Practical Applications of Digital-Twin-enabled Risk Management in Infrastructure Projects

Yulin Sun

doi:10.62051/ijgem.v10n6.17

Authors

Yulin Sun School of Project Management, The University of Sydney, Sydney, NSW 2006, Australia

DOI:

https://doi.org/10.62051/ijgem.v10n6.17

Keywords:

Digital twin technology, Infrastructure, Project risk management, Full-lifecycle, Engineering application

Abstract

Infrastructure risk management in China still mainly relies on manual inspections and empirical judgment, which is characterized by incomplete monitoring coverage and insufficient risk forecasting capability, and is no longer able to meet the requirements of refined safety control for modern engineering. The industry is beginning to use digital twins to transform traditional risk control models and optimize the effectiveness of engineering safety management. This paper takes transportation, water conservancy and municipal engineering as the main research domains, and analyzes typical projects including Beixi of Jiulong River, City-wide Underground Pipeline Network of Chongqing, and the Yangtze-to-Han River Water Diversion Project as research samples. This paper explores five core dimensions, including risk identification, assessment, risk response, emergency coordination, and full-lifecycle operation and maintenance. Based on multiple practical engineering projects, this paper summarizes the on-site application patterns of digital twins. According to the publicly available engineering data from the Ministry of Water Resources and the Ministry of Housing and Urban Rural Development, the short-term flood forecasting accuracy for the watershed stands at 90% after the application of this technology, and the timely repair rate of municipal pipeline networks exceeds 98%. This technology reduces missed hazard identification, shortens emergency response time and lowers losses resulting from safety incidents. Practical experience from field applications can provide valuable guidance for similar infrastructure projects.

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References

[1] Hu, X., Olgun, G., & Assaad, R. H. (2024). An intelligent BIM-enabled digital twin framework for real-time structural health monitoring using wireless IoT sensing, digital signal processing, and structural analysis. Expert Systems with Applications, 252, 124204. https://doi.org/10.1016/j.eswa.2024.124204

[2] Sun, Z., Jayasinghe, S., Sidiq, A., Shahrivar, F., Mahmoodian, M., & Setunge, S. (2024). Approach towards the development of digital twin for structural health monitoring of civil infrastructure: A comprehensive review. Sensors, 25(1), 59. https://doi.org/10.3390/s25010059

[3] Zhu, H., Huang, M., Ji, P., Xiao, F., & Zhang, Q. B. (2025). Transforming the maintenance of underground infrastructure through Digital Twins: State of the art and outlook. Tunnelling and Underground Space Technology, 161, 106508. https://doi.org/10.1016/j.tust.2025.106508

[4] Berlato, M., Binni, L., Durmus, D., Gatto, C., Giusti, L., Massari, A., ... & Mirarchi, C. (2025). Digital platforms for the built environment: A systematic review across sectors and scales. Buildings, 15(14), 2432. https://doi.org/10.3390/buildings15142432

[5] Kaewunruen, S., Sresakoolchai, J., Ma, W., & Phil-Ebosie, O. (2021). Digital twin aided vulnerability assessment and risk-based maintenance planning of bridge infrastructures exposed to extreme conditions. Sustainability, 13(4), 2051. https://doi.org/10.3390/su13042051

[6] Zio, E., & Miqueles, L. (2024). Digital twins in safety analysis, risk assessment and emergency management. Reliability Engineering & System Safety, 246, 110040. https://doi.org/10.1016/j.ress.2024.110040

[7] Chen, C., Fu, H., Zheng, Y., Tao, F., & Liu, Y. (2023). The advance of digital twin for predictive maintenance: The role and function of machine learning. Journal of Manufacturing Systems, 71, 581–594. https://doi.org/10.1016/j.jmsy.2023.08.007

[8] Fan, C., Zhang, C., Yahja, A., & Mostafavi, A. (2021). Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management. International Journal of Information Management, 56, 102049. https://doi.org/10.1016/j.ijinfomgt.2020.102049

[9] Liu, M., Fang, S., Dong, H., & Xu, C. (2021). Review of digital twin about concepts, technologies, and industrial applications. Journal of Manufacturing Systems, 58, 346–361. https://doi.org/10.1016/j.jmsy.2021.01.003

[10] Hakimi, O., Liu, H., & Abudayyeh, O. (2024). Digital twin-enabled smart facility management: A bibliometric review. Frontiers of Engineering Management, 11(1), 32–49. https://doi.org/10.1007/s42452-023-00468-7

[11] Wu, D., Zheng, A., Yu, W., Cao, H., Ling, Q., Liu, J., & Zhou, D. (2025). Digital twin technology in transportation infrastructure: a comprehensive survey of current applications, challenges, and future directions. Applied Sciences, 15(4), 1911. https://doi.org/10.3390/app15041911