Advance in Road and Pavement Engineering

Dear Colleagues,

Road and pavement engineering is rapidly evolving toward a data-driven and intelligence-enabled paradigm, fueled by breakthroughs in advanced sensing, large-scale data analytics, and artificial intelligence. Emerging technologies—including LiDAR, 3D vision, hyperspectral imaging, ground-penetrating radar, unmanned aerial platforms, and connected vehicle data—are enabling high-resolution, non-contact, and network-level monitoring of pavement condition and infrastructure performance. At the same time, advanced computational approaches such as machine learning, computer vision, multimodal data fusion, and digital twins are reshaping how infrastructure systems are modeled, analyzed, and managed.

Beyond traditional inspection and evaluation, there is a growing shift toward predictive, adaptive, and autonomous decision-making in pavement maintenance and operation. Optimization-based scheduling, lifecycle management, and intelligent maintenance strategies are increasingly supported by data-centric frameworks. Notably, the emergence of large AI models and intelligent agents provides new opportunities to integrate perception, reasoning, and decision-making into unified, end-to-end solutions for road infrastructure systems.

This Special Issue aims to attract high-quality contributions that address cutting-edge developments in sensing technologies, data analytics, intelligent modeling, and decision-making for road infrastructure. We particularly encourage submissions that explore the integration of foundation models, AI agents, and multimodal data with traditional engineering approaches. Interdisciplinary studies, real-world applications, and scalable solutions are especially welcome.

Topics of interest include, but are not limited to, the following:

• Advanced sensing and non-contact pavement inspection
• Multimodal data fusion and infrastructure perception
• AI-driven pavement condition assessment and prediction
• Digital twins and smart infrastructure systems
• Intelligent maintenance, scheduling, and lifecycle optimization
• Large models and agent-based approaches for infrastructure management

Dr. Yishun Li
Prof. Dr. Yuchuan Du
Dr. Zihang Weng
Guest Editors

Manuscript Submission Information

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