B., & Sadeghi, N. (2016). Fuzzy arithmetic risk analysis approach to determine construction project contingency. Journal of Construction Engineering and Management, 142(12), Article 04016070. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001191

Govindan, K., & Jepsen, M. B. (2016). Supplier risk assessment based on trapezoidal intuitionistic fuzzy numbers and ELECTRE TRI-C: a case illustration involving service suppliers. Journal of the Operational Research Society, 67(2), 339–376. https://doi.org/10.1057/jors.2015.51

Guo, X., Ji, J., Khan, F., Ding, L., & Tong, Q. (2021). A novel fuzzy dynamic Bayesian network for dynamic risk assessment and uncertainty propagation quantification in uncertainty environment. Safety Science, 141, Article 105285. https://doi.org/10.1016/j.ssci.2021.105285

Haimes, Y. Y. (2018). Risk modeling of interdependent complex systems of systems: Theory and practice. Risk Analysis, 38(1), 84–98. https://doi.org/10.1111/risa.12804

Halabi, A., Kenett, R. S., & Sacerdote, L. (2017). Using dynamic Bayesian networks to model technical risk management efficiency. Quality and Reliability Engineering International, 33(6), 1179–1196. https://doi.org/10.1002/qre.2186

Haruna, A., & Jiang, P. (2022). Adaptability analysis of design for additive manufacturing by using fuzzy Bayesian network approach. Advanced Engineering Informatics, 52, Article 101613. https://doi.org/10.1016/j.aei.2022.101613

Hetemi, E., Gemünden, H. G., & Meré, J. O. (2020). Embeddedness and actors’ behaviors in large-scale project life cycle: Lessons learned from a high-speed rail project in Spain. Journal of Management in Engineering, 36(6), Article 05020014. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000849

Li, J., Zhang, J., & Suo, W. (2019). Risk assessment in cross-border transport infrastructure projects: A fuzzy hybrid method considering dual interdependent effects. Information Sciences, 488, 140–157. https://doi.org/10.1016/j.ins.2019.03.028

Li, H., Zhong, Y., & Fan, C. (2020). Reducing the social risks of transnational railway construction: A discussion on the formation mechanism of host country people’s coping behaviors. Engineering, Construction and Architectural Management, 28(6), 1657–1682. https://doi.org/10.1108/ECAM-04-2020-0232

Li, J., Yang, Z., He, H., Guo, C., Chen, Y., & Zhang, Y. (2024). Risk causation analysis and prevention strategy of working fluid systems based on accident data and complex network theory. Reliability Engineering & System Safety, 252, Article 110445. https://doi.org/10.1016/j.ress.2024.110445

Liu, K., Wang, M., Cao, Y., Zhu, W., Wu, J., & Yan, X. (2018). A comprehensive risk analysis of transportation networks affected by rainfall‐induced multihazards. Risk Analysis, 38(8), 1618–1633. https://doi.org/10.1111/risa.12968

Liu, B., Liu, S., Xue, B., Lu, S., & Yang, Y. (2021). Formalizing an integrated decision-making model for the risk assessment of carbon capture, utilization, and storage projects: From a sustainability perspective. Applied Energy, 303, Article 117624. https://doi.org/10.1016/j.apenergy.2021.117624

Mazher, K. M., Chan, A. P., Zahoor, H., Khan, M. I., & Ameyaw, E. E. (2018). Fuzzy integral–based risk-assessment approach for public–private partnership infrastructure projects. Journal of Construction Engineering and Management, 144(12), Article 04018111. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001573

Moret, Y., & Einstein, H. H. (2016). Construction cost and duration uncertainty model: Application to high-speed rail line project. Journal of Construction Engineering and Management, 142(10), Article 05016010. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001161

Nwadigo, O., Naismith, N. N., Ghaffarianhoseini, A., Ghaffarian Hoseini, A., & Tookey, J. (2021). Dynamic Bayesian network modelling for predicting adaptability of time performance during time influencing factors disruptions in construction enterprise. Engineering, Construction and Architectural Management, 28(10), 2994–3013. https://doi.org/10.1108/ECAM-05-2020-0371

Piao, Y., Xu, W., Wang, T. K., & Chen, J. H. (2021). Dynamic fall risk assessment framework for construction workers based on dynamic Bayesian network and computer vision. Journal of Construction Engineering and Management, 147(12), Article 04021171. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002200

Project Management Institute. (2017). The standard for portfolio management (4th ed.). Newtown Square, Pennsylvania, USA.

Ruan, D., Bian, J., Wang, Y., Wu, J., & Gu, Z. (2024). Identification of groundwater pollution sources and health risk assessment in the Songnen Plain based on PCA-APCS-MLR and trapezoidal fuzzy number-Monte Carlo stochastic simulation model. Journal of Hydrology, 632, Article 130897. https://doi.org/10.1016/j.jhydrol.2024.130897

Sadeghi, M., Mahmoudi, A., & Deng, X. (2023). Blockchain technology in construction organizations: risk assessment using trapezoidal fuzzy ordinal priority approach. Engineering, Construction and Architectural Management, 30(7), 2767–2793. https://doi.org/10.1108/ECAM-01-2022-0014

Sadeh, H., Mirarchi, C., & Pavan, A. (2021). Integrated approach to construction risk management: Cost implications. Journal of Construction Engineering and Management, 147(10), Article 04021113. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002140

Singh, K., Kaushik, M., & Kumar, M. (2022). Integrating α-cut interval based fuzzy fault tree analysis with Bayesian network for criticality analysis of submarine pipeline leakage: A novel approach. Process Safety and Environmental Protection, 166, 189–201. https://doi.org/10.1016/j.psep.2022.07.058

Sun, S., Gao, G., Li, Y., Zhou, X., Huang, D., Chen, D., & Li, Y. (2022). A comprehensive risk assessment of Chinese high-speed railways affected by multiple meteorological hazards. Weather and Climate Extremes, 38, Article 100519. https://doi.org/10.1016/j.wace.2022.100519

Thron, E., Faily, S., Dogan, H., & Freer, M. (2024). Human factors and cyber-security risks on the railway–the critical role played by signalling operations. Information & Computer Security, 32(2), 236–263. https://doi.org/10.1108/ICS-05-2023-0078

Wang, C., Loo, S. C., Yap, J. B. H., & Abdul-Rahman, H. (2019). Novel capability-based risk assessment calculator for construction contractors venturing overseas. Journal of Construction Engineering and Management, 145(10), Article 04019059. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001696

Wang, Q., Liu, K., Wang, M., & Koks, E. E. (2021). A river flood and earthquake risk assessment of railway assets along the belt and road. International Journal of Disaster Risk Science, 12, 553–567. https://doi.org/10.1007/s13753-021-00358-2

Wu, X., & Xu, F. (2021). Detection model for unbalanced bidding in railway construction projects: Considering the risk of quantity variation. Journal of Construction Engineering and Management, 147(7), Article 04021055. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002058

Xiang, P., Xia, X., & Pang, X. (2024). An integrated risk assessment method for cross-regional mega construction projects. Engineering, Construction and Architectural Management, 31(6), 2369–2391. https://doi.org/10.1108/ECAM-06-2022-0534

Xiao, Y., Wan, A., Li, Y., Elahi, E., & Peng, B. (2024). Evaluating the evolution of investment risks in belt and road energy projects: A case study of Belarus’ M5 thermal power station. International Journal of Energy Research, 2024, Article 9322649. https://doi.org/10.1155/er/9322649

Xin, X., Liu, K., Yang, Z., Zhang, J., & Wu, X. (2021). A probabilistic risk approach for the collision detection of multi-ships under spatiotemporal movement uncertainty. Reliability Engineering & System Safety, 215, Article 107772. https://doi.org/10.1016/j.ress.2021.107772

Yang, L., Lou, J., & Zhao, X. (2021). Risk response of complex projects: Risk association network method. Journal of Management in Engineering, 37(4), Article 05021004. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000916

Ye, J., Koopialipoor, M., Zhou, J., Armaghani, D. J., & He, X. (2021). A novel combination of tree-based modeling and Monte Carlo simulation for assessing risk levels of flyrock induced by mine blasting. Natural Resources Research, 30, 225–243. https://doi.org/10.1007/s11053-020-09730-3

Zai, W., He, Y., & Wang, H. (2023). Risk prediction method for renewable energy investments abroad based on Cloud-DBN. Sustainability, 15(14), Article 11297. https://doi.org/10.3390/su151411297

Zhang, B., Bai, L., Zhang, K., Kang, S., & Zhou, X. (2023). Dynamic assessment of project portfolio risks from the life cycle perspective. Computers & Industrial Engineering, 176, Article 108922. https://doi.org/10.1016/j.cie.2022.108922

Zuo, F., & Zhang, K. (2018). Selection of risk response actions with consideration of secondary risks. International Journal of Project Management, 36(2), 241–254. https://doi.org/10.1016/j.ijproman.2017.11.002

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2026-03-13

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How to Cite

Duan, X., Xiang, P., & Feng, S. (2026). Dynamic risk assessment of international engineering projects from the life cycle perspective. Journal of Civil Engineering and Management, 32(2), 133–147. https://doi.org/10.3846/jcem.2026.25212

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