Damage Monitoring and Defect Identification Based on Deep/Machine Learning (2nd Edition)
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".
Deadline for manuscript submissions: 25 January 2025 | Viewed by 138
Special Issue Editor
Interests: high-performance concrete; structural analysis; intelligent detection
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue is a continuation of our previous Special Issue, entitled “Damage Monitoring and Defect Identification Based on Deep/Machine Learning”.
As the final barrier for humankind, civil structures constantly confront hazards, such as winds, earthquakes, floods, and even manmade machinery or vehicles. Excessive loading, fatigue, undesired vibrations, deformations, collapses, and previous losses also remind us that structural safety is never a one-size-fits-all task. For these reasons, structural health monitoring, damage detection, risk forecast, and reliability assessment bear paramount socioeconomic importance.
On the cusp of the digital era, several AI techniques, particularly data-driven optimization, deep/machine learning, and reduced-order modeling, have made breakthroughs in many applications. The interdisciplinary integration of civil engineering and data science has already shown great potential. Applying AI to structural safety and damage monitoring is also one of the hottest topics in civil/wind/earthquake/environmental/structural engineering.
This Special Issue is dedicated to highlighting the state-of-the-art advances and latest applications of data-driven/AI techniques in structural safety and relevant fields. We welcome high-quality and original work addressing, but not limited to, the following topics:
Advances in data-driven theories and algorithms that show potential in civil applications;
Applications of data-driven theories and algorithms in civil problems concerning structural safety and health monitoring;
Methods, regardless of whether they are numerical, experimental, field, or analytical in nature, for structure safety and health monitoring;
Case studies of damage detection, risky forecast, design optimization, and reliability assessment using computer-aided techniques;
All other interdisciplinary efforts solving civil engineering problems with data/computer methods.
Prof. Dr. Zengshun Chen
Guest Editor
Manuscript Submission Information
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Keywords
- structure safety
- damage detection
- artificial intelligence
- health monitoring
- machine learning
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