Artificial Intelligence-Enabled Decision Support Systems for Smart Infrastructure Management (International Journal of Artificial Intelligence Engineering and Systems, 2026)
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.
Keywords: smart infrastructure, decision support systems, artificial intelligence, governance, sustainability, robustness, fairness, digital twins