The Application of Machine Learning in Structural Health Monitoring
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 June 2024 | Viewed by 3373
Special Issue Editor
Special Issue Information
Dear Colleagues,
For decades, continuous efforts have been made in the area of structural health monitoring (SHM) due to its crucial role in real-time health state awareness and safety evaluation for various engineering structures. With its typical interdisciplinary nature, SHM embraces the advances in solid mechanics, sensor technology, signal processing, hardware design, etc. In particular, the recent success of machine learning (ML) that has been proven in a wide range of fields offers SHM new opportunities to be applied with enhanced applicability, accuracy, and reliability. To boost the application of ML in SHM tasks, however, some unique problems need to be considered. For example, the complex mapping relationship between monitored signals and health state characteristics (e.g., damage features), the insufficient and unbalanced data amount for healthy/unhealthy state classification, and the severe interference from uncertainties and noises due to the on-line service conditions of structures.
In this Special Issue, we invite worldwide researchers to publish their original works highlighting the state-of-the-art application of machine learning in structural health monitoring. The topics of submitted articles can cover both fundamental algorithms as well as lasting ML applications in aerospace, civil engineering, the automobile industry, machinery, and so on. The welcome topics include (among others):
- Data generation and augmentation;
- Robust model training to adapt to application environments;
- Novel SHM-related knowledge obtained from data mining;
- Uncertainty identification, propagation, and control in SHM process;
- Image/pattern recognition using SHM/NDT techniques.
We look forward to receiving your contributions.
Dr. Hao Xu
Guest Editor
Manuscript Submission Information
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Keywords
- structural health monitoring
- machine learning
- deep learning
- damage identification
- state sensing and evaluation
