Guest Editors
Dr. Tonghao Zhang
Email: txzhang@thorntontomasetti.com
Affiliation: Department of Civil, Materials, and Environmental Engineering, University of Illinois Chicago, Chicago, 60612, United States
Homepage:
Research Interests: forensics engineering, acoustic emission, ultrasonics
Dr. Chenxi Xu
Email: cxu@thorntontomasetti.com
Affiliation: Department of Civil, Materials, and Environmental Engineering, University of Illinois Chicago, Chicago, 60612, United States
Homepage:
Research Interests: acoustic emission, pipelines, guided waves, nuclear structures
Dr. Lin Du
Email: ldu3@uic.edu
Affiliation: Department of Biomedical Engineering, University of Illinois Chicago, Chicago, 60612, United States
Homepage:
Research Interests: machine learning, AI, statistical modelling, high-performance computing
Dr. Mehdi Shahzamanian
Email: shahzamm@mailbox.sc.edu
Affiliation: McNair Center for Aerospace Innovation and Research, University of South Carolina, Columbia, 29201, United States
Homepage:
Research Interests: machine learning, AI, statistical modelling, high-performance computing
Summary
This special collection will highlight cutting-edge structural health monitoring (SHM) and AI-enabled techniques applied to real-world infrastructure under industry constraints. As critical assets face growing threats from natural hazards—such as hurricanes, wildfires, earthquakes, and tornadoes—as well as mechanical degradation like corrosion, fatigue, and material failure, SHM has become essential for emergency response, risk mitigation, and asset management.
Recent advances in sensing technologies, data acquisition, and AI/machine learning have enabled more accurate, scalable, and real-time damage detection. These tools are increasingly deployed in bridges, buildings, power plants, and transportation networks to support rapid decision-making and long-term resilience planning.
This collection invites contributions on innovative SHM systems, AI-driven diagnostics, field case studies, and multi-hazard monitoring solutions. Submissions should emphasize practical implementation, especially in projects focused on emergency preparedness, critical asset protection, and life-cycle management.
By showcasing technologies that bridge the gap between research and application, this collection aims to serve infrastructure owners, engineers, and decision-makers navigating complex operational environments.
Keywords
AI, machine learning, power plants, failure analysis, fitness of service
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