Cyber-Physical Systems for Intelligent Civil Infrastructure Asset Management: Smart Sensing, AI, Robotics, and Digital Twins

Civil infrastructure assets, such as bridges, pavements, and buildings, are increasingly challenged by aging, structural deterioration, climate change, rising maintenance demands, workforce shortages, and the urgent need for sustainable and low-carbon development. Addressing these challenges requires a new generation of cyber-physical systems (CPS) that tightly integrate physical infrastructure assets and human operations with emerging technologies, including smart sensing, artificial intelligence (AI), robotics, and digital twins. Through CPS, civil infrastructure asset management can move beyond periodic inspection and reactive maintenance toward continuous monitoring, predictive assessment, intelligent intervention, and sustainability-informed maintenance decision-making.Despite rapid progress in sensing, machine learning, robotic inspection, and infrastructure digitalization, significant gaps remain in the development and deployment of reliable CPS for civil infrastructure. These gaps include limited interoperability among sensing platforms and data acquisition systems, insufficient generalizability and low interpretability of AI models across infrastructure types and environments, uncertainty in translating monitoring data into maintenance decisions, and challenges in validating digital twins under real-world operating conditions. In addition, practical deployment is often constrained by cost, workforce readiness, data quality, cybersecurity, regulatory concerns, and the difficulty of integrating emerging technologies into existing asset management workflows.To address these challenges, this Research Topic encourages contributions that advance both fundamental knowledge and practical applications of CPS for intelligent civil infrastructure asset management. Potential solutions may include multimodal sensing and data fusion, physics-informed and explainable AI, autonomous and semi-autonomous robotic inspection systems, reality capturing and digital twins, low carbon construction material discovery, lifecycle-based sustainability assessment, and human-in-the-loop decision support systems. This collection welcomes original research articles, review papers, and case studies that fit the following topics (but not limited to):- Smart sensing systems for infrastructure inspection, monitoring, and condition assessment- AI and machine learning for damage detection, deterioration prediction, and maintenance decision support- Sensing fusion and multimodal data analytics for bridges, pavements, buildings, and other civil infrastructure assets- Robotics and automated platforms for infrastructure inspection and repair- Digital twins for infrastructure monitoring, diagnosis, prognosis, and lifecycle management- Physics-informed, explainable, trustworthy, and uncertainty-aware AI for infrastructure applications- Sustainable, low-carbon, and lifecycle-oriented infrastructure maintenance planning- Field implementation, validation, benchmarking, and scalability of CPS technologies- Case studies, pilot deployments, and lessons learned from real-world CPS applications in civil infrastructure Topic editors: Md Tahmid Rashid, Jin Fan, Xi Hu Source: https://www.frontiersin.org/research-topics/83545/cyber-physical-systems-for-intelligent-civil-infrastructure-asset-management-smart-sensing-ai-robotics-and-digital-twins