Recent advancements in artificial intelligence (AI), including new strategies for capturing, storing, analyzing, and interpreting data, have the potential to revolutionize the nondestructive evaluation (NDE) of materials and structures in many ways. The field of NDE can significantly benefit from new machine learning (ML) and deep learning (DL) algorithms, increased computer processing power, the accessibility of mobile internet, the availability of substantial amounts of high-quality data, and modern data storage technology.
The focus of this special issue is to consolidate recent research activities that utilize existing or new AI/ML methods to advance the state of NDE. These include but are not limited to: (a) the design and development of novel algorithms adapted for NDE data acquisition, analysis, modeling, or prediction; (b) implementation of existing AI/ML algorithms to new NDE application; and (c) data-driven methods for extracting relevant information from NDE data sets compared to conventional approaches.
To be considered for this special issue, manuscripts are required to demonstrate a significant contribution to the field of NDE with carefully documented methodology and results, reporting model performance compared to conventional approaches, and including relevant code in public depositories (such as GitHub) following common practices in the field of ML.
Scope:
- Artificial intelligence
- Machine learning
- Deep learning
- Computer vision
- Signal and image processing
- Big data
- Time series analysis
- Surrogate modeling
- Pattern recognition
