Novel Approaches for Structural Health Monitoring II
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".
Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 21780
Related Special Issue: Novel Approaches for Structural Health Monitoring
Special Issue Editor
Interests: structural dynamics; structural health monitoring; machine learning; nonlinear dynamics; signal processing; structural engineering; vibration analysis; biomechanics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The thirty-plus years of progress in the field of structural health monitoring (SHM) have left a paramount impact on our everyday lives. Be it for the monitoring of fixed- and rotary-wing aircrafts, for the preservation of the cultural and architectural heritage, or for the predictive maintenance of long-span bridges or wind farms, SHM has shaped the framework of many engineering fields. Given the current state of quantitative and principled methodologies, it is now possible to rapidly and consistently evaluate the structural safety of industrial machines, modern concrete buildings, historical masonry complexes, etc., to test their capability to serve their intended purpose. However, old unsolved problems as well as new challenges exist. Unmodeled nonlinearities, ineffective sensor placement, and the effects of confounding influences due to operational and environmental variability still harm the effectiveness of state-of-the-art SHM apparatuses. Unprecedented conditions such as hypersonic flight, stricter safety requirements, and aging civil infrastructure pose new challenges for confrontation. Therefore, the aim of this Special Issue is to gather the main contributions of academics and practitioners in civil, aerospace, and mechanical engineering to provide a common ground for structural health monitoring. Studies concerning nondestructive testing, machine learning, signal processing, sensor fusion, vibration-based techniques, and related fields are all welcome, both numerical and experimental.
Prof. Dr. Cecilia Surace
Guest Editor
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Keywords
- structural health monitoring
- nondestructive testing
- machine learning
- vibration-based
- signal processing
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