Guest Editors
Dr. Tao Jin
Email: jintao@zjut.edu.cn
Affiliation: College of Civil Engineering, Zhejiang University of Technology, Hangzhou 310023, China
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Research Interests: computer vision based structural deformation monitoring, deep learning based structural damage recognition, structural health monitoring
Assoc. Prof. You-hua Su
Email: ceyhsu@xjtu.edu.cn
Affiliation: Department of Civil Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Homepage:
Research Interests: structural health monitoring
Assist. Prof. Yang Ding
Email: ceyangding@zju.edu.cn
Affiliation: Department of Civil Engineering, Hangzhou City University, Hangzhou 310015, China
Homepage:
Research Interests: structural health monitoring, uncertainty quantification, wind engineering, corrosion-fatigue analysis, reliability assessment
Prof. Xiao-wei Ye
Email: cexwye@zju.edu.cn
Affiliation: Department of Civil Engineering, Zhejiang University, Hangzhou 310027, China
Homepage:
Research Interests: structural health monitoring, intelligent underground structure, soil-structure interaction, artificial intelligence civil engineering
Summary
Modern transportation infrastructure, encompassing bridges, tunnels, roads, rails, and associated structures, is under escalating pressure. Aging infrastructure, increasing traffic volumes and loads, and extreme weather intensify the need for uninterrupted service, demand robust solutions for ensuring structural integrity, safety, and long-term sustainability. Traditional visual and periodic manual inspections are often insufficient for the timely detection of hidden structural damage. This Special Issue seeks advanced research on Structural Health Monitoring (SHM) for transportation infrastructure. Our goal is to achieve real-time condition assessment, early damage detection and predictive maintenance by leveraging advanced sensors, artificial intelligence/machine learning analysis, non-destructive testing methods and digital twins.
Topics of interest include, but are not limited to:
· AI/ML technology for damage identification and service life prediction of transportation facilities.
· NDT (ultrasonic, GPR) for bridges, tunnels, rails, roads.
· Digital twins integrated with BIM/GIS.
· Application of drone and robotic technologies for detailed inspection of hard-to-reach areas.
· Field deployments and practical case studies.
Keywords
structural health monitoring (SHM), transportation infrastructure, artificial intelligence (AI), machine learning (ML), non-destructive testing (NDT), digital twin, data analysis.
Published Papers
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