New Sensor-Based Methods for Structural Health Monitoring-2nd Edition
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".
Deadline for manuscript submissions: 20 October 2024 | Viewed by 751
Special Issue Editors
Interests: structural health monitoring; condition monitoring; piezoelectric transducers; PZT; data science; wind turbines
Special Issues, Collections and Topics in MDPI journals
Interests: structural health monitoring; pattern recognition; condition monitoring; sensors; digital design; robotics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We are delighted to introduce the second edition of our Special Issue focusing on the evolving realm of structural health monitoring (SHM). With the rapidly advancing integration of technology in both civilian and defense infrastructure, the significance of SHM in devising intelligent structures has become more pronounced than ever before.
Central to the efficacy of SHM is the ability to harness accurate, actionable data. To do that, deploying advanced sensor systems and networks and applying sophisticated signal processing, data fusion, and interpretation techniques are necessary. Whether it is about delivering immediate, real-time insights or comprehensive offline analyses, our mission is to create SHM systems that minimize false positives and preemptively thwart potential calamities.
For this Special Issue, we are eagerly seeking groundbreaking research and applications that bolster the evolution of SHM. We welcome submissions delineating fresh perspectives, innovative methodologies, and impactful applications. Topics of interest encompass, but are not limited to:
- Advanced damage classification techniques;
- Innovations in sensor fault detection;
- Development and applications of intelligent structures;
- Next-generation smart sensors and their integrations;
- Cutting-edge data preprocessing and normalization strategies;
- Expanding and optimizing sensor network systems;
- Novel pattern recognition algorithms tailored for SHM;
- Breakthroughs in machine learning for SHM applications;
- Pioneering multivariate analyses;
- Leading-edge data-driven algorithms and models;
- IoT advancements and its convergences in SHM;
- Techniques for environmental and operational data compensation.
Dr. Francesc Pozo
Dr. Diego Alexander Tibaduiza Burgos
Guest Editors
Manuscript Submission Information
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