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Intelligent Damage Detection of Materials and Structural Health Monitoring Technology
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Intelligent Damage Detection of Materials and Structural Health Monitoring Technology

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Advanced Materials Characterization".

Deadline for manuscript submissions: 20 May 2026 | Viewed by 2925

Special Issue Editors

Dr. Nizar Faisal Alkayem

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Guest Editor
1. College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210046, China
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
Prof. Dr. Wei Xu

E-Mail Website
Guest Editor
Department of Engineering Mechanics, College of Mechanics and Materials, Hohai University, Nanjing 210098, China
Interests: structural health monitoring; structural damage identification; vibro-acoustics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Panagiotis G. Asteris

E-Mail Website
Guest Editor
Computational Mechanics Laboratory, Department of Civil Engineering, School of Pedagogical and Technological Education, GR15122 Athens, Greece
Interests: computational intelligence; computational mechanics; machine learning; masonry structures; structural engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, a wide variety of structural materials have been employed in the construction of critical structural systems such as bridges, buildings, and transportation networks. However, these materials deteriorate over time due to long-term use, environmental exposure, and operational loading. Early-stage defects and damage in structural materials can propagate, compromising the safety and integrity of the structures. Consequently, there is increasing demand for advanced structural health monitoring (SHM) technologies to assess the condition of structural materials in aging structures. Recent advancements in sensing technologies, data analytics, artificial intelligence, machine learning, and computational techniques have opened new avenues for innovative SHM solutions. This Special Issue aims to compile cutting-edge research on intelligent damage detection and SHM methodologies specifically focused on structural materials.

Topics of interest include, but are not limited to, the following:

  • Characterization and property prediction of structural materials for SHM;
  • Intelligent damage detection in structural materials (concrete, steel, and composites);
  • Defect identification in structural materials using deep learning;
  • Signal processing and modal analysis for SHM of structural materials;
  • Physics-informed machine learning for structural materials;
  • Failure prognostics and remaining useful life prediction of materials;
  • Condition assessment and integrity evaluation of structural materials;
  • Structural model updating;
  • Failure prognostics and early warning.

Dr. Nizar Faisal Alkayem
Prof. Dr. Wei Xu
Prof. Dr. Panagiotis G. Asteris
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. Materials 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
  • damage detection
  • structural materials
  • material characterization
  • defect identification
  • concrete crack detection
  • deep learning
  • material property prediction
  • failure prognostics
  • condition assessment

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Published Papers (4 papers)

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Research

30 pages, 8136 KB  
Article
AE-YOLO: Research and Application of the YOLOv11-Based Lightweight Improved Model in Photovoltaic Panel Surface Intelligent Defect Detection
by Bin Zheng and Yunjin Yang
Materials 2025, 18(23), 5404; https://doi.org/10.3390/ma18235404 - 30 Nov 2025
Viewed by 280
Abstract
With the rapid development of renewable energy, surface defect detection of photovoltaic panels has become an important link in improving photoelectric conversion efficiency and ensuring safety. However, there are various types of surface defects on photovoltaic panels with complex backgrounds, and traditional detection [...] Read more.
With the rapid development of renewable energy, surface defect detection of photovoltaic panels has become an important link in improving photoelectric conversion efficiency and ensuring safety. However, there are various types of surface defects on photovoltaic panels with complex backgrounds, and traditional detection methods face challenges such as low efficiency and insufficient accuracy. This article proposes a lightweight improved model AE-YOLO (YOLOv11+Adown +ECA) based on YOLOv11, which improves detection performance and efficiency by introducing a lightweight dynamic down-sampling module (Adown) and an Efficient Channel Attention (ECA). The Adown module reduces the complexity of computational and parameters through steps such as average pooling preprocessing, channel dimension segmentation, branch feature processing, and feature fusion. The ECA mechanism enhances the model’s response to defect sensitive feature channels and improves its ability to discriminate low contrast small defects through adaptive average pooling, one-dimensional convolution, and sigmoid activation. The experimental results indicate that the AE-YOLO model performs well on the PVEL-AD dataset. mAP@0.5 reached 90.3%, the parameter count decreased by 18.7%, the computational load decreased by 19%, and the inference speed reached 259.56 FPS. The ablation experiment further validated the complementarity between Adown and ECA modules, providing an innovative solution for real-time and accurate defect detection of photovoltaic panels in industrial scenarios. Full article
(This article belongs to the Special Issue Intelligent Damage Detection of Materials and Structural Health Monitoring Technology)
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21 pages, 4882 KB  
Article
Damage Identification in Composite Wind Turbine Blades Using Relative Natural Frequency Changes and Bayesian Probability
by Panida Kaewniam, Qingyang Wei, Haoan Gu, Nizar Faisal Alkayem and Maosen Cao
Materials 2025, 18(23), 5263; https://doi.org/10.3390/ma18235263 - 21 Nov 2025
Viewed by 432
Abstract
Structural health monitoring (SHM) of composite wind turbine blades (WTBs) is crucial for improving power efficiency, reducing maintenance costs, and ensuring long-term structural reliability. Traditional frequency-based damage detection, often derived from simplified isotropic beam principles, can be challenged by the anisotropy, heterogeneity, and [...] Read more.
Structural health monitoring (SHM) of composite wind turbine blades (WTBs) is crucial for improving power efficiency, reducing maintenance costs, and ensuring long-term structural reliability. Traditional frequency-based damage detection, often derived from simplified isotropic beam principles, can be challenged by the anisotropy, heterogeneity, and geometric complexity of composite WTBs. Moreover, as global indicators, natural frequencies are sensitive to environmental variations but are also limited in localizing damage. To overcome these challenges, this research introduces a combined approach of relative natural frequency change (RNFC) and Bayesian probability, referred to as the B-RNFC method. The framework includes four stages: (i) analyzing the correlation between natural frequencies and damage conditions (location and severity) in composite cantilever beams and WTBs; (ii) developing normalized RNFC curves from various damage sizes to establish a spatial damage reference dataset, which is then used for the next steps; (iii) integrating the resulting frequency-related data with Bayesian probability to identify damage locations and map them onto the structures; and (iv) evaluating the performance of the B-RNFC in multiple-damage localization. Simulation results demonstrate the effective damage localization range of the B-RNFC method. For a simple cantilever beam, this range is 20–80% of the distance from the fixed end. When applied to the composite WTB, this effective range corresponds to 40–80% of the blade length from the root. In addition, the proposed method can localize the dual damages when the damages are symmetrically located or when one damage is at the mid-span. Full article
(This article belongs to the Special Issue Intelligent Damage Detection of Materials and Structural Health Monitoring Technology)
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14 pages, 3426 KB  
Article
Damage Diagnosis Framework for Composite Structures Based on Multi-Dimensional Signal Feature Space and Neural Network
by Jian Wang, Jing Wang, Shaodong Zhang, Qin Yuan, Minhua Lu and Qiang Wang
Materials 2025, 18(16), 3834; https://doi.org/10.3390/ma18163834 - 15 Aug 2025
Viewed by 572
Abstract
It is particularly important to ensure the safety of engineering structures, such as aerospace vehicles and wind turbines, most of which are made of composite materials. A sudden failure of the structure may happen following the accumulation of structural damage. Since they are [...] Read more.
It is particularly important to ensure the safety of engineering structures, such as aerospace vehicles and wind turbines, most of which are made of composite materials. A sudden failure of the structure may happen following the accumulation of structural damage. Since they are sensitive to tiny damage and can propagate through engineering structures over a long distance, Lamb waves have been widely explored to develop highly efficient damage detection theories and methodologies. During propagation, affected by the mechanical properties of the structure, a large amount of information and features related to structural states can be reflected and transmitted by Lamb waves, including the occurrence and extent of structural damage. By analyzing the effect of damage acting on Lamb waves, a multi-scale wavelet transform analysis is adopted to extract multi-feature parameters in the time–frequency domain of the acquired signals. With the help of the nonlinear mapping ability of a neural network, a damage assessment model for composite structures is constructed to realize the evaluation of typical structural damage at different levels. The results of an experiment conducted on an epoxy–glass-fiber-reinforced plate show that the extracted multi-feature parameters of Lamb waves in the time–frequency domain are sensitive to the accumulated typical damage. The damage assessment model can properly evaluate the damage degree with satisfactory accuracy. Full article
(This article belongs to the Special Issue Intelligent Damage Detection of Materials and Structural Health Monitoring Technology)
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14 pages, 3123 KB  
Article
Impact of Activation Functions on the Detection of Defects in Cast Steel Parts Using YOLOv8
by Yunxia Chen, Yangkai He and Yukun Chu
Materials 2025, 18(12), 2834; https://doi.org/10.3390/ma18122834 - 16 Jun 2025
Viewed by 822
Abstract
In this paper, to address the issue of the unknown influence of activation functions on casting defect detection using convolutional neural networks (CNNs), we designed five sets of experiments to investigate how different activation functions affect the performance of casting defect detection. Specifically, [...] Read more.
In this paper, to address the issue of the unknown influence of activation functions on casting defect detection using convolutional neural networks (CNNs), we designed five sets of experiments to investigate how different activation functions affect the performance of casting defect detection. Specifically, the study employs five activation functions—Rectified Linear Unit (ReLU), Exponential Linear Units (ELU), Softplus, Sigmoid Linear Unit (SiLU), and Mish—each with distinct characteristics, based on the YOLOv8 algorithm. The results indicate that the Mish activation function yields the best performance in casting defect detection, achieving an mAP@0.5 value of 90.1%. In contrast, the Softplus activation function performs the worst, with an mAP@0.5 value of only 86.7%. The analysis of the feature maps shows that the Mish activation function enables the output of negative values, thereby enhancing the model’s ability to differentiate features and improving its overall expressive power, which enhances the model’s ability to identify various types of casting defects. Finally, gradient class activation maps (Grad-CAM) are used to visualize the important pixel regions in the casting digital radiography (DR) images processed by the neural network. The results demonstrate that the Mish activation function improves the model’s focus on grayscale-changing regions in the image, thereby enhancing detection accuracy. Full article
(This article belongs to the Special Issue Intelligent Damage Detection of Materials and Structural Health Monitoring Technology)
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