Generative AI in Infrastructure Management

The rapid advancement of Generative Artificial Intelligence (GenAI)—encompassing Large Language Models (LLMs), Vision Language Models (VLMs), and multimodal foundation models—has opened new horizons for the management, analysis, and operation of infrastructure systems. These foundational models, trained on extensive multimodal datasets, offer unprecedented capabilities in reasoning, prediction, simulation, and automated decision-making.

In today’s data-rich and interconnected infrastructure environments, GenAI enables seamless integration and analysis of geospatial, visual, sensor, and textual data. Applications range from condition assessment and risk prediction to resource allocation, safety monitoring, compliance inspection, design optimization, and project planning. The combination of multimodal learning and domain-specific adaptation has the potential to address enduring challenges in efficiency, scalability, resilience, transparency, and equity—fundamentally transforming how transportation, construction, energy, and other critical built environment domains are managed.

Aims and Scope

This Research Topic will showcase state-of-the-art research, case studies, and technical innovations that harness GenAI’s capabilities to realize the next generation of infrastructure management systems. We especially invite empirical studies, methodological advances, and real-world demonstrations that move beyond conceptual discussions.

Submissions may address, but are not limited to, the following themes:

o AI-enabled safety monitoring, predictive maintenance, and risk assessment
o AI-assisted compliance, inspection, and quality assurance systems
o Project planning, resource allocation, and engineering decision support
o Integration of GenAI with IoT, GIS, BIM, and sensor data
o Prompt engineering and domain adaptation for infrastructure applications
o Interpretability, transparency, and responsible GenAI deployment
o Edge applications, real-time analytics, and operational scalability
o AI-augmented stakeholder engagement, collaboration, and policy development

Original research articles, systematic reviews, methodological papers, case studies, and data papers are welcome. We especially encourage submissions demonstrating empirical results, new toolkits or datasets, and those analyzing the societal, ethical, and managerial implications of GenAI adoption in the built environment.

Invitation

This collection aims to foster interdisciplinary collaboration among engineering management, computer science, policy, and domain experts. By providing a platform for knowledge sharing and debate, the Topic will advance technical capabilities, managerial practices, and ethical guardrails for GenAI-powered infrastructure—inspiring transparent, trustworthy, and sustainable innovation in the sector.

Article types and fees

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

• Brief Research Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• ... View all formats

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:

• Brief Research Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review

Keywords: Generative AI, Large Language Models, Vision Language Models, Foundation Models, GPT, Infrastructure, Construction Management, Transportation systems

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.