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Intelligent Damage Detection of Materials and Structural Health Monitoring Technology

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A Special Issue of Materials (ISSN 1996-1944) belonging to the section "Advanced Materials Characterization".

Deadline for manuscript submissions: closed (20 May 2026) | Viewed by 10803

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Editors

Dr. Nizar Faisal Alkayem

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1. College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210046, China
2. College of Civil Engineering, Hohai University, Nanjing 210098, China
Interests: optimization; soft computing; structural health monitoring; damage detection; evolutionary computation; fuzzy logic; swarm algorithms; deep learning; manu-facturing; welding; evolutionary algorithms; damage identification; neural net-works; machine learning
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Prof. Dr. Wei Xu

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Department of Engineering Mechanics, College of Mechanics and Materials, Hohai University, Nanjing 210098, China
Interests: structural health monitoring; structural damage identification; vibro-acoustics
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Prof. Dr. Panagiotis G. Asteris

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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

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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 (9 papers)

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Research

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18 pages, 19499 KB

Open AccessArticle

Cross-Sectional Cladding Segmentation of Stainless-Steel/Carbon-Steel Clad Wire Rods Using an Improved U-Net with Multi-Scale Attention

by Lei Zeng, Zecheng Zhuang, Geng Zhou, Weiping Lu, Xuehai Qian, Zhen Li, Zhe Gou, Yue Yu and Jianping Tan

Materials 2026, 19(11), 2359; https://doi.org/10.3390/ma19112359 - 2 Jun 2026

Viewed by 397

Abstract

Accurate cladding segmentation is essential for quantitative quality assessment of stainless-steel/carbon-steel clad wire rods used in bridge cables, yet remains challenging because of weak core–cladding contrast, narrow interfacial transition zones, local cladding-thickness fluctuations, and limited repeatability of manual inspection. This study proposes an improved U-Net framework that integrates residual feature extraction, multi-scale contextual perception, and attention-guided feature refinement for robust cladding identification. A cross-sectional image dataset comprising 18,566 samples was constructed through standardized specimen preparation, chemical color development, image acquisition, pixel-level annotation, and data augmentation. In the proposed model, the original U-Net encoder is replaced with ResNet50 to enhance deep semantic representation, while atrous spatial pyramid pooling and a convolutional block attention module are embedded into the feature-fusion stage to improve boundary discrimination and thin-cladding recognition. On the test set, the model achieved a mean pixel accuracy of 97.29%, cladding intersection over union of 88.82%, and mean intersection over union of 93.72%, outperforming the baseline U-Net by 1.38, 9.19, and 5.17 percentage points, respectively. Ablation and comparative experiments further demonstrate improved boundary continuity, local-detail preservation, and segmentation stability compared with representative CNN-based segmentation models. These findings suggest that the proposed framework provides a practical and reliable vision-based approach for cladding-thickness measurement, eccentricity evaluation, uniformity assessment, and batch quality inspection of clad wire rods. Full article

(This article belongs to the Special Issue Intelligent Damage Detection of Materials and Structural Health Monitoring Technology)

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29 pages, 8526 KB

Open AccessArticle

ASW-YOLO: Lightweight Gear Defect Detection with Improved YOLOv8n

by Zhecheng Luo and Bin Zheng

Materials 2026, 19(11), 2309; https://doi.org/10.3390/ma19112309 - 29 May 2026

Viewed by 886

Abstract

Aiming at the problems of diverse defect types, large-scale differences, and complex background interference in gear surface defect detection, a lightweight model, ASW-YOLO, is proposed based on YOLOv8n. By using an ADown dual downsampling module to compress feature map resolution and preserve fine-grained information. C2f_SE channel attention is introduced to enhance small-scale defect response. The CIoU is replaced with WIoU to optimize multi-scale target localization accuracy. The experiments are conducted on the gear dataset. The comparative experiments show that mAP@0.5 of ASW-YOLO reached 94.8%, an increase of 4.5% compared to YOLOv8n, with a reduction of 9.3% in parameter count and 8.5% in computational complexity. The ablation experiments confirm the effectiveness of the three modules. ASW-YOLO achieves a 4.5% increase in mAP@0.5 and a 6.1% increase in recall compared to YOLOv8n. The generalization experiments demonstrate that the mAP@0.5 fluctuation of ASW-YOLO remains below 2% under strong highlight and striped shadow. Moreover, the model maintains over 85% mAP@0.5 under motion blur. ASW-YOLO balances precision and lightweight, making it suitable for real-time quality monitoring in industry. Full article

(This article belongs to the Special Issue Intelligent Damage Detection of Materials and Structural Health Monitoring Technology)

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28 pages, 12481 KB

Open AccessEditor’s ChoiceArticle

Quantitative Damage Detection and Evolution in Composite Structures Using Digital Image Correlation, Machine Learning, and Peridynamics

by Tomas Vaitkūnas, Elena Jasiūnienė, Justas Griškevičius, Vykintas Samaitis and Paulius Griškevičius

Materials 2026, 19(10), 1917; https://doi.org/10.3390/ma19101917 - 7 May 2026

Viewed by 608

Abstract

Structural health monitoring (SHM) of composite structures using surface strain fields measured by digital image correlation (DIC) has been widely demonstrated; however, accurate damage quantification remains challenging. This study proposes a hybrid framework integrating finite element (FE) modeling, machine learning (ML), and peridynamics (PD). A CFRP specimen with a notch was subjected to cyclic loading, and damage evolution was monitored using DIC and validated by ultrasound measurements. A validated FE model generated synthetic strain-field datasets for ML training, enabling defect detection and quantitative characterization directly from surface strains. The trained models achieved high accuracy, including perfect notch detection and low prediction errors. A calibrated PD model captured internal damage evolution and fatigue behavior. The combined DIC–ML–PD approach enables accurate, non-contact damage identification and prognosis, supporting physics-informed digital twins for composite structures. Full article

(This article belongs to the Special Issue Intelligent Damage Detection of Materials and Structural Health Monitoring Technology)

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28 pages, 9431 KB

Open AccessArticle

Research on the Edge–Discrepancy Collaborative Method for Defect Detection in Casting DR Images

by Yangkai He and Yunxia Chen

Materials 2026, 19(5), 900; https://doi.org/10.3390/ma19050900 - 27 Feb 2026

Cited by 2 | Viewed by 731

Abstract

To address the limited detection accuracy of casting defects—including pores, inclusions, and looseness—in digital radiography (DR) images, which stems from their small scale, high morphological variability, and interference from complex background textures, we propose MTS-YOLOv11: an edge–discrepancy collaborative defect detection framework tailored for casting DR imagery. Built upon YOLOv11, MTS-YOLOv11 incorporates three key innovations: (1) a Multi-Scale Edge Information Enhancement System (MSEES), integrated into the C3K2 module of the backbone network, to strengthen discriminative feature extraction for minute defects; (2) a TripletAttention mechanism embedded in high-level backbone stages to jointly calibrate channel–spatial dependencies and suppress texture-induced spurious responses under complex backgrounds; (3) a Scale-Discrepancy-Aware Gated Fusion (SDAGFusion) module positioned immediately before the detection head, enabling scale-discrepancy-aware gated fusion of multi-scale features, emphasizing defect regions while suppressing background interference. Experimental results show that on the casting DR dataset, MTS-YOLOv11 achieves mAP@0.5 = 96.5% and mAP@0.5:0.95 = 68.5%—improvements of 1.3 and 1.2 percentage points over the baseline YOLOv11—across all three defect categories. Moreover, on the same platform, MTS-YOLOv11 achieves an inference speed of 359.07 FPS, compared with 346.86 FPS for the baseline. Meanwhile, the model has 2.72M parameters and 7.8G FLOPs. These results indicate a consistent improvement in detection accuracy while maintaining a practical balance between precision and computational efficiency. Moreover, cross-dataset generalization tests on newly acquired industrial DR data show that MTS-YOLOv11 consistently outperforms the baseline across evaluation metrics, suggesting improved robustness to unseen imaging conditions and supporting its potential for real-world foundry inspection. Full article

(This article belongs to the Special Issue Intelligent Damage Detection of Materials and Structural Health Monitoring Technology)

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30 pages, 8136 KB

Open AccessArticle

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

Cited by 6 | Viewed by 1539

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 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

Open AccessArticle

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 1147

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 961

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 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

Open AccessArticle

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

Cited by 2 | Viewed by 1348

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, 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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Review

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44 pages, 20519 KB

Open AccessReview

Infrared Thermography for Defect Detectability in Structural Health Monitoring of Concrete Structures: Heat Transfer Physics, Resolution Limits, and AI-Assisted Diagnostics

by Wiktor Wciślik and Sylwia Wciślik

Materials 2026, 19(12), 2596; https://doi.org/10.3390/ma19122596 - 16 Jun 2026

Viewed by 1648

Abstract

Infrared thermography (IRT) has emerged as a vital tool for structural health monitoring (SHM), yet the quantitative links between heat transfer physics and defect detectability in reinforced concrete remain underdeveloped. This review establishes a physics-oriented framework to clarify the mechanisms, operational parameters, and technological constraints governing IRT-based diagnostics. We analyze the interplay of thermal diffusivity, emissivity, and transient conduction in generating defect-induced contrasts. Quantitative synthesis identifies a minimum thermal contrast of 0.5 °C as a requirement for reliable detection, with active thermography proving effective for defects within a 5–10 cm depth range. Furthermore, we quantify how moisture-induced emissivity variations exceeding 10% introduce critical uncertainties in field measurements. The study underscores the necessity of integrating heat transfer theory with advanced signal processing and artificial intelligence (AI) to overcome current resolution limits. By defining these physical boundaries, this review provides a robust foundation for automated, intelligent infrastructure diagnostics, emphasizing that detectability is fundamentally governed by diffusion physics rather than raw temperature measurements. Full article

(This article belongs to the Special Issue Intelligent Damage Detection of Materials and Structural Health Monitoring Technology)

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