Guest Editors
Dr. Qi Xiao
Email: xiaoqi1@shanghaitech.edu.cn
Affiliation: School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China
Homepage:
Research Interests: Structural integrity assessment
Dr. Bo Zhao
Email: bozhao@cityu.edu.hk
Affiliation: School of Data Science, City University of Hong Kong, 999077, China
Homepage:
Research Interests: Fault Diagnosis, Information Fusion, Condition Monitoring
Dr. Rongbiao Wang
Email: wangrongbiao@nchu.edu.cn
Affiliation: Nanchang Hangkong University, School of Instrument Science and Optoelectronic Engineering, 330063, China
Homepage:
Research Interests: Electromagnetic testing
Dr. Gaige Ru
Email: r.gg@uestc.edu.cn
Affiliation: School of Automation Engineering, University of Electronic Science and Technology of China, 611731, China
Homepage:
Research Interests: Electromagnetic non-destructive testing, intelligent equipment, RFID wireless transmission
Dr. Shaoxuan Zhang
Email: zhangsx@lzjtu.edu.cn
Affiliation: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, 730070, China
Homepage:
Research Interests: Magnetic flux leakage, Graph Neural Network
Summary
As infrastructure systems become increasingly complex and subject to variable operational conditions, there is a pressing need for innovative detecting and monitoring approaches that can ensure the safety and reliability of infrastructure systems, while providing clear, actionable insights for stakeholders. The significance of this research lies in its ability to enhance the reliability and safety of critical structures by leveraging advanced data analytics, machine learning, and electromagnetic sensing techniques.
This special issue focuses on recent advancements in the detection and monitoring across the critical structures of infrastructure systems. This research area integrates advanced sensing technologies, intelligent algorithms, and data analytics to monitor and assess the condition of equipment, structures, and operational systems. The sensing technologies and intelligent algorithms developed for this purpose leverage big data to analyze and interpret vast amounts of sensor data. These algorithms enable real-time condition monitoring, predictive maintenance, and early warning systems for potential failures. By analyzing patterns in structural behavior, equipment performance, and operational parameters, these systems can identify anomalies and predict deterioration before critical failures occur. This special issue aims to explore methodologies that not only capture and analyze detection data but also ensure that the resulting models and predictions remain interpretable to engineers and decision-makers. Contributions from researchers and practitioners are welcomed to share their insights, novel methodologies, and case studies that advance the field.
Suggested themes shall be listed.
Topics of interest include, but are not limited to:
- Advanced electromagnetic sensing techniques
- Multi-sensor integration and data fusion
- Health monitoring and facility maintenance for railway track
- Condition assessment and defect detection for nuclear fuel assembly
- Techniques for enhancing interpretability in machine learning models applied to SHIM
- Methods for uncertainty quantification and risk assessment in intelligent SHM systems
Keywords
Sensing techniques; data fusion method; interpretability for machine learning models
Published Papers
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