🔒Call for Papers-Advanced detection technologies and interpretable

machine learning methods in energy infrastructure

Journal: Structural Durability & Health Monitoring

🔗SI URL: https://www.techscience.com/sdhm/special_detail/energy_infrastructure

📅 Submission Deadline: 20 July 2025

✉️Email Enquiry: [email protected]

🌟 Guest Editors:

Dr. Qi Xiao, School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China

Dr. Bo Zhao, School of Data Science, City University of Hong Kong, 999077, China

Dr. Rongbiao Wang, Nanchang Hangkong University, School of Instrument Science and Optoelectronic Engineering, 330063, China

Dr. Gaige Ru, School of Automation Engineering, University of Electronic Science and Technology of China, 611731, China

Dr. Shaoxuan Zhang, School of Automation and Electrical Engineering, Lanzhou Jiaotong University，730070, China

🔍 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

Key Words:

Sensing techniques; data fusion method; interpretability for machine learning models