Global/Local/Hybrid Structural Health Monitoring Technologies
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".
Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 330
Special Issue Editors
Interests: structural health monitoring; damage detection; smart sensor; image processing; soft computing; deep learning
Interests: structural health monitoring; structural dynamics; earthquake engineering; reliability analysis; image processing; damage detection
Special Issues, Collections and Topics in MDPI journals
Interests: structural health monitoring; smart sensors; piezoelectric sensors; temperature effects; transducers; electromechanical impedance; FBG sensors; damage detection; impedance method; wireless sensing; computer vision
Special Issue Information
Dear Colleagues,
As the demand for enhanced structural integrity and operational efficiency continues to rise, the exploration and implementation of cutting-edge structural health monitoring (SHM) technologies have become increasingly crucial. SHM has evolved to become an essential tool used for maintaining civil infrastructure. In addition to global and local methods that have been the mainstay of SHM methodologies, recent advances in hybrid approaches utilizing global and local data show promise in enhancing SHM performances. As civil engineering structures are intrinsically large and complex, with a wide variety of structural damages, global/local/hybrid methods have their unique advantages and particular applications.
This Special Issue aims to establish a comprehensive platform for researchers, engineers, and practitioners by presenting the most recent and advanced SHM technologies, incorporating global, local, and hybrid sensing and data processing. The papers published in this Special Issue will provide valuable insights into state-of-the-art SHM technologies and the direction of research and development in the SHM field.
Potential topics include, but are not limited to:
- Global sensing and data processing for SHM;
- Local sensing and data processing for SHM;
- Hybrid sensing and data processing for SHM;
- Smart sensors and quality assessment of sensor data;
- The application of artificial intelligence for advanced data processing;
- The automation of SHM methodologies;
- Real implementations of SHM technologies in infrastructure.
Prof. Dr. Jeong-Tae Kim
Prof. Dr. Sung-Han Sim
Dr. Thanh-Canh Huynh
Guest Editors
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