Sensor Networks for Structural Health Monitoring
1American University of Beirut, Beirut, Lebanon
2University of Adelaide, Adelaide, Australia
3Monash University, Clayton, Australia
4Institute of Fluid Flow Machinery, Polish Academy of Sciences, Gdansk, Poland
Sensor Networks for Structural Health Monitoring
Description
Structural Health Monitoring (SHM) systems provide an automated and continuous monitoring of aerospace, mechanical, and civil structures. This concept requires sensors to be embedded in or mounted on various components of the structure, to collect data in order to predict the current state and remaining life of the structure. Sensors can be clustered according to where they are placed and organized to provide information about different types of failure modes. The SHM systems may also have the ability to conduct an assessment and advise on methods of repair.
Many issues are associated with the implementation of SHM technology, such as sensor selection and placement on the structure, data communication and management, data analytics (feature extraction), and visualization (data fusion).
The objective of this Special Issue is to create a forum of discussion, for research scientists and engineers working in the area of SHM, advanced sensor technologies, sensor placement (ultrasonic transducers, vibrations sensors, etc.), and data analytics. The Special Issue also provides a platform to discuss the challenges associated with sensor integration and possible solutions to enhance its application in real structures. We invite researchers to submit both original research and review articles on any type of sensors towards applications of the SHM systems, innovative research and reviews.
Potential topics include but are not limited to the following:
- Advanced sensor design and development for SHM
- Wired and wireless sensing for SHM
- Contact and noncontact sensors for flaw detection
- Self-powered sensors for SHM applications
- Sensor network design for continuous monitoring
- Sensor placement strategies and optimization–failure assessment within the network for more robust designs
- Sensor integration on structures and impact on the operational conditions
- Feature extraction and data fusion of data collected from the sensor networks for damage detection and assessment