Deep Learning-Enhanced Structural Health Monitoring
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".
Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 330
Special Issue Editors
Interests: structural engineering; structural health monitoring; aerospace engineering
Interests: deep learning; computational intelligence; smart sensor networks; quantum computing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Over the past few decades, several methods have been investigated to conduct defect diagnosis and failure analysis for structural systems. However, traditionally adopted strategies for structural health monitoring can be characterized by some limitations, especially related to the need for advanced signal filtering, image processing solutions and management of large volumes of data. Therefore, there has been growing interest in the integration of artificial intelligence techniques, in particular of deep learning methods and deep neural networks, in order to enable automated damage detection, classification, and prediction by effectively analyzing complex data patterns and identifying subtle changes in structural systems. These approaches hold the promise to lead to improved safety, reduced maintenance costs, and improved operational efficiency, benefiting industries such as civil engineering, aerospace, and energy production.
This Special Issue calls for high-quality original research articles, review articles, and technical notes focused on such emergent technologies and the latest advances in the field. Topics may cover broad areas related to the development or enhancement of applied methodologies, new SHM architectures, wireless sensing networks, smart devices, optimal sensors placement, issues related to structural implementation, synthetic data generation and processing, and inter-disciplinary applications.
Dr. Federica Angeletti
Prof. Dr. Massimo Panella
Guest Editors
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Keywords
- deep learning
- convolutional neural networks
- recurrent deep neural networks
- structural health monitoring
- damage detection
- smart sensors
- early detection
- real-time detection
- sensor technology and wireless systems
- signal processing
- intelligent algorithms for data mining
- optimal sensor placement
- synthetic data generation
- pattern recognition
- quantum advantage on simulation and optimization
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