AI-Powered Digital Twins for Predictive Infrastructure Management

  • Authors

    • P. K. Iyengar Scientific Computing Researcher, BARC, India Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2025PII4R8P

    Published 07-03-2025

  • Artificial Intelligence, Digital Twin, Predictive Infrastructure Management, Machine Learning, Internet of Things, Predictive Maintenance, Smart Cities, Infrastructure Analytics, Deep Learning, Intelligent Asset Management

    Issue

    Vol. 8 No. 2 (2025)

    Section

    Articles

    How to Cite

    [1]
    P. K. Iyengar, “AI-Powered Digital Twins for Predictive Infrastructure Management”, ijmiet, vol. 8, no. 2, pp. 01–15, Jul. 2025, doi: 10.67228/30715628/IJMIET-2025PII4R8P.

  • Abstract

    Smart infrastructure increasingly requires predictive maintenance to improve asset reliability and reduce operational risks. AI-powered Digital Twins integrate IoT, cloud computing, big data, and machine learning to enable real-time monitoring, anomaly detection, asset health assessment, and failure prediction. This paper reviews their architecture, enabling technologies, applications, current research trends, and key challenges, including data quality, interoperability, cybersecurity, and explainability. The study highlights that AI-driven Digital Twins significantly improve maintenance efficiency, reduce costs, enhance safety, and support sustainable, intelligent infrastructure management.

  • References

    [1] F. Tao, Q. Qi, A. Liu, and A. Kusiak, "Data-driven smart manufacturing," Journal of Manufacturing Systems, vol. 48, pp. 157–169, Jul. 2018.

    [2] F. Tao and M. Zhang, "Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing," IEEE Access, vol. 5, pp. 20418–20427, 2017.

    [3] M. Grieves and J. Vickers, "Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems," in Transdisciplinary Perspectives on Complex Systems, Springer, 2017, pp. 85–113.

    [4] E. Glaessgen and D. Stargel, "The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles," in 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Honolulu, HI, USA, 2012.

    [5] A. Rasheed, O. San, and T. Kvamsdal, "Digital Twin: Values, Challenges and Enablers from a Modeling Perspective," IEEE Access, vol. 8, pp. 21980–22012, 2020.

    [6] X. Lu, J. Xu, J. Wang, and Y. Li, "Digital Twin-driven Smart Manufacturing: Connotation, Reference Model, Applications and Research Issues," Robotics and Computer-Integrated Manufacturing, vol. 61, pp. 101837, 2020.

    [7] L. Monostori, "Cyber-physical Production Systems: Roots, Expectations and R&D Challenges," Procedia CIRP, vol. 17, pp. 9–13, 2014.

    [8] Suresh, A. (2024). Auto-BI Frameworks Powered by Generative Artificial Intelligence for Scalable Self-Service Data Analytics in Large Organizations. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(3), 191-201. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I3P119

    [9] A. A. A. Mohamed, Y. H. Lee, and S. H. Hong, "IoT-Based Predictive Maintenance Framework Using Machine Learning Algorithms," IEEE Access, vol. 8, pp. 215497–215509, 2020.

    [10] S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997.

    [11] Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, no. 7553, pp. 436–444, May 2015.

    [12] M. T. Ribeiro, S. Singh, and C. Guestrin, "Why Should I Trust You? Explaining the Predictions of Any Classifier," in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016, pp. 1135–1144.

    [13] S. Lundberg and S.-I. Lee, "A Unified Approach to Interpreting Model Predictions," in Advances in Neural Information Processing Systems (NeurIPS), vol. 30, 2017.

    [14] J. Konecny, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, "Federated Learning: Strategies for Improving Communication Efficiency," in NIPS Workshop on Private Multi-Party Machine Learning, 2016.

    [15] W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, "Edge Computing: Vision and Challenges," IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646, Oct. 2016.

    [16] M. Armbrust et al., "A View of Cloud Computing," Communications of the ACM, vol. 53, no. 4, pp. 50–58, Apr. 2010.

    [17] Veershetty, G. (2023). Risk-adaptive transition and transformation (RATT): A predictive governance framework for SAP cloud migration programs. International Journal of Leading Research Publication, 4(12). https://doi.org/10.70528/IJLRP.v4.i12.2170

    [18] Gajula, S. (2023). A review of anomaly identification in finance frauds using machine learning system. International Journal of Current Engineering and Technology, 13(6), 568–575. https://ijcet.evegenis.org/index.php/ijcet/article/view/820

    [19] Gajula, S. (2024). Adaptive zero trust architecture for securing financial microservices. Computer Fraud & Security, 2024(12), 643–655. https://doi.org/10.52710/cfs.845

    [20] Gajula, S. (2024). Cybersecurity risk prediction using graph neural networks. Journal of Information Systems Engineering and Management

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