Deep Learning-Enhanced Structural Health Monitoring

Dear Colleagues,

Over the past few decades, several methods have been investigated to conduct defect diagnosis and failure analysis for structural systems. However, traditionally adopted strategies for structural health monitoring can be characterized by some limitations, especially related to the need for advanced signal filtering, image processing solutions and management of large volumes of data. Therefore, there has been growing interest in the integration of artificial intelligence techniques, in particular of deep learning methods and deep neural networks, in order to enable automated damage detection, classification, and prediction by effectively analyzing complex data patterns and identifying subtle changes in structural systems. These approaches hold the promise to lead to improved safety, reduced maintenance costs, and improved operational efficiency, benefiting industries such as civil engineering, aerospace, and energy production.

This Special Issue calls for high-quality original research articles, review articles, and technical notes focused on such emergent technologies and the latest advances in the field. Topics may cover broad areas related to the development or enhancement of applied methodologies, new SHM architectures, wireless sensing networks, smart devices, optimal sensors placement, issues related to structural implementation, synthetic data generation and processing, and inter-disciplinary applications.

Dr. Federica Angeletti
Prof. Dr. Massimo Panella
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 250 words) can be sent to the Editorial Office for assessment.

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.