Submission Deadline: 31 July 2027 View: 107 Submit to Special Issue
Assist. Prof. Li Ai
Email: li.ai@utrgv.edu
Affiliation: Department of Civil Engineering, University of Texas Rio Grande Valley, Edinburg, USA
Research Interests: structural health monitoring, nondestructive testing/evaluation, damage diagnosis and prognosis
Assist. Prof. Zhou Huang
Email: huangzhou@ccsu.edu.cn
Affiliation: School of Civil Engineering, Changsha University, Changsha, China
Research Interests: structural health monitoring; vehicle bridge interaction, multi-scale damage mechanics.
Dr. Zhenkun Li
Email: zhenkun.li@polimi.it
Affiliation: Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Milano, Italy
Research Interests: structural health monitoring; bridge engineering; machine learning; uncertainty quantification
Dr. Yifu Lan
Email: yl2195@cam.ac.uk
Affiliation: Department of Engineering, University of Cambridge, Cambridge, United Kingdom
Research Interests: structural health monitoring, physics-informed AI, vehicle sensing; infrastructure digital twins
Assist. Prof. Kun Feng
Email: kun.feng@aru.ac.uk
Affiliation: School of Engineering and the Built Environment, Anglia Ruskin University, Peterborough, United Kingdom
Research Interests: structural health monitoring, artificial intelligence, digital twins, computational modelling
The increasing demand for resilient, sustainable, and intelligent infrastructure has accelerated the integration of advanced computational modelling, artificial intelligence (AI), and digital twin technologies into civil and transportation engineering. Traditional infrastructure inspection and maintenance strategies are gradually evolving towards data-driven and predictive approaches, supported by rapid advances in sensing technologies, numerical simulation, machine learning, and high-performance computing. These developments provide unprecedented opportunities for real-time structural condition assessment, damage diagnosis, predictive maintenance, and informed asset management throughout the infrastructure lifecycle.
This Special Issue aims to provide a multidisciplinary platform for researchers and practitioners to present recent advances in computational modelling, AI, digital twins, and intelligent decision-support methodologies for infrastructure monitoring and management. The issue welcomes both theoretical developments and practical engineering applications, encouraging contributions that integrate physics-based modelling with data-driven techniques for enhanced reliability, interpretability, and operational efficiency.
The scope includes, but is not limited to, bridges, highways, railways, tunnels, buildings, dams, offshore structures, pipelines, and other critical infrastructure systems. Original research, review papers, and case studies addressing innovative methodologies, computational frameworks, sensing technologies, and intelligent maintenance strategies are all encouraged. The Special Issue seeks to promote interdisciplinary collaboration between computational mechanics, structural engineering, artificial intelligence, and digital engineering communities, thereby supporting the development of next-generation intelligent infrastructure systems.
Suggested Themes
· Computational modelling and numerical simulation for infrastructure systems
· Artificial intelligence and machine learning in structural health monitoring
· Digital twins for infrastructure lifecycle management
· Physics-informed machine learning and hybrid modelling
· Data-driven structural damage detection and localisation
· Signal processing and modal identification techniques
· Sensor fusion and intelligent sensing technologies
· Computer vision and remote sensing for infrastructure inspection
· Predictive maintenance and remaining service life prediction
· Infrastructure asset management and decision-support systems
· Uncertainty quantification, reliability, and risk assessment
· Big data analytics and cloud-based infrastructure monitoring
· Vehicle-bridge interaction and drive-by structural health monitoring
· Digital engineering and smart transportation infrastructure
· High-performance computing and real-time monitoring frameworks
· Applications to bridges, highways, railways, buildings, dams, pipelines, offshore and energy infrastructure
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