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Advanced Sensing and Deep Learning for Damage Detection and Performance...
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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 September 2026 | Viewed by 1633

Special Issue Editors

Dr. Andy Nguyen

E-Mail Website
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
Dr. Yang Yu

E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2502, Australia
Interests: concrete materials and structures; modeling; alkali-activated materials; fiber-reinforced polymer composites
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

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Published Papers (2 papers)

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Research

33 pages, 11689 KB  
Article
SPR-DETR: DETR with Self-Supervised Learning and Position Relation Modeling for UAV-Based Catenary Support Component Detection in Electrified Railways
by Tao Liang, Zhigang Liu, Linjun Shi, Haonan Yang, Ning Ma and Hui Wang
Sensors 2026, 26(10), 3077; https://doi.org/10.3390/s26103077 - 13 May 2026
Viewed by 47
Abstract
Catenary support components (CSCs) are essential for the safe and efficient operation of electrified railway systems. However, detecting CSCs in images presents significant challenges due to the scarcity of labeled data, the presence of complex and diverse backgrounds, and the difficulties associated with [...] Read more.
Catenary support components (CSCs) are essential for the safe and efficient operation of electrified railway systems. However, detecting CSCs in images presents significant challenges due to the scarcity of labeled data, the presence of complex and diverse backgrounds, and the difficulties associated with multi-scale variations. To tackle these issues, this paper introduces a novel detection framework designed explicitly for CSCs. First, a Siamese-based self-supervised learning framework is designed as a pre-training strategy to reduce the reliance on labeled data, effectively leveraging unlabeled images and significantly lowering annotation costs. This pre-training approach enables the model to focus on identifying and extracting relevant features from prior knowledge, honing its ability to discern key patterns and structures within the data. Second, the Vision Attention-based Intrascale Feature Interaction (Vision-AIFI) and Relation Vision Module (RVM) are proposed to enhance the model, which can strengthen its ability to extract multi-scale features and effectively address challenges posed by complex backgrounds and scale variations. Third, a Dempster–Shafer (DS) evidence theory-based detection head is inserted to improve classification confidence and localization precision, ensuring accurate detection results in complex inspection scenarios. Finally, a UAV-based dataset for CSCs is constructed and validation experiments are performed. To evaluate the model, we used several standard COCO metrics, including mAP (77.84), APs (67.84), APm (70.31), and APl (90.04). In addition, the framework is further evaluated for Domain Generalization, which can demonstrate its strong adaptability and high detection accuracy for real-world CSC detection tasks. Full article
(This article belongs to the Special Issue Advanced Sensing and Deep Learning for Damage Detection and Performance Assessment in Structural Health Monitoring)
17 pages, 4169 KB  
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 983
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
(This article belongs to the Special Issue Advanced Sensing and Deep Learning for Damage Detection and Performance Assessment in Structural Health Monitoring)
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