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 21
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
Manuscripts should be submitted online at mdpi.longhoe.net by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- structure safety
- damage detection
- artificial intelligence
- health monitoring
- machine learning
