Methods and Applications of Machine/Deep Learning for Structural Monitoring and Sensing
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Industrial Sensors".
Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 3584
Special Issue Editors
Interests: structural control; advanced large-scale structural testing; smart structures; earthquake engineering
Special Issues, Collections and Topics in MDPI journals
Interests: structural health monitoring; artificial intelligence; information theory; bridge engineering; smart structural control
Special Issues, Collections and Topics in MDPI journals
Interests: structural health monitoring; wireless smart sensor networks; infrastructure management and policies; performance-based monitoring; augmented reality; human–machine interfaces and human cognition
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Advanced learning technologies have further extended the capability of structural sensing and monitoring for infrastructure. Structural-health monitoring aims to provide a more scientific solution to understanding the current performance of structures and to diagnosing structural deterioration before it becomes a catastrophic disaster. A combination of monitoring and sensing strategies is needed to fulfill this goal. Meanwhile, these strategies should address the uncertainties (i.e., modeling errors) and deviations (i.e., measurement noise and sensor faults) that may occur in a complex or large-scale structure. With the aid of machine-/deep-learning technologies, accurate decision making can be carried out by retrievable information and identifiable conditions in the built environment. Consequently, predictive maintenance and structural protection against hazards become realizable and practical.
This Special Issue provides the opportunities to gather studies addressing the theoretical, computational, and experimental methods and applications of machine/deep learning for structural monitoring and sensing. Topics include, but are not limited to:
- Advanced sensing technologies and networks for structural-health monitoring;
- Structural-health monitoring using machine learning;
- Innovative neural network architectures for structural-health monitoring;
- Damage diagnosis and prognosis;
- Quantification and localization;
- Early warning systems;
- Sensor fault detection;
- Computer vision approaches for structural identification, damage detection, and response measurement with machine learning;
- Digital twins;
- Artificial-intelligence-based inspection using UAVs and UGVs.
Dr. Chia-Ming Chang
Prof. Dr. Tzu-Kang Lin
Dr. Fernando Moreu
Guest Editors
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Keywords
- advanced sensing
- structural-health monitoring
- machine and deep learning
- damage detection
- artificial intelligence
- SHM methods and applications
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