Special Issue "Non-destructive Testing and Evaluation for Civil Infrastructures"
A special issue of Infrastructures (ISSN 2412-3811).
Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 3572
Interests: concrete Pavement; non destructive tests; slag paste
Interests: non-destructive testing; construction; crack detection in concrete
Special Issues, Collections and Topics in MDPI journals
A comprehensive structural condition assessment and structural safety of civil infrastructure (bridges, roads, buildings, etc.) is not possible without evaluating the material properties (steel, concrete, timer, masonry, etc.) and detecting surface and subsurface defects. Nondestructive evaluation and testing (NDE and T) methods such as impact echo, ground penetrating radar, ultrasonic surface waves, infrared thermography, etc. have been used for this task for the past half century. Despite decades of effort for NDE and T implementation in structural engineering, though, their applications are still somewhat limited, since the raw data associated with these techniques require user expertise. Introducing automation to NDE and T data analysis could alleviate this issue and would pave the way for more popular implementations of the NDE and T techniques in future. Using artificial intelligence and signal and image processing, especially deep learning models, has shown substantial advantages over traditional data analysis; however, they are either relatively new to the practice or are completely untested for NDE and T techniques. This issue could be associated to the lack of available and reliable ground truth. The editor invites the civil and structural engineering community (researchers, engineers, NDE and T manufacturers, and users) to submit their solutions to the aforementioned issues. The scope of this Special Issue includes but is not limited to the followings:
- Data papers: Introducing and sharing annotated NDE and T datasets (with reliable ground truth);
- Technical papers: Implementing of artificial intelligence for NDE and T data analysis;
- Technical papers: Decision making using data fusion techniques for two or more NDE and T methods;
- Technical papers: Data augmentation techniques to generate realistic NDE and T data for deep learning implementation;
- Review papers: History of using artificial intelligence for NDE and T techniques, challenges, and potentials.
Dr. Alireza Joshaghani
Dr. Sattar Dorafshan
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. Infrastructures is an international peer-reviewed open access monthly 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 1600 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.
- Ground-penetrating radar (GPR)
- Impact echo (IE)
- Ultrasonic surface waves (USW)
- Infrared thermography (IRT)
- Signal processing
- Image processing
- Deep learning
- Ground truth