Intelligent Sensor Technologies for Predictive Maintenance and Structural Health Monitoring
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".
Deadline for manuscript submissions: 28 February 2025 | Viewed by 369
Special Issue Editors
2. College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China
Interests: optimization; soft computing; structural health monitoring; damage detection; evolutionary computation; fuzzy logic; swarm algorithms; deep learning; manufacturing; welding; evolutionary algorithms; damage identification; neural networks; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: structural health monitoring; structural damage identification; smart materials and structures; applied soft computing; structural vibration and control
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
A vast amount of infrastructure, such as bridges, roads, and buildings, has been constructed over the past several decades. However, the structural condition of this infrastructure can deteriorate over time due to long-term use, environmental factors, and loading from traffic/use. Small initial defects and damage can worsen and jeopardize the safety and integrity of these structures. As a result, there is expected to be a massive surge in demand for structural health monitoring (SHM) and predictive maintenance of in-service infrastructure over the next decade. Advancements in intelligent sensor networks, data acquisition systems, and the Internet of Things (IoT) have created new opportunities and shown great promise for SHM and predictive maintenance applications. Therefore, it is highly important to develop cutting-edge intelligent sensor technologies specifically for monitoring structural health and enabling the predictive maintenance of infrastructure. This Special Issue solicits research papers exploring the latest advances in intelligent sensor technologies for predictive maintenance and SHM of infrastructure. All submitted papers will undergo peer review before being published. Topics of interest include, but are not limited to, the following:
- Intelligent sensor networks for SHM;
- Wireless/online sensor networks for SHM data acquisition;
- Internet of Things (IoT) for SHM sensor data collection;
- Multi-sensor data fusion for SHM;
- Embedded sensors for SHM of structural materials;
- Energy-efficient sensor nodes for long-term SHM;
- Sensor technologies for concrete crack detection;
- Sensor technologies for structural condition assessment;
- Sensor technologies for predictive maintenance;
- Sensor data analytics for SHM and predictive maintenance;
- Structural model updating using sensor data;
- Deep learning for SHM sensor data analysis;
- Machine learning for predictive maintenance using sensor data
Dr. Nizar Faisal Alkayem
Prof. Dr. Maosen Cao
Prof. Dr. Qiang Wang
Guest Editors
Manuscript Submission Information
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Keywords
- structural health monitoring
- intelligent sensors
- wireless sensor networks
- Internet of Things (IoT)
- multi-sensor data fusion
- structural model updating
- deep learning
- machine learning
- concrete crack detection
- structural condition assessment
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