Artificial Intelligence-Enabled Decision Support Systems for Smart Infrastructure Management
Keywords:
smart infrastructure, decision support systems, artificial intelligence, governance, sustainability, robustness, fairness, digital twinsAbstract
The increasing complexity and interdependency of critical infrastructure systems, from transportation and energy to water and telecommunications, demand a new generation of decision support tools that can integrate heterogeneous data streams, anticipate disruptions, and optimize operations under deep uncertainty. Artificial intelligence-enabled decision support systems (AI-DSS) offer transformative potential for smart infrastructure management by coupling real-time sensing, predictive analytics, and prescriptive optimization within layered socio-technical architectures. This paper presents a system-level examination of AI-DSS for infrastructure, moving beyond algorithmic novelty to address structural trade-offs, governance frameworks, robustness requirements, fairness constraints, sustainability imperatives, and deployment challenges. It delineates the architectural foundations that link edge sensing, digital twins, and multi-model inference pipelines, and it explores how these architectures shape accountability, transparency, and equity across public and private stakeholders. Detailed analytical discussion covers concept drift and adversarial resilience in safety-critical environments, the distributive justice implications of algorithmic resource allocation, life-cycle environmental costs of large-scale AI systems, and the institutional reconfigurations necessary for brownfield integration. Through cross-domain case illustrations spanning transport network control, water distribution management, and energy grid balancing, the paper reveals emergent tensions between efficiency, interpretability, and adaptive governance. It concludes by identifying forward-looking policy levers, including algorithmic auditing mandates, adaptive regulatory sandboxes, and multi-level participatory design processes that can align AI-DSS with long-term public values. The analysis underscores that the successful embedding of artificial intelligence into infrastructure decision-making is less a computational problem than a socio-institutional challenge requiring careful navigation of resilience, sustainability, and justice.
References
1. Bibri, S. E., & Krogstie, J. (2017). Smart sustainable cities of the future: An extensive interdisciplinary literature review. Sustainable Cities and Society, 31, 183–212.
2. Spencer, B. F., Jr., Hoskere, V., & Narazaki, Y. (2019). Advances in computer vision-based civil infrastructure inspection and monitoring. Engineering, 5(2), 199–222.
3. Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415.
4. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35.
5. Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management, 53, 102104.
6. Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., ... & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.
7. Gunes, V., Peter, S., Givargis, T., & Vahid, F. (2014). A survey on concepts, applications, and challenges in cyber-physical systems. KSII Transactions on Internet and Information Systems, 8(12), 4242–4268.
8. Janssen, M., van der Voort, H., & Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of Business Research, 70, 338–345.
9. Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014). A survey on concept drift adaptation. ACM Computing Surveys, 46(4), 1–37.
10. Motta, R. C., de Oliveira, K. M., & Travassos, G. H. (2019). On challenges of machine learning applications in critical systems. ACM Computing Surveys, 52(4), 1–33.
11. Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165, 633–705.
12. Rasheed, A., San, O., & Kvamsdal, T. (2020). Digital twin: Values, challenges and enablers from a modeling perspective. IEEE Access, 8, 21980–22012.
13. Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646.
14. Amershi, S., Cakmak, M., Knox, W. B., & Kulesza, T. (2014). Power to the people: The role of humans in interactive machine learning. AI Magazine, 35(4), 105–120.
15. Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L. M., Rothchild, D., ... & Dean, J. (2021). Carbon emissions and large neural network training. arXiv preprint arXiv:2104.10350.
16. Linkov, I., Eisenberg, D. A., Bates, M. E., Chang, D., Convertino, M., Allen, J. H., ... & Seager, T. P. (2013). Measurable resilience for actionable policy. Environmental Science & Technology, 47(18), 10108–10110.
17. Carroll, J. M., & Rosson, M. B. (2007). Participatory design in community informatics. Design Studies, 28(3), 243–261.
18. Blaschke, T., Merschdorf, H., & Pramboeck, A. (2020). Standards for geographic information and smart city frameworks. ISPRS International Journal of Geo-Information, 9(10), 612.
19. Rinaldi, S. M., Peerenboom, J. P., & Kelly, T. K. (2001). Identifying, understanding, and analyzing critical infrastructure interdependencies. IEEE Control Systems Magazine, 21(6), 11–25.
20. Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660.
21. Wang, Z., & Srinivasan, R. (2017). A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models. Renewable and Sustainable Energy Reviews, 75, 796–808.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Artificial Intelligence Engineering and Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
