Digital Twins and Artificial Intelligence in Transportation Infrastructure

Dear Colleagues,

The convergence of Digital Twins (DTs) and Artificial Intelligence (AI) presents transformative opportunities for the planning, monitoring, and management of transportation infrastructure. This Special Issue focuses on applied research and innovations that harness these technologies to create smarter, more resilient, and data-driven infrastructure systems. Of growing interest is the role of Large Language Models (LLMs) in augmenting infrastructure decision-making through natural language interfaces, policy analysis, and the extraction of actionable insights from unstructured data.

We welcome contributions that explore AI- and LLM-powered DT applications in the design, construction, operation, and maintenance of roads, bridges, tunnels, and public transit systems. Topics of interest include predictive maintenance using AI-driven sensor analytics; real-time asset monitoring and simulation; infrastructure risk assessment under climate stressors; construction progress tracking with computer vision; and optimization of infrastructure investments using reinforcement learning. Studies on the integration of LLMs for interpreting engineering documentation, automating compliance checks, supporting stakeholder engagement, and enabling policy scenario modeling are particularly encouraged.

This Special Issue invites interdisciplinary research from civil engineering, urban systems, and data science communities. By highlighting practical use cases, methodological advances, and system-level implications, the Issue aims to advance the adoption of AI- and LLM-enhanced Digital Twins in transportation infrastructure management.

Dr. Mahdi Aghaabbasi
Guest Editor

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