Artificial Intelligence for Resilient and Sustainable Infrastructure Systems

Infrastructure systems, including transportation networks, energy and water lifelines, buildings, and urban services, are increasingly challenged by climate extremes, cascading failures, and long-term sustainability pressures. Conventional approaches to infrastructure design, monitoring, and management often depend on static models and fragmented data, limiting their capacity to represent system dynamics, interdependencies, and uncertainty. Recent advances in artificial intelligence (AI) offer new opportunities to enhance infrastructure resilience and sustainability through data-driven prediction, optimization, and decision support. Progress in machine learning, AI-enabled digital twins, and large language models (LLMs) enables the integration and analysis of heterogeneous data sources (e.g., IoT sensors, remote sensing, and operational records) in near real time. Together, these developments position AI as a transformative tool for understanding complex infrastructure behavior, supporting adaptive decision-making, and improving the performance of interconnected systems across the built environment.



The goal of this Research Topic is to advance the theoretical foundations, methodological developments, and practical applications of artificial intelligence for resilient and sustainable infrastructure systems. While AI has been increasingly applied to tasks such as forecasting, anomaly detection, and optimization, substantial challenges remain in developing AI frameworks that are robust to uncertainty, scalable across interconnected infrastructure systems, and applicable throughout planning, operation, and recovery phases. This Research Topic seeks to address these challenges by bringing together interdisciplinary research that examines how AI can support infrastructure decision-making under dynamic conditions, extreme events, and long-term sustainability constraints. Key objectives include investigating AI methods for fusing multi-source and real-time data, enhancing situational awareness, and enabling adaptive responses to disruptions; exploring advanced AI techniques (e.g., LLMs, graph-based learning, reinforcement learning, and hybrid data-physics approaches) for infrastructure analysis and system-level assessment; and evaluating AI-enabled solutions through real-world case studies and large-scale deployments. Collectively, this Research Topic aims to promote AI-driven approaches that substantially improve the resilience, adaptability, and sustainability of infrastructure systems.


Topics of interest include, but are not limited to:

· AI for infrastructure risk assessment, monitoring, and system optimization

· AI-enabled digital twins integrating sensor data, remote sensing, and operational information

· Graph-based approaches for modeling interconnected infrastructure systems

· AI supporting buildings, transportation, energy, water, and urban infrastructure

· LLMs and knowledge-based AI for infrastructure analysis, planning, and policy support

· AI approaches addressing sustainability objectives, lifecycle impacts, and resource efficiency

This Research Topic welcomes Original Research Articles, Review Papers, Perspective, Opinion, and Conceptual Articles that advance understanding of how AI can enable resilient, adaptive, and sustainable infrastructure systems.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review

Keywords: Artificial Intelligence, Infrastructure Resilience, sustainability, Agentic AI, Digital Twins, Disaster Informatics, Risk Management

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.