Cutting-Edging Technologies and Application of Structural Health Monitoring & Nondestructive Testing

Dear Colleagues,

Over the last several decades, structural health monitoring (SHM)  and nondestructive testing techniques have been commonly applied to structures such as bridges, large buildings, dams, and various machines. The objective of SHM is to diagnose faults in structures by quantitatively and continuously monitoring their structural and environmental conditions with sensor networks. As a result, these sensor networks accumulate a large amount of data that require effective methods with regard to retrieval and processing. Worse still, as in-service sensors and associated transmission hardware are usually subjected to harsh environments, the monitoring data inevitably contain various anomalies, and it is too laborious to detect and correct these data anomalies manually. Hence, there is a pressing need to handle the ‘big data’ problem of SHM in terms of data anomaly detection and fault diagnosis. Recently, deep learning has attracted much attention because of its superiority with respect to accuracy and robustness when applied in complex problems, especially in problems wherein the volume of data is huge. Many studies have shown that the combination of deep learning and SHM is rather promising. However, relevant research in this field is still in its infancy, and there is a need for new perspectives and methods when combining deep learning and SHM for use in data anomaly detection and fault diagnosis.

Topic of Interest:

In light of the current progress in the fields of SHM and deep learning, this Special Issue aims to collect state-of-the-art contributions on the latest research and applications, up-to-date issues, and challenges regarding the big data problem of SHM. We invite researchers from academia and industry to submit their high-quality research and practical findings in using deep learning to detect data anomalies and diagnose structural faults or damages for SHM purposes. Topics of interest for this Special Issue include, but are not limited to:

• NDT sensors, detectors, and sources: ultrasound, acoustical emission, X-ray, thermography, eddy currents, EMAT etc;
  Data anomaly detection for SHM using deep learning;
• Data reconstruction for SHM using deep learning;
• Fault diagnosis for SHM using deep learning;
• Structural damage identification based on deep learning;
• Structural load identification based on deep learning;
• Structural pattern recognition employing deep learning;
• Infrastructure management using deep learning;
• Practical validations of deep learning in the field of SHM;
• Engineering applications of SHM;
  Defect detection and localization methods;
  Signal and image processing.

Prof. Dr. Danhui Dan
Dr. Ye Xia
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.

• NDT sensors, detectors, and sources: ultrasound, acoustical emission, X-ray, thermography, eddy currents, EMAT etc.
• data anomaly detection for SHM using deep learning
• data anomaly detection for SHM using deep learning Data reconstruction for SHM using deep learning
• fault diagnosis for SHM using deep learning
• structural damage identification based on deep learning
• structural load identification based on deep learning Structural pattern recognition employing deep learning
• infrastructure management using deep learning
• practical validations of deep learning in the field of SHM
• engineering applications of SHM
• defect detection and localization methods
• signal and image processing