Sensing Technologies and Deep Learning Methods for Structural Health Monitoring Systems
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 31 May 2024 | Viewed by 685
Special Issue Editors
2. Director of the Center for Intelligent Infrastructure, Missouri University of Science and Technology, Rolla, MO 65401, USA
3. Director of INSPIRE University Transportation Center, Missouri University of Science and Technology, Rolla, MO 65401, USA
4. Associate Director of Mid-America Transportation Center, University of Nebraska, Lincoln, NE 68588, USA
Interests: structural health monitoring; structural control; interface mechanics and deterioration; multihazard mitigation; bridge inspection and maintenance
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We are pleased to announce a Special Issue focused on the latest advancements at the intersection of deep learning and structural health monitoring (SHM). Sensors have become instrumental in SHM by providing crucial data for assessing the health and integrity of various structures. Recent innovations in deep learning methodologies present a promising avenue to enhance the analysis and interpretation of sensor-generated data for more accurate and efficient SHM. This Special Issue aims to compile state-of-the-art research exploring the integration of deep learning techniques with sensor data to advance structural health monitoring.
We invite researchers and practitioners to submit original research and review articles related to sensing technologies and deep learning methods for structural health monitoring systems. Topics of interest include, but are not limited to:
- Deep learning algorithms for SHM;
- Sensor fusion and multimodal data processing for comprehensive structural assessment;
- Edge computing and real-time applications for SHM;
- Transfer learning and domain adaptation for SHM tasks in various domains;
- Uncertainty quantification and model interpretability in SHM systems.
Prof. Dr. Genda Chen
Dr. Woubishet Taffese
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. Sensors 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 2600 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
- structural health monitoring
- deep learning
- sensor networks
- intelligent monitoring techniques
- computer vision
- damage detection
- Internet of Things
