Structural Health Monitoring, Damage Diagnosis, and Damage Prognosis: New Technologies and Applications

Dear Colleagues,

In recent years, the integration of machine learning algorithms within structural mechanics frameworks has revolutionized several aspects, from making numerical simulations faster to identifying patterns in diagnostic signals for damage diagnosis and prognosis.

This Special Issue aims to collate the most recent advances in the field of artificial intelligence techniques for structural health monitoring, focusing on damage diagnosis and prognosis. We invite submissions presenting theoretical contributions and applications based on experimental and/or numerical case studies.

Topics of interest include but are not limited to:

1. Innovative applications of deep learning techniques for damage diagnosis and prognosis;
2. Exploring the transition from traditional supervised methods to unsupervised machine learning approaches;
3. Advancements in generative artificial intelligence and its implications for structural health monitoring;
4. Delving into the interpretability of intelligent algorithms.

Dr. Luca Lomazzi
Dr. Francesco Cadini
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com 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.

• machine learning
• generative artificial intelligence
• unsupervised learning
• explainability
• structural health monitoring
• damage diagnosis
• damage prognosis