Search Results (83)

The poly-lactic acid (PLA) coating was widely applied to the WE43 alloy to modulate its degradation for biomedical implants, a strategy whose long-term efficacy is critically dictated by the coating’s protective and ion-permeation properties under dynamic physiological flow. This work systematically investigates the corrosion performance under the such flow condition using a novel in situ monitoring method. This method enables a direct, in situ assessment of both the ion-permeation rate across the PLA membrane acted as the coating and the concurrent evolution of the electrochemical properties of the membrane as well as the WE43 alloy substrate. Results revealed that the applied flow accelerated the formation of micro-cracks in the PLA membrane, which facilitated the permeation of Na⁺ and Cl⁻ ions and thereby intensified the corrosion of the underlying substrate. During the initial 15 days, the ion permeation rates for Na⁺ and Cl⁻ ions under the flow condition were 0.097 and 0.042 mmol/(L·h), respectively. The degradation rate of the substrate exhibited a strong positive correlation with the concentration of permeated Cl⁻ ions. In contrast, the deposition of calcium-containing compounds was identified as a time-dependent process, governed by the permeation kinetics of Ca²⁺ ions through the membrane. Full article

Natural rock materials, containing micro-cracks and pore defects, significantly alter their mechanical behavior. This study investigated fracture interactions of red sandstone containing double close-round holes (diameter: 10 mm; bridge angle: 30°, 45°, 60°, 90°) using acoustic emission (AE) monitoring and the discrete element simulations method (DEM), which was a novel methodology for revealing dynamic failure mechanisms. The uniaxial compression tests showed that hole geometry critically controlled failure modes: specimens with 0° bridge exhibited elastic–brittle failure with intense AE energy releases and large fractures, while 45° arrangements displayed elastic–plastic behaviors with stable AE signal responses until collapse. The quantitative AE analysis revealed that the fracture-type coefficient k had a distinct temporal clustering characteristic, demonstrating the spatiotemporal synchronization of tensile and shear crack initiation and propagation. Furthermore, numerical simulations identified a critical stress redistribution phenomenon, that axial compressive force chains concentrated along the loading axis, forming continuous longitudinal compression zones, while radial tensile dispersion dominated hole peripheries. Crucially, specimens with 45° and 90° bridges induced prominently symmetric tensile fractures (85° to horizontal direction) and shear-dominated failure near junctions. These findings can advance damage prediction in discontinuous geological media and offer direct insights for optimizing excavation sequences and support design in cavern engineering. Full article

Soil desiccation cracks are common in farmland under dry conditions, which can alter soil water movement by providing preferential flow paths and thus affect water and fertilizer use efficiency. Understanding the mechanism of soil shrinkage and cracking is of great significance for optimizing field management by crack utilization or prevention. The behavior of soil shrinkage and cracking was monitored during drying experiments and analyzed with the help of a digital image processing method. The results showed that during shrinkage, the changes in soil height and equivalent diameter with water content differed significantly. The height change consisted of a rapid decline stage and a residual stage, while the equivalent diameter had a stable stage before the rapid decline stage. The VG-Peng model was suitable to fit the soil shrinkage characteristic curves, and the curves revealed that the soil shrinkage contained structural shrinkage, proportional shrinkage, residual shrinkage, and zero shrinkage stages. According to the changes in evaporation intensity, soil water evaporation could be divided into three stages: stable stage, declining stage, and residual stage. Cracks first formed in the defect areas and edge areas of the soil, and they mainly propagated in the stable evaporation stage. Crack development was dominated by an increase in crack length during the early cracking stage, while the propagation of crack width played a major role during the later stage. At the end of drying, the contribution ratio of crack length and width to the crack area was approximately 30% and 70%, respectively. The box-counting fractal dimension of the stabilized cracks was approximately 1.65, indicating that the crack network had significant self-similarity. The experimental results were used to implement the discrete element method to model the process of soil shrinkage and cracking. The models could effectively simulate the variation characteristics of soil height and equivalent diameter during shrinkage, as well as the variation characteristics of crack ratio and length density during cracking, with acceptable relative errors. In particular, the modeled morphology of the crack network was highly similar to the experimental observation. Our results provide new insights into the characterization and simulation of soil desiccation cracks, which will be conducive to understanding crack evolution and soil water movement in farmland. Full article

Road-surface distress poses a serious threat to traffic safety and imposes a growing burden on urban maintenance budgets. While modern detectors based on convolutional networks and Vision Transformers achieve strong frame-level performance, they often overlook an essential property of road environments—structural symmetry within road networks and damage patterns. We present Graph-MambaRoadDet (GMRD), a symmetry-aware and lightweight framework that integrates dynamic graph reasoning with state–space modeling for accurate, topology-informed, and real-time road damage detection. Specifically, GMRD employs an EfficientViM-T1 backbone and two DefMamba blocks, whose deformable scanning paths capture sub-pixel crack patterns while preserving geometric symmetry. A superpixel-based graph is constructed by projecting image regions onto OpenStreetMap road segments, encoding both spatial structure and symmetric topological layout. We introduce a Graph-Generating State–Space Model (GG-SSM) that synthesizes sparse sample-specific adjacency in $\mathcal{O}\left(M\right)$ time, further refined by a fusion module that combines detector self-attention with prior symmetry constraints. A consistency loss promotes smooth predictions across symmetric or adjacent segments. The full INT8 model contains only 1.8 M parameters and 1.5 GFLOPs, sustaining 45 FPS at 7 W on a Jetson Orin Nano—eight times lighter and 1.7× faster than YOLOv8-s. On RDD2022, TD-RD, and RoadBench-100K, GMRD surpasses strong baselines by up to +6.1 mAP_50:95 and, on the new RoadGraph-RDD benchmark, achieves +5.3 G-mAP and +0.05 consistency gain. Qualitative results demonstrate robustness under shadows, reflections, back-lighting, and occlusion. By explicitly modeling spatial and topological symmetry, GMRD offers a principled solution for city-scale road infrastructure monitoring under real-time and edge-computing constraints. Full article

To investigate the failure mechanisms of surrounding rock in deep mine tunnels and its spatio-temporal evolution patterns, a true triaxial disturbance unloading rock testing system, the acoustic emission (AE) system, and the miniature camera monitoring system were employed to conduct true triaxial graded loading tests on sandstone containing circular holes at burial depths of 800 m, 1000 m, 1200 m, 1400 m, and 1600 m. The study investigated the patterns of mechanical properties and failure characteristics of porous sandstone at different burial depths. The results showed that the peak strength of the specimens increased quadratically with increasing burial depth; the failure process of porous sandstone could be divided into four stages: the calm period, the particle ejection period, the stable failure period, and the complete collapse period; as burial depth increases, the failure mode transitions from a composite tensile–shear crack type to a shear crack-dominated type, with the ratio of shear cracks to tensile cracks exhibiting quadratic growth and reduction, respectively; the particle ejection stage is characterised by low-frequency, low-amplitude signals, corresponding to the microcrack initiation stage, while the stable failure stage exhibits a sharp increase in low-frequency, high-amplitude signals, reflecting macrocrack propagation characteristics, with the spatial evolution of their locations ultimately forming a penetrating oblique shear failure zone; and peak stress analysis indicates that as burial depth increases, peak stress during the particle ejection phase first increases and then decreases, while peak stress during the stable failure phase first decreases and then stabilises. The duration of the pre-instability calm phase shows a significant negative correlation with burial depth. The research findings provide a theoretical basis for controlling tunnel rock mass stability and disaster warning. Full article

Part defects in additive manufacturing (AM) operations like laser sintering (LS) can negatively affect the quality and integrity of the manufactured parts. Therefore, it is important to understand and mitigate these part defects to improve the performance and safety of the manufactured parts. Integrating machine learning to detect part defects in AM can enable efficient, fast, and automated real-time monitoring, reducing the need for labor-intensive manual inspections. In this work, a novel approach incorporating a lightweight Visual Geometry Group (VGG) structure with soft attention is presented to detect powder bed defects (such as cracks, powder bed ditches, etc.) in laser sintering processes. The model was evaluated on a publicly accessible dataset (called LS Powder bed defects) containing 8514 images of powder bed images pre-split into training, validation, and testing sets. The proposed methodology achieved an accuracy of 98.40%, a precision of 97.45%, a recall of 99.40%, and an f1-score of 98.42% with a computation complexity of 0.797 GMACs. Furthermore, the proposed method achieved better performance than the state-of-the-art in terms of accuracy, precision, recall, and f1-score on LS powder bed images, while requiring lower computational power for real-time application. Full article

Bridges are fundamental components of transportation infrastructure, facilitating the efficient movement of people and goods. However, the conservation of heritage bridges introduces additional challenges, encompassing environmental, social, cultural, and economic dimensions of sustainability. This study investigates risk mitigation strategies for a heritage-listed, 120-year-old reinforced concrete bridge in Australia—one of the nation’s earliest examples of reinforced concrete construction, which remains operational today. The structure faces multiple risks, including passage of overweight vehicles, environmental degradation, progressive crack development due to traffic loading, and potential foundation scouring from an adjacent stream. Due to the heritage status and associated legal constraints, only non-invasive testing methods were employed. Ambient vibration testing was conducted to identify the bridge’s dynamic characteristics under normal traffic conditions, complemented by non-contact displacement monitoring using laser distance sensors. A digital twin structural model was subsequently developed and validated against field data. This model enabled the execution of various “what-if” simulations, including passage of overweight vehicles and loss of foundation due to scouring, providing quantitative assessments of potential risk scenarios. Drawing on insights gained from the case study, the article proposes a six-phase Incident Response Framework tailored for heritage bridge management. This comprehensive framework incorporates remote sensing technologies for incident detection, digital twin-based structural assessment, damage containment and mitigation protocols, recovery planning, and documentation to prevent recurrence—thus supporting the long-term preservation and functionality of heritage bridge assets. Full article

The instability of mine roadways is significantly influenced by the coupled effects of groundwater seepage and non-uniform loading. These interactions often induce localized plastic deformation and progressive failure, particularly in the roof and sidewall regions. Seepage elevates pore water pressure and deteriorates the mechanical properties of the rock mass, while non-uniform loading leads to stress concentration. The combined effect facilitates the propagation of microcracks and the formation of shear zones, ultimately resulting in localized instability. This initial damage disrupts the mechanical equilibrium and can evolve into severe geohazards, including roof collapse, water inrush, and rockburst. Therefore, understanding the damage and failure mechanisms of mine roadways at the mesoscale, under the combined influence of stress heterogeneity and hydraulic weakening, is of critical importance based on laboratory experiments and numerical simulations. However, the large scale of in situ roadway structures imposes significant constraints on full-scale physical modeling due to limitations in laboratory space and loading capacity. To address these challenges, a straight-wall circular arch roadway was adopted as the geometric prototype, with a total height of 4 m (2 m for the straight wall and 2 m for the arch), a base width of 4 m, and an arch radius of 2 m. Scaled physical models were fabricated based on geometric similarity principles, using defect-bearing sandstone specimens with dimensions of 100 mm × 30 mm × 100 mm (length × width × height) and pore-type defects measuring 40 mm × 20 mm × 20 mm (base × wall height × arch radius), to replicate the stress distribution and deformation behavior of the prototype. Uniaxial compression tests on water-saturated sandstone specimens were performed using a TAW-2000 electro-hydraulic servo testing system. The failure process was continuously monitored through acoustic emission (AE) techniques and static strain acquisition systems. Concurrently, FLAC^3D 6.0 numerical simulations were employed to analyze the evolution of internal stress fields and the spatial distribution of plastic zones in saturated sandstone containing pore defects. Experimental results indicate that under non-uniform loading, the stress–strain curves of saturated sandstone with pore-type defects typically exhibit four distinct deformation stages. The extent of crack initiation, propagation, and coalescence is strongly correlated with the magnitude and heterogeneity of localized stress concentrations. AE parameters, including ringing counts and peak frequencies, reveal pronounced spatial partitioning. The internal stress field exhibits an overall banded pattern, with localized variations induced by stress anisotropy. Numerical simulation results further show that shear failure zones tend to cluster regionally, while tensile failure zones are more evenly distributed. Additionally, the stress field configuration at the specimen crown significantly influences the dispersion characteristics of the stress–strain response. These findings offer valuable theoretical insights and practical guidance for surrounding rock control, early warning systems, and reinforcement strategies in water-infiltrated mine roadways subjected to non-uniform loading conditions. Full article

Acute oak decline (AOD) is a multifactorial disease that affects European oaks and represents a growing threat to forests. The disease results from a complex interaction between biotic and abiotic factors: the various environmental stresses, which vary depending on the area in question, and generally increased by climate change, predispose trees to attack by opportunistic pathogens. Among them, we focused on a bacterial consortium associated with AOD, consisting mainly of Brenneria goodwinii, Gibbsiella quercinecans, Rahnella victoriana, and Lonsdalea britannica, which produce degrading enzymes that contribute to phloem necrosis and the development of stem bleeds and bark cracks. However, the role of other pathogens, such as fungi, cannot be ruled out, but instead could be contributory. The potential involvement of xylophagous insects is also being studied, particularly Agrilus biguttatus, which, although, frequently associated with the disease, has not been conclusively demonstrated to act as an active vector of the bacteria. Currently, disease management requires integrated approaches, including monitoring and other forestry strategies to increase forest resilience. Given the phenomenon’s complexity and the risk of the future expansion of that bacterial consortium, further research is necessary to understand the dynamics and to develop effective containment strategies of AOD-associated bacteria. Full article

Orthotropic steel box girders and steel bridge decks are commonly applied to bridges. Because of the coupling of original defects and alternating forces, fatigue cracks are likely to appear in the structures. In order to ensure the life span of bridges, methods for automatic crack identification are needed. In this paper, we present a novel approach for crack detection and bridge condition monitoring by integrating convolutional neural networks (CNNs) with graph attention networks (GATs). At first, the original large-sized images are divided into small-sized patches, and these patches are input into a CNN architecture to extract features by decreasing dimensions. Then, the output features of the CNN model are considered as nodes of the graph. Considering the spatial relationship among the patches in the original image, the node from the central patch is connected to the nodes from its neighboring patches to constitute a graph structure, which can be input into a GAT model to learn the relationship among the nodes and update the features. Finally, the output features of GAT can judge whether the central patch contains cracks. Forty original large-sized images are cropped into abundant patches for the training of the CNN-GAT model. With the use of a sliding window technique, the trained CNN-GAT model is capable of finding the patches containing cracks in the test images with large sizes. From the test results, the location and the size of the cracks are exhibited, which indicates that the proposed approach is effective for crack identification in bridge structures. Full article

This study investigates the mechanical properties and microstructure of basalt fiber (BF) and nanoalumina (NA)-modified ultra-high-performance concrete with recycled aggregates (UHPC-RA) under high-temperature conditions. The effects of different replacement rates of recycled aggregates (RAs), BF content, and NA content on the compressive strength, splitting tensile strength, and elastic modulus were evaluated at ambient temperatures and after exposure to 200 °C, 400 °C, 600 °C, and 800 °C. The results show that mechanical properties decrease with temperature rise, but specimens containing BF exhibited improved crack resistance and better high-temperature integrity. The incorporation of NA enhanced the thermal stability and heat resistance of the concrete. Digital image correlation (DIC) was used to monitor real-time surface deformation, and scanning electron microscopy (SEM) analysis revealed improved microstructure with reduced porosity and cracks. This study demonstrates that the combination of BF and NA significantly enhances the high-temperature performance of UHPC-RA, which holds promising potential for applications in environments subjected to elevated temperatures. Full article

The use of Artificial Intelligence (AI) to detect defects such as concrete cracks in civil and transport infrastructure has the potential to make inspections less expensive, quicker, safer and more objective by reducing the need for on-site human labour. One deployment scenario involves using a drone to carry an embedded device and camera, with the device making localised predictions at the edge about the existence of defects using a trained convolutional neural network (CNN) for image classification. In this paper, we trained six CNNs, namely Resnet18, Resnet50, GoogLeNet, MobileNetV2, MobileNetV3-Small and MobileNetV3-Large, using transfer learning technology to classify images of concrete structures as containing a crack or not. To enhance the model’s robustness, the original dataset, comprising 3000 images of concrete structures, was augmented using salt and pepper noise, as well as motion blur, separately. The results show that Resnet50 generally provides the highest validation accuracy (96% with the original dataset and a batch size of 16) and the highest validation F1-score (95% with the original dataset and a batch size of 16). The trained model was then deployed on an Nvidia Jetson Nano device for real-time inference, demonstrating its capability to accurately detect cracks in both laboratory and field settings. This study highlights the potential of using transfer learning on Edge AI devices for Structural Health Monitoring, providing a cost-effective and efficient solution for automated crack detection in concrete structures. Full article

Roadway section form is an important part of the underground engineering structure, and it directly affects the overall stability of the roadway and the occurrence of underground disasters in coal mines. Based on this, this paper adopts a TYJ-500 electro-hydraulic servo rock shear rheology testing machine to conduct a uniaxial compression test on sandstone containing different prefabricated hole section morphology and analyzes the damage characteristics seen during the damage evolution process, with the help of a high-speed camera and acoustic emission monitoring equipment. The test results show that the pore morphology is the main factor affecting the mechanical parameters of sandstone, and the peak stress and elastic modulus of sandstone with pore sections have the characteristics of increasing and decreasing at the same time, except for the intact rock samples. The pore morphology exhibits central symmetry (circular holes and rectangular holes) damage, more pressure-shear cracks and shear cracks, and the acoustic emission characteristics of high-energy–low-amplitude–low-count of the “two low-trend and one high-trend characteristic curves” attributes; moreover, due to the special existence of its pore morphology, it leads to the rock samples having less energy accumulation and release. The axisymmetric hole types (trapezoidal holes and straight-wall domed holes) are damaged by tensile cracks and shear cracks, and their acoustic emission characteristics show the characteristic properties of “three high-trend characteristic curves” of high-energy–high-amplitude–high-count, and there is a strong elastic energy accumulation and output. The conclusions of this article can provide a certain theoretical basis for the design of coal mine roadway sections in underground structures, failure analysis, and stability evaluation of roadway structures. Full article

The introduced work deals with the microscopic analysis of metallographically prepared selected metal materials structures, using a scanning electron microscope (SEM). Prepared samples of seamless steel pipes were subjected to a thorough microscopic examination from the outer surface to the inner regions in order to interpret the specific structure, including the change in the inner surfaces due to wear. The experiment demonstrated that the microstructure and character of the surfaces play a key role in the behavior of metallic materials in real conditions of hot water heating. Four pipe samples were monitored according to their use. The unused steel pipe (designated as sample No. 1) exhibited a rough outer surface with identified inclusions, while the used pipe (designated as sample No. 2) showed marks of intergranular corrosion and significant wear after long-term use. The older pipe (designated as sample No. 3) showed a decarburized area and inclusions containing sulfides and aluminum. The steel pipe with corrosion layers (designated as sample No. 4) exhibited a continuous corrosion layer with cavitation and cracks. The results of this study offer a comprehensive view of the influence of the nature of microstructure and wear on water flow in metal pipes, with an emphasis on the identification of possible risks associated with geometry change, corrosion, and wear. The findings form the basis for predicting degradation and appropriate maintenance in order to ensure their long and reliable service life under real conditions of use. They offer the possibility of continuing and expanding research and analysis of the use of metallic materials in comparison with polymers and composites. Full article

With the extension of road service life, cracks are the most significant type of pavement distress. To monitor road conditions and avoid excessive damage, pavement crack detection is absolutely necessary and an indispensable part of road periodic maintenance and performance assessment. The development and application of computer vision have provided modern methods for crack detection, which are low in cost, less labor-intensive, continuous, and timely. In this paper, an intelligent model based on a target detection algorithm in computer vision was proposed to accurately detect and classify four classes of cracks. Firstly, by vehicle-mounted camera capture, a dataset of pavement cracks with complicated backgrounds that are the most similar to actual scenarios was built, containing 4007 images and 7882 crack samples. Secondly, the YOLOv5 framework was improved from the four aspects of the detection layer, anchor box, neck structure, and cross-layer connection, and thereby the network’s feature extraction capability and small-sized-target detection performance were enhanced. Finally, the experimental results indicated that the proposed model attained an AP of the four classes of 81.75%, 83.81%, 98.20%, and 92.83%, respectively, and a mAP of 89.15%. In addition, the proposed model achieved a 2.20% missed detection rate, representing a 6.75% decrease over the original YOLOv5. These results demonstrated the effectiveness and practicality of our proposed model in addressing the issues of low accuracy and missed detection for small targets in the original network. Overall, the implementation of computer vision-based models in crack detection can promote the intellectualization of road maintenance. Full article