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Advanced Sensing and Deep Learning for Damage Detection and Performance Assessment in 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: 15 February 2026 | Viewed by 421

Special Issue Editors


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Guest Editor
School of Engineering, University of Southern Queensland, Springfield Central, QLD 4300, Australia
Interests: structural health monitoring; resilient and intelligent infrastructure; AI for infrastructure
Special Issues, Collections and Topics in MDPI journals
School of Civil and Environmental Engineering, UNSW, Sydney, NSW 2502, Australia
Interests: structural health monitoring; sustainable construction materials; seismic resilient structures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Structural Health Monitoring (SHM) has rapidly developed over the past few decades as authorities and practitioners look for new, efficient methods to ensure the safety and longevity of critical infrastructure. Recent advancements in sensor and computing technologies have significantly improved the ability to capture detailed data on structural conditions. When combined with the power of state-of-the-art artificial intelligence techniques, these technologies can provide unprecedented insights into the health and performance of engineering structures. With an ability to learn complex patterns and make accurate predictions, deep learning models are particularly well-suited for analyzing the vast amounts of data generated by modern sensor systems.

To cater for the need of the scientific community, we invite original research and comprehensive review articles that explore innovative approaches to damage detection and performance assessment using advanced sensing and deep learning. The topics of interest include, but are not limited to, sensing techniques, edge computing, data fusion, and the latest application of deep neural networks. Contributions that address the challenges and opportunities in integrating these technologies in real-world scenarios are particularly encouraged. By bringing together cutting-edge studies from around the globe, this Special Issue aims to capture the transformative applications in SHM and provide the reader with the latest insights into this fascinating realm of engineering research.

Dr. Andy Nguyen
Dr. Yang Yu
Guest Editors

Manuscript Submission Information

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Keywords

  • structural health monitoring
  • advanced sensing
  • deep learning
  • damage detection
  • performance assessment
  • smart inspection

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Published Papers (1 paper)

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Research

17 pages, 4169 KiB  
Article
Single-Sensor Impact Source Localization Method for Anisotropic Glass Fiber Composite Wind Turbine Blades
by Liping Huang, Kai Lu and Liang Zeng
Sensors 2025, 25(14), 4466; https://doi.org/10.3390/s25144466 - 17 Jul 2025
Viewed by 146
Abstract
The wind turbine blade is subject to multi-source impacts, such as bird strikes, lightning strikes, and hail, throughout its extended service. Accurate localization of those impact sources is a key technical link in structural health monitoring of the wind turbine blade. In this [...] Read more.
The wind turbine blade is subject to multi-source impacts, such as bird strikes, lightning strikes, and hail, throughout its extended service. Accurate localization of those impact sources is a key technical link in structural health monitoring of the wind turbine blade. In this paper, a single-sensor impact source localization method is proposed. Capitalizing on deep learning frameworks, this method innovatively transforms the impact source localization problem into a classification task, thereby eliminating the need for anisotropy compensation and correction required by conventional localization algorithms. Furthermore, it leverages the inherent coding effects of the blade’s material and geometric anisotropy on impact sources originating from different positions, enabling localization using only a single sensor. Experimental results show that the method has a high localization accuracy of 96.9% under single-sensor conditions, which significantly reduces the cost compared to the traditional multi-sensor array scheme. This study provides a cost-effective solution for real-time detection of wind turbine blade impact events. Full article
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