Intelligent Damage Detection of Materials and Structural Health Monitoring Technology
A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Advanced Materials Characterization".
Deadline for manuscript submissions: 20 March 2025 | Viewed by 141
Special Issue Editors
2. College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China
Interests: optimization; soft computing; structural health monitoring; damage detection; evolutionary computation; fuzzy logic; swarm algorithms; deep learning; manufacturing; welding; evolutionary algorithms; damage identification; neural networks; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: structural health monitoring; structural damage identification; vibro-acoustics
Special Issues, Collections and Topics in MDPI journals
Interests: artificial Intelligence; bioinformatics; building materials; computational mechanics; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent decades, a wide variety of structural materials have been employed in the construction of critical structural systems such as bridges, buildings, and transportation networks. However, these materials deteriorate over time due to long-term use, environmental exposure, and operational loading. Early-stage defects and damage in structural materials can propagate, compromising the safety and integrity of the structures. Consequently, there is increasing demand for advanced structural health monitoring (SHM) technologies to assess the condition of structural materials in aging structures. Recent advancements in sensing technologies, data analytics, artificial intelligence, machine learning, and computational techniques have opened new avenues for innovative SHM solutions. This Special Issue aims to compile cutting-edge research on intelligent damage detection and SHM methodologies specifically focused on structural materials.
Topics of interest include, but are not limited to, the following:
- Characterization and property prediction of structural materials for SHM;
- Intelligent damage detection in structural materials (concrete, steel, and composites);
- Defect identification in structural materials using deep learning;
- Signal processing and modal analysis for SHM of structural materials;
- Physics-informed machine learning for structural materials;
- Failure prognostics and remaining useful life prediction of materials;
- Condition assessment and integrity evaluation of structural materials;
- Structural model updating;
- Failure prognostics and early warning.
Dr. Nizar Faisal Alkayem
Prof. Dr. Wei Xu
Prof. Dr. Panagiotis G. Asteris
Guest Editors
Manuscript Submission Information
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Keywords
- structural health monitoring
- damage detection
- structural materials
- material characterization
- defect identification
- concrete crack detection
- deep learning
- material property prediction
- failure prognostics
- condition assessment
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