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Review Article

Digital Twins and AI in Infrastructure Engineering: A Global Review of Risk-Informed Design, Operations, and Maintenance

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Abstract

As global infrastructure systems become increasingly complex and vulnerable, the integration of Digital Twins (DT) and Artificial Intelligence (AI) has emerged as a transformative strategy for risk-informed design, operations, and maintenance. However, a significant research gap remains in understanding the global patterns, integration challenges, and practical impact of DT-AI systems across different infrastructure sectors. This systematic review, guided by the PRISMA framework, synthesizes findings from 126 peer-reviewed studies sourced from Scopus, Web of Science, and IEEE Xplore. Analysis revealed that 68% of implementations focus on predictive maintenance and real-time monitoring, while only 12% address early-stage design optimization highlighting an imbalance in lifecycle focus. Furthermore, projects that applied AI-enhanced DTs achieved up to 30% reduction in unplanned maintenance events and improved infrastructure lifespan predictions by an average of 22%. Case studies from Singapore, the UK, Norway, and the US demonstrate real-world benefits in city planning, structural health monitoring, and transportation. Despite these successes, key barriers persist, including data interoperability, cybersecurity vulnerabilities, high implementation costs, and insufficient regulatory standards. This review underscores the need for cross-sectoral collaboration, global policy frameworks, and inclusive innovation strategies to fully leverage DT-AI capabilities in building resilient, adaptive infrastructure.

Keywords:

AI in Infrastructure Digital Twins Global Review Infrastructure Engineering

Article information

Journal

Scientific Journal of Engineering, and Technology

Volume (Issue)

2(2), (2025)

Pages

63-70

Published

14-08-2025

How to Cite

Ogunleye, E., Anyaene, K., Oladetan, J. O., Lawal, A. B., Okeke, F. C., Ogunbule, O. O., & Eromosele, E. I. (2025). Digital Twins and AI in Infrastructure Engineering: A Global Review of Risk-Informed Design, Operations, and Maintenance. Scientific Journal of Engineering, and Technology, 2(2), 63-70. https://doi.org/10.69739/sjet.v2i2.808

References

Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A. (2019). Applications of Artificial Intelligence in Transport: An Overview. Sustainability, 11(1), Article 1. https://doi.org/10.3390/su11010189 DOI: https://doi.org/10.3390/su11010189

Alfaro-Viquez, D., Zamora-Hernandez, M., Fernandez-Vega, M., Garcia-Rodriguez, J., & Azorin-Lopez, J. (2025a). A Comprehensive Review of AI-Based Digital Twin Applications in Manufacturing: Integration Across Operator, Product, and Process Dimensions. Electronics, 14(4), Article 4. https://doi.org/10.3390/electronics14040646

Alfaro-Viquez, D., Zamora-Hernandez, M., Fernandez-Vega, M., Garcia-Rodriguez, J., & Azorin-Lopez, J. (2025b). A Comprehensive Review of AI-Based Digital Twin Applications in Manufacturing: Integration Across Operator, Product, and Process Dimensions. Electronics, 14(4), Article 4. https://doi.org/10.3390/electronics14040646 DOI: https://doi.org/10.3390/electronics14040646

Alnaser, A. A., Maxi, M., & Elmousalami, H. (2024). AI-Powered Digital Twins and Internet of Things for Smart Cities and Sustainable Building Environment. Applied Sciences, 14(24), Article 24. https://doi.org/10.3390/app142412056 DOI: https://doi.org/10.3390/app142412056

Ammar, A., Nassereddine, H., AbdulBaky, N., AbouKansour, A., Tannoury, J., Urban, H., & Schranz, C. (2022). Digital Twins in the Construction Industry: A Perspective of Practitioners and Building Authority. Frontiers in Built Environment, 8. https://doi.org/10.3389/fbuil.2022.834671 DOI: https://doi.org/10.3389/fbuil.2022.834671

Aragón, A., Arquier, M., Tokdemir, O. B., Enfedaque, A., Alberti, M. G., Lieval, F., Loscos, E., Pavón, R. M., Novischi, D. M., Legazpi, P. V., & Yagüe, Á. (2025). Seeking a Definition of Digital Twins for Construction and Infrastructure Management. Applied Sciences, 15(3), Article 3. https://doi.org/10.3390/app15031557 DOI: https://doi.org/10.3390/app15031557

Callcut, M., Cerceau Agliozzo, J.-P., Varga, L., & McMillan, L. (2021). Digital Twins in Civil Infrastructure Systems. Sustainability, 13(20), Article 20. https://doi.org/10.3390/su132011549 DOI: https://doi.org/10.3390/su132011549

Daraba, D., Pop, F., & Daraba, C. (2024). Digital Twin Used in Real-Time Monitoring of Operations Performed on CNC Technological Equipment. Applied Sciences, 14(22), Article 22. https://doi.org/10.3390/app142210088 DOI: https://doi.org/10.3390/app142210088

Deng, Z., Huang, M., Wan, N., & Zhang, J. (2023). The Current Development of Structural Health Monitoring for Bridges: A Review. Buildings, 13(6), Article 6. https://doi.org/10.3390/buildings13061360 DOI: https://doi.org/10.3390/buildings13061360

Dihan, Md. S., Akash, A. I., Tasneem, Z., Das, P., Das, S. K., Islam, Md. R., Islam, Md. M., Badal, F. R., Ali, Md. F., Ahamed, Md. H., Abhi, S. H., Sarker, S. K., & Hasan, Md. M. (2024). Digital twin: Data exploration, architecture, implementation and future. Heliyon, 10(5), e26503. https://doi.org/10.1016/j.heliyon.2024.e26503 DOI: https://doi.org/10.1016/j.heliyon.2024.e26503

El Bilali, H., Strassner, C., & Ben Hassen, T. (2021). Sustainable Agri-Food Systems: Environment, Economy, Society, and Policy. Sustainability, 13(11), Article 11. https://doi.org/10.3390/su13116260 DOI: https://doi.org/10.3390/su13116260

Elahi, M., Afolaranmi, S. O., Martinez Lastra, J. L., & Perez Garcia, J. A. (2023a). A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment. Discover Artificial Intelligence, 3(1), 43. https://doi.org/10.1007/s44163-023-00089-x

Elahi, M., Afolaranmi, S. O., Martinez Lastra, J. L., & Perez Garcia, J. A. (2023b). A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment. Discover Artificial Intelligence, 3(1), 43. https://doi.org/10.1007/s44163-023-00089-x DOI: https://doi.org/10.1007/s44163-023-00089-x

Ficili, I., Giacobbe, M., Tricomi, G., & Puliafito, A. (2025). From Sensors to Data Intelligence: Leveraging IoT, Cloud, and Edge Computing with AI. Sensors, 25(6), Article 6. https://doi.org/10.3390/s25061763 DOI: https://doi.org/10.3390/s25061763

Florido-Benítez, L. (2024). The Use of Digital Twins to Address Smart Tourist Destinations’ Future Challenges. Platforms, 2(4), Article 4. https://doi.org/10.3390/platforms2040016 DOI: https://doi.org/10.3390/platforms2040016

Jang, K., Song, T., Kim, D., Kim, J., Koo, B., Nam, M., Kwak, K., Lee, J., & Chung, M. (2023). Analytical Method for Bridge Damage Using Deep Learning-Based Image Analysis Technology. Applied Sciences, 13(21), Article 21. https://doi.org/10.3390/app132111800 DOI: https://doi.org/10.3390/app132111800

Lawal, O., Elechi, K., Adekunle, F., Farinde, O., Kolapo, T., Igbokwe, C., Elechi, U., Victoria, & Chikezie, O. (2025). A Review on Artificial Intelligence and Point-of-Care Diagnostics to Combat Antimicrobial Resistance in Resource-Limited Healthcare Settings like Nigeria: Review Article. Journal of Pharma Insights and Research, 3(2), Article 2. https://doi.org/10.69613/reeh4906 DOI: https://doi.org/10.69613/reeh4906

Lawal, O., Oyebamiji, H. O., Kelenna, I. J., Chioma, F. J., Oyefeso, E., Adeyemi, B. I., Foster-Pagaebi, E., & Moses, E. F. (2025). A Review on Usage of Digital Health Literacy to Combat Antibiotic Misuse and Misinformation in Nigeria: Review Article. Journal of Pharma Insights and Research, 3(2), Article 2. https://doi.org/10.69613/dja1jc18 DOI: https://doi.org/10.69613/dja1jc18

Lawal, O. P., Egwuatu, E. C., Akanbi, K. O., Orobator, E. T., Eweje, O. Z., Omotayo, E. O., Igbokwe, C. ...., & Chibueze, E. S. (2025). Fighting Resistance With Data: Leveraging Digital Surveillance to Address Antibiotic Misuse in Nigeria. Path of Science, 11(3), Article 3. https://doi.org/10.22178/pos.115-25 DOI: https://doi.org/10.22178/pos.115-25

Lawal, O. P., Igwe, E. P., Olosunde, A., Chisom, E. P., Okeh, D. U., Olowookere, A. K., Adedayo, O. A., Agu, C. P., Mustapha, F. A., Odubo, F., & Orobator, E. T. (2025). Integrating Real-Time Data and Machine Learning in Predicting Infectious Disease Outbreaks: Enhancing Response Strategies in Sub-Saharan Africa. Asian Journal of Microbiology and Biotechnology, 10(1), 147–163. https://doi.org/10.56557/ajmab/2025/v10i19371 DOI: https://doi.org/10.56557/ajmab/2025/v10i19371

Li, J., Liu, Z., Han, G., Demian, P., & Osmani, M. (2024). The Relationship Between Artificial Intelligence (AI) and Building Information Modeling (BIM) Technologies for Sustainable Building in the Context of Smart Cities. Sustainability, 16(24), Article 24. https://doi.org/10.3390/su162410848 DOI: https://doi.org/10.3390/su162410848

Mchirgui, N., Quadar, N., Kraiem, H., & Lakhssassi, A. (2024a). The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review. Applied Sciences, 14(23), Article 23. https://doi.org/10.3390/app142310933

Mchirgui, N., Quadar, N., Kraiem, H., & Lakhssassi, A. (2024b). The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review. Applied Sciences, 14(23), Article 23. https://doi.org/10.3390/app142310933 DOI: https://doi.org/10.3390/app142310933

Olaitan, O. F., Ayeni, S. O., Olosunde, A., Okeke, F. C., Okonkwo, U. U., Ochieze, C. G., Chukwujama, O. V. ...., & Akatakpo, O. N. (2025). Quantum Computing in Artificial Intelligence: A Review of Quantum Machine Learning Algorithms. Path of Science, 11(5), Article 5. https://doi.org/10.22178/pos.117-25 DOI: https://doi.org/10.22178/pos.117-25

Palley, B., Poças Martins, J., Bernardo, H., & Rossetti, R. (2025). Integrating Machine Learning and Digital Twins for Enhanced Smart Building Operation and Energy Management: A Systematic Review. Urban Science, 9(6), Article 6. https://doi.org/10.3390/urbansci9060202 DOI: https://doi.org/10.3390/urbansci9060202

Sadiq, R., Nahiduzzaman, K. M., & Hewage, K. (2020). Infrastructure at the Crossroads–Beyond Sustainability. Frontiers in Sustainable Cities, 2. https://doi.org/10.3389/frsc.2020.593908 DOI: https://doi.org/10.3389/frsc.2020.593908

Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. Sn Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x DOI: https://doi.org/10.1007/s42979-021-00592-x

Siddiqui, F. H., Thaheem, M. J., & Abdekhodaee, A. (2023). A Review of the Digital Skills Needed in the Construction Industry: Towards a Taxonomy of Skills. Buildings, 13(11), Article 11. https://doi.org/10.3390/buildings13112711 DOI: https://doi.org/10.3390/buildings13112711

Sustainability | February-2 2025—Browse Articles. (n.d.). Retrieved July 20, 2025, from https://www.mdpi.com/2071-1050/17/4

Wang, G., & Ke, J. (2024). Literature Review on the Structural Health Monitoring (SHM) of Sustainable Civil Infrastructure: An Analysis of Influencing Factors in the Implementation. Buildings, 14(2), Article 2. https://doi.org/10.3390/buildings14020402 DOI: https://doi.org/10.3390/buildings14020402

Yaacoub, J.-P. A., Salman, O., Noura, H. N., Kaaniche, N., Chehab, A., & Malli, M. (2020). Cyber-physical systems security: Limitations, issues and future trends. Microprocessors and Microsystems, 77, 103201. https://doi.org/10.1016/j.micpro.2020.103201 DOI: https://doi.org/10.1016/j.micpro.2020.103201

Yitmen, I., Almusaed, A., Hussein, M., & Almssad, A. (2025). AI-Driven Digital Twins for Enhancing Indoor Environmental Quality and Energy Efficiency in Smart Building Systems. Buildings, 15(7), Article 7. https://doi.org/10.3390/buildings15071030 DOI: https://doi.org/10.3390/buildings15071030

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