Artificial Intelligence and Sensing Techniques for Structural Health Monitoring, Damage Detection and Control
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".
Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 327
Special Issue Editor
Interests: vibration; energy harvesting; structural health monitoring and control; smart materials and structures; dynamical systems; risk quantification and reliability analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Artificial Intelligence is quickly becoming an important aspect surrounding the topic of assessing mechanical and civil structures. While a wide range of approaches have been considered in this regard and when using different methodologies, significant questions and gaps remain around their assessment and understanding, in particular around the topic of the robustness of these methods as well as in the comparability and creation of robust benchmarks. The detection and creation of appropriate features have also become important, and so has being cognizant of the fundamental physics that often guide the responses of these structures.
To this effect, this Special Issue intends to capture the current global efforts to address these aspects. The issue considers but is not limited to the following topics in structural health monitoring, damage detection, and control:
- Deep learning, machine learning, and artificial neural networks;
- Physics-based learning approaches;
- The robust selection and detection of features of interest;
- Sensor fusion and wireless sensors;
- Sensor fault detection;
- Real-time applications;
- Digital twinning;
- Synthetic data;
- Hybrid methods and experiments;
- Automated systems;
- Benchmark applications and case studies;
- Hardware implementation.
Dr. Vikram Pakrashi
Guest Editor
Manuscript Submission Information
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Keywords
- structural health monitoring
- damage detection
- feature engineering
- structural control
- deep learning
- machine learning
- neural networks
