Search Results (2,114)

Crack type classification in concrete structures is essential for assessing structural integrity, yet traditional visual inspections and RA–AF parameter-based Acoustic Emission (AE) methods suffer from subjectivity and limited ability to capture temporal signal dependencies. This study proposes TemporalAE-Net, a self-attention-based machine learning framework designed to classify tensile and shear cracks while explicitly incorporating the temporal evolution of AE signals. AE data were collected from axial tension tests, shear-failure tests, and four-point bending tests on reinforced concrete beams, and a sliding-window reconstruction method was used to transform sequential AE signals into two-dimensional temporal matrices. TemporalAE-Net integrates one-dimensional convolution for local feature extraction and multi-head self-attention for global temporal correlation learning, followed by multilayer perceptron classification. The proposed model achieved an accuracy of 99.72%, outperforming both its ablated variants without convolutional or attention modules and conventional time-series architectures. Generalization tests on 12 unseen specimens yielded 100% correct classifications, and predictions for reinforced concrete beams closely matched established crack-evolution patterns, with shear cracks detected approximately 15 s prior to visual observation. These results demonstrate that TemporalAE-Net effectively captures temporal dependencies in AE signals. Moreover, it provides accurate and efficient tensile–shear crack identification, making it suitable for real-time structural health monitoring applications. Full article

Cup rupture failures in strip steel welds can lead to strip breakage, resulting in unplanned downtime of high-speed continuous rolling mills and scrap steel losses. Manual visual inspection suffers from a high false positive rate and cannot meet the production cycle time requirements. This paper proposes a lightweight online cup rupture visual inspection method based on an improved YOLOv10 algorithm. The backbone feature extraction network is replaced with ShuffleNetV2 to reduce the model’s parameter count and computational complexity. An ECA attention mechanism is incorporated into the backbone network to enhance the model’s focus on cup rupture micro-cracks. A Slim-Neck design is adopted, utilizing a dual optimization with GSConv and VoV-GSCSP, significantly improving the balance between real-time performance and accuracy. Based on the results, the optimized model achieves a precision of 98.8% and a recall of 99.2%, with a mean average precision (mAP) of 99.5%—an improvement of 0.2 percentage points over the baseline. The model has a computational load of 4.4 GFLOPs and a compact size of only 3.24 MB, approximately half that of the original model. On embedded devices, it achieves a real-time inference speed of 122 FPS, which is about 2.5, 11, and 1.8 times faster than SSD, Faster R-CNN, and YOLOv10n, respectively. Therefore, the lightweight model based on the improved YOLOv10 not only enhances detection accuracy but also significantly reduces computational cost and model size, enabling efficient real-time cup rupture detection in industrial production environments on embedded platforms. Full article

Dendrochronology serves as a vital tool for analyzing the long-term interactions between commercial timber growth and environmental variables such as soil, water, and climate. This study presents Dendro-AutoCount, an innovative image processing framework designed for identifying obscured tree rings in cross-sectional images of Pinus taeda L. The methodology integrates Hessian-based ridge detection with a weighted radial voting gradient method to precisely locate the pith. Following pith detection, the system performs radial cropping to generate directional sub-images (north, east, south, west), where rings are identified via intensity profile analysis, signal smoothing, and peak detection. By filtering outliers and averaging directional counts, the system effectively mitigates common visual interference from black molds, fungus, structural cracks, buds, knots, and cracks. Experimental results confirm the high efficacy of Dendro-AutoCount in processing anomalous tree ring images. Full article

In the field of photovoltaic (PV) power generation, PV systems are prone to faults such as cells and cracks due to prolonged operation in complex environments, which reduce power generation efficiency and pose safety risks. While traditional fault diagnosis methods can detect typical faults, they struggle to capture texture features. Texture features directly reflect the physical degradation process of PV modules and are significant indicators of faults. However, existing deep learning methods, although effective at extracting image features, do not focus on this crucial aspect, leading to insufficient sensitivity when classifying complex fault patterns (such as shadowing and aging overlap). This results in unsatisfactory classification performance. To address this issue, this paper proposes a texture-feature-enhanced fault classification method for PV modules. First, texture features from fault images were extracted using the gray-level co-occurrence matrix (GLCM). Then, a pilot study was conducted to verify the close relationship between texture features and PV faults. A feature fusion module was designed to combine these texture features with global features extracted by the ResNet50 network, enhancing the model. Ultimately, the texture-enhanced model achieved accurate fault classification, significantly improving the model’s sensitivity to fault textures. The proposed model was evaluated on a test dataset and compared with four other classical models. Experimental results showed that the proposed model outperforms existing models in both accuracy and recall, further validating the key role of texture features in model performance and providing a new reference for research in PV module fault classification. Full article

With the acceleration of urbanization, municipal landscape roads play a crucial role in urban public spaces. This study focuses on the distress detection and aging characteristics of asphalt pavements in municipal landscape roads. Firstly, a novel method is proposed based on the SpA-Former shadow removal network, which effectively addresses the interference caused by tree shadows and significantly improves the accuracy of automated distress identification. Distress detection results indicate that transverse cracks are the most common type of distress, primarily influenced by environmental factors such as asphalt material aging, temperature fluctuations, and freeze-thaw cycles—these factors induce asphalt embrittlement and a substantial decline in crack resistance. Subsequently, accelerated aging experiments were conducted to simulate the aging process of asphalt materials. It was found that as aging time extends, asphalt stiffness increases significantly; while this enhances deformation resistance, it also makes the material more prone to cracking under low-temperature conditions. Low-temperature crack resistance tests reveal that asphalt aged for more than six years exhibits a sharp deterioration in low-temperature crack resistance, showing distinct brittle characteristics. Furthermore, freeze-thaw cycle experiments demonstrate that the coupling effect of asphalt aging and freeze-thaw action significantly impairs its freeze-thaw resistance—particularly for asphalt aged over six years, which nearly loses its freeze-thaw resistance. In summary, the coupling effect of asphalt aging and environmental factors is the primary cause of pavement damage in municipal landscape roads. This study divides 2542 images into three mutually exclusive subsets: a training set of 2123 images, a validation set of 209 images, and a test set of 210 images. The research provides new theoretical references and technical support for the maintenance and management of landscape roads, especially demonstrating practical significance in distress detection and the analysis of material aging mechanisms. Full article

The rapid urbanization of coastal regions has intensified the demand for durable underground infrastructure like shield tunnels, where reinforced concrete (RC) structures are critical yet susceptible to long-term degradation in marine environments. This study develops an integrated machine vision-based framework for assessing the long-term durability of RC in marine shield tunnels by synergistically combining point cloud analysis and deep learning-based damage recognition. The methodology involves preprocessing tunnel point clouds to extract the centerline and cross-sections, enabling the quantification of geometric deformations, including segment misalignment and elliptical distortion. Concurrently, an advanced YOLOv8 model is employed to automatically identify and classify surface corrosion damages—specifically water leakage, cracks, and spalling—from images, achieving high detection accuracies (e.g., 95.6% for leakage). By fusing the geometric indicators with damage metrics, a quantitative risk scoring system is established to evaluate structural durability. Experimental results on a real-world tunnel segment demonstrate the framework’s effectiveness in correlating surface defects with underlying geometric irregularities. This integrated approach offers a data-driven solution for the continuous health monitoring and residual life prediction of RC tunnel linings in marine conditions, bridging the gap between visual inspection and structural performance assessment. Full article

Ground settlement is readily induced by shield–tail gaps formed during tunneling, where soil loss must be compensated through backfill grouting. However, improper grouting control may trigger tunnel uplift, segment misalignment, and, after solidification, problems such as voids, cracking, and water ingress. Ensuring construction safety and long-term serviceability requires both reliable detection of grouting effectiveness and a mechanistic understanding of grout diffusion. This review systematically synthesizes sensing technologies, diffusion modeling, and intelligent data interpretation. It highlights their interdependence and identifies emerging trends toward multimodal joint inversion and real-time grouting control. Non-destructive testing techniques can be broadly categorized into geophysical approaches and sensor-based methods. For synchronous detection, vehicle-mounted GPR systems and IoT-based monitoring platforms have been explored, although studies remain sparse. Theoretically, grout diffusion has been investigated via numerical simulation and field measurement, including the spherical diffusion theory, columnar diffusion theory, and sleeve-pipe permeation grouting theory. These theories decompose the diffusion process of the slurry into independent movements. Nevertheless, oversimplified models and sparse monitoring data hinder the development of universally applicable frameworks capable of capturing diverse engineering conditions. Existing techniques are further constrained by limited imaging resolution, insufficient detection depth, and poor adaptability to complex strata. Looking ahead, future research should integrate complementary non-destructive methods with numerical simulation and intelligent data analytics to achieve accurate inversion and dynamic monitoring of the entire process, ranging from grout diffusion and consolidation to defect evolution. Such efforts are expected to advance both synchronous grouting detection theory and intelligent and digital-twin tunnel construction. Full article

Surface crack detection and temporal evolution analysis are fundamental tasks in remote sensing and photogrammetry, providing critical information for slope stability assessment, infrastructure safety inspection, and long-term geohazard monitoring. However, current unmanned aerial vehicle (UAV)-based crack detection pipelines typically treat spatial detection and temporal change analysis as separate processes, leading to weak geometric consistency across time and limiting the interpretability of crack evolution patterns. To overcome these limitations, we propose the Longitudinal Crack Fitting Network (LCFNet), a unified and physically interpretable framework that achieves, for the first time, integrated time-series crack detection and evolution analysis from UAV remote sensing imagery. At its core, the Longitudinal Crack Fitting Convolution (LCFConv) integrates Fourier-series decomposition with affine Lie group convolution, enabling anisotropic feature representation that preserves equivariance to translation, rotation, and scale. This design effectively captures the elongated and oscillatory morphology of surface cracks while suppressing background interference under complex aerial viewpoints. Beyond detection, a Lie-group-based Temporal Crack Change Detection (LTCCD) module is introduced to perform geometrically consistent matching between bi-temporal UAV images, guided by a partial differential equation (PDE) formulation that models the continuous propagation of surface fractures, providing a bridge between discrete perception and physical dynamics. Extensive experiments on the constructed UAV-Filiform Crack Dataset (10,588 remote sensing images) demonstrate that LCFNet surpasses advanced detection frameworks such as You only look once v12 (YOLOv12), RT-DETR, and RS-Mamba, achieving superior performance (mAP_50:95 = 75.3%, F1 = 85.5%, and CDR = 85.6%) while maintaining real-time inference speed (88.9 FPS). Field deployment on a UAV–IoT monitoring platform further confirms the robustness of LCFNet in multi-temporal remote sensing applications, accurately identifying newly formed and extended cracks under varying illumination and terrain conditions. This work establishes the first end-to-end paradigm that unifies spatial crack detection and temporal evolution modeling in UAV remote sensing, bridging discrete deep learning inference with continuous physical dynamics. The proposed LCFNet provides both algorithmic robustness and physical interpretability, offering a new foundation for intelligent remote sensing-based structural health assessment and high-precision photogrammetric monitoring. Full article

To address the challenges of varying scales of concrete defects, class imbalance, and hardware limitations, we propose EMAFG-RTDETR, a UAV-based concrete defect detection algorithm built upon RTDETR. In the feature extraction stage, a lightweight multi-scale attention feature extraction module (EMA-PRepFaster block) is designed, where PConv and RepConv are fused to improve the FasterNet block. At the same time, an Efficient Multi-scale Attention (EMA) module is introduced to enhance spatial feature extraction while reducing computational redundancy. For feature fusion, the Gather-and-Distribute mechanism of GOLD-YOLO is adopted to improve the fusion of multi-scale features. The introduction of Powerful-IoU v2 not only accelerates the training process but also enhances the model’s ability to capture defects of different sizes. To handle the issue of sample imbalance, a novel classification loss function, EMASVLoss, is proposed. This function adjusts classification loss values through piecewise weighting and integrates an exponential moving average mechanism for dynamic weight smoothing, improving model adaptability. Finally, the algorithm was deployed and validated on an octocopter UAV developed by our team. Experimental results demonstrate that EMAFG-RTDETR achieves a 2.5% improvement in mean Average Precision (mAP@0.5), reaching 90% on the concrete defect dataset, with reductions in both parameter size and computational cost. Moreover, the UAV equipped with the proposed algorithm can accurately detect cracks and spalling defects on concrete surfaces, validating the effectiveness of the improved model. Full article

Cracks serve as a critical indicator of tunnel structural degradation. Manual inspections are difficult to meet engineering requirements due to their time-consuming and labor-intensive nature, high subjectivity, and significant error rates, while traditional image processing methods exhibit poor performance under complex backgrounds and irregular crack morphologies. To address these limitations, this study developed a high-quality dataset of tunnel crack images and proposed an improved lightweight semantic segmentation network, LiteSqueezeSeg, to enable precise crack identification and quantification. The model was systematically trained and optimized using a dataset comprising 10,000 high-resolution images. Experimental results demonstrate that the proposed model achieves an overall accuracy of 95.15% in crack detection. Validation on real-world tunnel surface images indicates that the method effectively suppresses background noise interference and enables high-precision quantification of crack length, average width, and maximum width, with all relative errors maintained within 5%. Furthermore, an integrated intelligent detection system was developed based on the MATLAB (R2023b) platform, facilitating automated crack feature extraction and standardized defect grading. This system supports routine tunnel maintenance and safety assessment, substantially enhancing both inspection efficiency and evaluation accuracy. Through synergistic innovations in lightweight network architecture, accurate quantitative analysis, and standardized assessment protocols, this research establishes a comprehensive technical framework for tunnel crack detection and structural health evaluation, offering an efficient and reliable intelligent solution for tunnel condition monitoring. Full article

This research article presents a novel road defect detection methodology that integrates deep learning techniques and a federated learning approach. Existing road defect detection systems heavily rely on manual inspection and sensor-based techniques, which are prone to errors. To overcome these limitations, a data-acquisition system utilizing a GoPro HERO 9 camera was used to capture high-quality videos and images of road surfaces. A comprehensive dataset consist of multiple road defects, such as cracks, potholes, and uneven surfaces, that were pre-processed and augmented to prepare them for effective model training. A Real-Time Detection Transformer-based architecture model was used that achieved mAP50 of 99.60% and mAP50-95 of 99.55% in cross-validation of road defect detection and object detection tasks. Federated learning helped to train the model in a decentralized manner that enhanced data protection and scalability. The proposed system achieves higher detection accuracy for road defects by increasing speed and efficiency while enhancing scalability, which makes it a potential asset for real-time monitoring. Full article

Fatigue-related ‘rotor crack’ can cause catastrophic failure if neglected. Thus, IoT-enabled AI-based predictive maintenance for fault detection and diagnosis is explored. Training and testing AI models under similar conditions improves their prediction performance. On variable speed machines, loss of performance occurs when the testing speed differs from the training speed. This research addresses significant performance loss issues using convolutional neural network (CNN)-based transfer learning models. The main causes of performance loss are domain shift, overfitting, data class imbalance, low fault data availability, and biassed prediction. All the above difficult issues make CNN-based fault prediction systems function badly under varying operating conditions. The proposed methodology addresses all domain adaptation challenges. The proposed methodology was tested by collecting vibration data from an experimental rotor system under varied operating conditions. The proposed methodology outperforms classical machine learning (ML) and deep learning (DL) models, overcoming the overfitting issue with optimised hyperparameters, achieving a prediction accuracy of 99.5%. Under varying operating conditions, it outperforms with a prediction accuracy of 93.2%, and in the ‘data class imbalanced’ scenario, the maximal transfer learning capability achieved was 84.4% with the highest F1-Score. Thus, CNN-based transfer learning enables industrial variable speed machines diagnose rotor crack flaws better than ML and DL models. Full article

Long-term exposure of plastics to the environment causes them to disintegrate, resulting in the formation of micro/nanoplastics as well as the release of additives and chemicals into the soil. The micro/nanoplastics are able to readily migrate into the soil, destabilize the soil microbiota, and finally enter crop plants. Endocytosis, apoplastic transport, root adsorption, transpiration pull, stomatal entry, and crack-entry mode are well-known pathways by which microplastics enter into plants. Roots of vegetable crops were able to transfer 0.2 µm–1.0 µm of microplastics through root adsorption and by transpiration pull to the xylem and then further transported them to the plant tissues through apoplastic pathways. Beads of 1000 nm size were also engulfed by BY-2 protoplast cells through endocytosis. Micro and nanoplastics that enter crops affected the physiological and biochemical activities of the plants. Aquaporins were needed to aid the symplastic pathway which made the symplastic pathway difficult for MPs/NPs transport. Microplastics block seed capsules and roots of seedlings, thereby negatively affecting the uptake and efficient use of nutrients supplied. Photosynthesis of plants was affected due to the reduction in chlorophyll contents. Exposing soils to MPs/NPs drastically affected the pH, EC, and bulk density of the soil. This review focused on bridging the knowledge gap with understanding how microplastics prevent nutrient uptake and nutrient use efficiency in plants. This understanding is essential for assessing the broader ecological impacts of plastic contamination and for developing effective mitigation strategies. Further research is needed on microorganisms capable of degrading plastics, as well as on developing analytical methods for detecting plastics in soil and plant tissues. Also, further research on how to replace plastic mulching and still provide the same benefits as plastic mulch is needed. Full article

Carbon fiber reinforced silicon carbide (C/SiC) composite material exhibits exceptional properties, including high strength, high stiffness, low density, outstanding high-temperature performance, and corrosion resistance. Consequently, they are widely used in aerospace, defense, and automotive engineering. However, their anisotropic, high hardness, and brittle characteristics make them a typical difficult-to-machine material. This paper focuses on achieving high-quality micro hole machining of C/SiC composite material via electrical discharge machining. It systematically investigates electrical discharge machining characteristics and innovatively develops a hollow internal flow helical electrode reaming process. Experimental results reveal four typical chip morphologies: spherical, columnar, blocky, and molten. The study uncovers a multi-mechanism cutting process: the EDM ablation of the composite involves material melting and explosive vaporization, the intact extraction and fracture of carbon fibers, and the brittle fracture and spalling of the SiC matrix. Discharge energy correlates closely with surface roughness: higher energy removes more SiC, resulting in greater roughness, while lower energy concentrates on m fibers, yielding higher vaporization rates. C fiber orientation significantly impacts removal rates: processing time is shortest at θ = 90°, longest at θ = 0°, and increases as θ decreases. Typical defects such as delamination were observed between alternating 0° and 90° fiber bundles or at hole entrances. Cracks were also detected at the SiC matrix–C fiber interface. The proposed hole-enlargement process enhances chip removal efficiency through its helical structure and internal flushing, reduces abnormal discharges, mitigates micro hole taper, and thereby improves forming quality. This study provides practical references for the EDM of C/SiC composite material. Full article

Photovoltaic systems (PV) are increasingly recognized as fundamental to the worldwide adoption of renewable energy technologies. Nonetheless, the efficiency and longevity of solar panels can be compromised by various anomalies, ranging from physical defects to environmental impacts. Early and accurate detection of these anomalies is crucial for maintaining optimal performance and preventing significant energy losses. This study presents SolarAttnNet, a novel convolutional neural network (CNN) architecture with integrated channel and spatial attention mechanisms for solar panel anomaly detection. The proposed model addresses the critical need for automated detection systems, which are crucial for maintaining energy production efficiency and optimizing maintenance. This approach leverages attention mechanisms that emphasize the most relevant features within thermal and visual imagery, improving detection accuracy across multiple anomaly types. SolarAttnNet is evaluated on three distinct solar panel datasets, demonstrating its effectiveness through comprehensive ablation studies that isolate the contribution of each architectural component. Experimental results show that SolarAttnNet achieves superior performance compared to state-of-the-art methods, with accuracy improvements of 3.9% on the PV Systems-AD dataset (94.2% vs. 90.3%), 3.6% on the InfraredSolarModules dataset (92.1% vs. 88.5%), and 3.5% on the RoboflowAnomalies dataset (89.7% vs. 86.2%) compared to baseline ResNet-50. For challenging subtle anomalies like cell cracks and PID, the proposed model demonstrates even more significant improvements with F1-score gains of 4.8% and 5.4%, respectively. Ablation studies reveal that the channel attention mechanism contributes a 2.6% accuracy improvement while spatial attention adds 2.3% across datasets. This work contributes to advancing automated inspection technologies for renewable energy infrastructure, supporting more efficient maintenance protocols and ultimately enhancing solar energy production. Full article