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10 pages, 225 KiB  
Article
Lower Airway Diseases in the Paediatric Population: A Two-Year, Single-Centre, Retrospective Study
by Anna Ferrero, Antonia Versace, Marco Denina, Giulia Spagna, Alessandra Vincenza Fera, Margherita Conrieri and Claudia Bondone
J. Clin. Med. 2025, 14(2), 384; https://doi.org/10.3390/jcm14020384 - 9 Jan 2025
Abstract
Background: Lower airway diseases in children are one of the major causes of hospitalisation. This study aimed to evaluate the characteristics of children admitted to a tertiary pediatric hospital diagnosed with lower airway disease and to identify differences between age groups and [...] Read more.
Background: Lower airway diseases in children are one of the major causes of hospitalisation. This study aimed to evaluate the characteristics of children admitted to a tertiary pediatric hospital diagnosed with lower airway disease and to identify differences between age groups and the two years of the study. Methods: In this single-centre retrospective observational study, demographic and clinical information about children hospitalised in the emergency pediatric ward and diagnosed with lower respiratory disease from 1 June 2021 to 30 June 2023 were retrospectively reviewed. Results: A total of 410 episodes of hospitalisation for lower airway diseases were registered. In 83.9% of cases, the patient needed hospitalisation for respiratory failure, and children <1 year of age were at higher risk. Rhinovirus and respiratory syncytial virus (RSV) were the leading causes of lower respiratory tract infections. No death has been recorded. In 8.8% of cases, the patient was admitted to the Pediatric Intensive Care Unit. In 2021–2022, we recorded more hospitalisations for bronchiolitis with RSV as the primary pathogen detected and more patients were admitted to the hospital for respiratory failure. In 2022–2023, we registered more admissions for bacterial pneumonia and the need for intravenous therapy. Conclusions: Lower respiratory tract diseases are frequent in the pediatric population, and the risk of respiratory failure is higher. Analysing the differences between the two years of study, we underline how the COVID-19 pandemic has changed the epidemiology of acute respiratory infections in children. Full article
(This article belongs to the Section Clinical Pediatrics)
20 pages, 19989 KiB  
Article
The Icing Characteristics of Post Insulators in a Natural Icing Environment
by Zhijin Zhang, Jiahui Tu, Yuanpeng Zhang, Xingliang Jiang and Zhenbing Zhu
Atmosphere 2025, 16(1), 64; https://doi.org/10.3390/atmos16010064 - 9 Jan 2025
Viewed by 112
Abstract
Icing significantly reduces the electrical performance of insulators, and grid failures caused by insulator icing are common. Currently, most research on the flashover characteristics of insulators under icing conditions focuses on artificially iced suspension insulators, with limited studies on post insulators under natural [...] Read more.
Icing significantly reduces the electrical performance of insulators, and grid failures caused by insulator icing are common. Currently, most research on the flashover characteristics of insulators under icing conditions focuses on artificially iced suspension insulators, with limited studies on post insulators under natural icing conditions. The shed spacing of post insulators is smaller, making them more prone to bridging by icicles in the same icing environment, which exacerbates insulation problems. Therefore, investigating the icing characteristics of post insulators is crucial. In this study, natural icing growth was observed on seven different types of post insulators at the Xuefeng Mountain Energy Equipment Safety National Observation and Research Station. The icing morphology and characteristics of these insulators were examined. The main conclusions are as follows: (1) the icing type and morphology of post insulators are influenced by meteorological conditions, with more severe icing observed on the windward side. (2) The icing mass and icicle length of the insulator increase nonlinearly with icing time. Specifically, during the glaze icing period from 0 to 8 h, the ice mass on the Type V composite post insulator was 3.89 times greater than that during the 13-to-18 h period. (3) Within the same icing cycle, the icing growth rate on composite post insulators is faster than on porcelain post insulators. (4) Compared to suspension insulators, the sheds of post insulators are more easily bridged by icicles. Notably, when the sheds of post insulators are bridged by icicles, the length of icicles on suspension insulators is only half of the gap size. Full article
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<p>Physical diagram of post insulators.</p>
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<p>Xuefeng Mountain Energy Equipment Safety National Observation and Research Station.</p>
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<p>The rotating conductor and vernier caliper.</p>
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<p>Glaze icing at different times for Type I to VII insulators: (<b>a</b>) t = 5 h; (<b>b</b>) t = 10 h; (<b>c</b>) t = 15 h.</p>
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<p>Glaze icing at different times. Glaze icing on the windward side and lateral side of type VI post insulator: (<b>a</b>) windward side; (<b>b</b>) leeward side.</p>
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<p>Changes in environmental parameters during the glaze icing process: (<b>a</b>) temperature; (<b>b</b>) wind-speed; (<b>c</b>) relative humidity; (<b>d</b>) rainfall.</p>
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<p>Mixed-phase icing at different times for type II, III and V insulators: (<b>a</b>) t = 7 h; (<b>b</b>) t = 20 h; (<b>c</b>) t = 30 h.</p>
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<p>Mixed-phase icing on different sides of type II post insulator: (<b>a</b>) Windward side; (<b>b</b>) Leeward side.</p>
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<p>Changes in environmental parameters during the mixed-phase icing process: (<b>a</b>) temperature; (<b>b</b>) windspeed; (<b>c</b>) relative humidity; (<b>d</b>) rainfall.</p>
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<p>Curve of glaze icing parameters changing with time: (<b>a</b>) Variation curve of icicle length over time; (<b>b</b>) Variation curve of icing mass over time; (<b>c</b>) Variation curve of rotating conductor icing thickness over time; (<b>d</b>) Relationship curve between icing mass and rotating conductor icing thickness.</p>
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<p>Curve of mixed-phase icing parameters changing with time: (<b>a</b>) Variation curve of icing mass over time; (<b>b</b>) Variation curve of rotating conductor icing thickness over time. (<b>c</b>) Relationship curve between icing mass and rotating conductor icing thickness.</p>
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<p>Comparison of the icing morphology between post insulator (I) and suspension insulator (II): (<b>a</b>) glaze icing; (<b>b</b>) rime icing.</p>
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12 pages, 1140 KiB  
Article
Plasma Concentrations of Benzylpenicillin and Cloxacillin in Infective Endocarditis—With Special Reference to Delayed Hypersensitivity Reactions
by Malin Hägglund, Ulrika Snygg-Martin, Lars Olaison, Michael Stofkoper, Bert Ove Larsson and Magnus Brink
Antibiotics 2025, 14(1), 56; https://doi.org/10.3390/antibiotics14010056 - 9 Jan 2025
Viewed by 120
Abstract
Background: Current antibiotic regimens for infective endocarditis (IE) are effective but pose a high risk of delayed hypersensitivity reactions (DHR). Dose adjustments guided by therapeutic drug monitoring (TDM) could mitigate these risks while maintaining treatment efficacy. This study aimed to investigate the plasma [...] Read more.
Background: Current antibiotic regimens for infective endocarditis (IE) are effective but pose a high risk of delayed hypersensitivity reactions (DHR). Dose adjustments guided by therapeutic drug monitoring (TDM) could mitigate these risks while maintaining treatment efficacy. This study aimed to investigate the plasma concentration of benzylpenicillin and cloxacillin in patients with IE and explore associations between antibiotic concentrations and DHR. Methods: Plasma concentrations of benzylpenicillin and cloxacillin were measured as centre (midpoint concentrations between consecutive doses) and trough values during the first and third weeks of treatment in patients with IE. Patient characteristics and outcomes, including DHR, were documented. Results: A total of 55 patients were included, with 37 patients (67%) receiving benzylpenicillin and 18 (33%) receiving cloxacillin. The 90-day mortality rate was 3%. Both centre and trough concentration exhibited substantial interpatient variation for the two antibiotics, while intra-patient variability between weeks 1 and 3 remained low for most patients. Kidney function could explain, at best, 54% of the variation, and a multiple regression model including kidney function, body mass index, age, and albumin explained up to 68% of the variation for benzylpenicillin. There was no relation between high plasma concentration and the prevalence of DHR; conversely, we observed a tendency of low plasma concentrations in these patients. Conclusions: This study revealed significant interindividual variation in plasma concentrations for both studied penicillins. TDM might be useful in situations where concentrations are hard to predict, such as severe obesity or kidney failure. Additionally, we found no indication that high plasma concentrations are related to the prevalence of DHR. Full article
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<p>Flowchart of the study. * Missing single sample.</p>
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<p>Centre and trough total plasma concentrations of benzylpenicillin and cloxacillin in treatment weeks 1 and 3. Benzylpenicillin, median (IQR); centre value, median 10 (5.1–21.5) and 20 (6.5–29.0), trough value 1.6 (0.79–5.3) and 3.6 (1.1–6.8). Cloxacillin, median (IQR); centre value 18 (7.3–27.0) and 17.5 (9.1–25.8). Trough value 3.7 (1.7–6.2) and 3.85 (1.7–7.4).</p>
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<p>Paired plasma concentrations (treatment weeks 1 and 3) of benzylpenicillin and cloxacillin. Wilcoxon signed-rank test with no significant intra-patient difference between weeks. Effective pairing with Spearman (one-tailed) 0.7–0.9, <span class="html-italic">p</span> &lt; 0.01.</p>
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17 pages, 5174 KiB  
Article
Mechanical Properties and Optimization Strategies of Tree Fork Structures
by Yi-Sen Peng, Bai-You Cheng and Tung-Chi Liu
Plants 2025, 14(2), 167; https://doi.org/10.3390/plants14020167 - 9 Jan 2025
Viewed by 148
Abstract
Trees are complex and dynamic living structures, where structural stability is essential for survival and for public safety in urban environments. Tree forks, as structural junctions, are key to tree integrity but are prone to failure under stress. The specific mechanical contributions of [...] Read more.
Trees are complex and dynamic living structures, where structural stability is essential for survival and for public safety in urban environments. Tree forks, as structural junctions, are key to tree integrity but are prone to failure under stress. The specific mechanical contributions of their internal conical structures remain largely unexplored. This study explores how conical structures optimize stress distribution, filling key gaps in the understanding of tree fork mechanics and supporting safety assessments. This study focused on the following factors: (1) external shape, (2) internal conical reinforcement, (3) interface of the conical connection, and (4) material changes within the conical structure. By analyzing physical samples to extract structural and morphological features, simulating these features in controlled variable models, and performing finite element analysis, we explored mechanical behavior, stress distribution, and performance characteristics, revealing the factors and mechanisms that strengthen tree forks. Insights from this study may facilitate safety assessments and inform pruning strategies for urban tree management. Full article
(This article belongs to the Special Issue Development of Woody Plants)
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<p>Structural analysis of a branch–stem junction. The (<b>left</b>) image shows the external morphology of a beech tree’s branch–trunk junction, highlighting the natural configuration at the tree fork. The (<b>middle</b>) image, adapted from a study conducted by Liu et al. [<a href="#B4-plants-14-00167" class="html-bibr">4</a>], indicates four key features of the junction that contribute to structural stability: (a) the U-shaped connection at the tree fork, (b) a collar around the base of the branch, (c) a bark ridge along the junction, and (d) a thickened area below the trunk. The (<b>right</b>) image, based on a model developed by Shigo [<a href="#B2-plants-14-00167" class="html-bibr">2</a>], shows the intertwined internal structure between the branch collar and the trunk collar (indicated by arrows), forming a unique interlocking fiber system that enhances the mechanical stability of the branch–stem junction.</p>
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<p>Diagrams depicting the distribution of stress and displacement. Stress and displacement distributions for various branch–stem junctions are presented. Surface stress and internal stress (equivalent and shear stress) are depicted alongside total displacement, with stress and displacement values shown through color gradients (e.g., red for high values and blue for low values).</p>
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<p>Stress–position curves for key areas analyzed using seven models. The figure indicates changes in stress, shear stress, and total displacement across the surface, the lower surface, and the internal region. Curves in the same column have the same y-axis and x-axis; no specific ranges or units are indicated. The curves indicate the stress and displacement at the branch–stem junction for various structural designs.</p>
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<p>Distribution of stress at the surface and lower surface regions of various branch–stem junctions. The (<b>left</b>) graph depicts the distribution of surface stress across horizontal positions, whereas the (<b>right</b>) graph depicts the distribution of lower surface stress across vertical positions. Vertical lines indicate two crucial points: the right line indicates the branch collar boundary, whereas the left line indicates the branch–stem junction. The length of the x-axis differs between the graphs because of differences in the curve extraction path.</p>
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<p>Correlation between the internal stress and the position across branch–stem junctions. The (<b>left</b>) diagram indicates internal stress, whereas the (<b>right</b>) diagram indicates internal shear stress.</p>
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<p>Displacement scenarios of each model. The (<b>left</b>) image shows the overall displacement distribution, with the red arrow indicating the localized region that is magnified in the (<b>right</b>) image presents localized displacement results. Based on the displacement data, the support capacity of the models is ranked from highest to lowest as follows: B2 &gt; B1 &gt; A1 &gt; C1 &gt;&gt; C2 &gt; A2 &gt; C3.</p>
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<p>Panels (<b>A</b>,<b>B</b>) illustrate the stress concentration phenomenon and the resulting failure at the interface. Panels (<b>C</b>,<b>D</b>) depict the strengthening effects and the stress relaxation achieved through the complex interface structure. Panels (<b>E</b>,<b>F</b>) further demonstrate the enhanced support capacity of branches following resinous wood formation. Dashed lines indicate the boundary between the tapered conical structure and the main trunk structure. Red highlights high-stress regions, while blue and gray indicate areas of low stress.</p>
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<p>Flowchart depicting the experimental process.</p>
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<p>Experimental materials and the modeling process for branch–stem junctions. The (<b>left</b>) image shows the original wood sample, scanned using a Revopoint POP 2 scanner (Revopoint 3D Technologies Inc., Xi’an and Shenzhen, China). The (<b>middle</b>) image shows the point cloud data model generated using data from the scan. The (<b>right</b>) image shows the branch–stem junction structure model developed for finite element analysis based on the point cloud data.</p>
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<p>Illustrates the key morphological features of the branch–stem internal structure. (<b>a</b>) The original tissue configuration at the branch–stem junction. (<b>b</b>) The conical core structure revealed after decay of the surrounding material. (<b>c</b>) Multiple stepped truncated cones form the conical structure, decreasing in size as they transition from the branch toward the main stem. (<b>d</b>) Beneath the conical core, the branch fibers bend and reorient, eventually aligning with the main stem’s vertical fiber orientation.</p>
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<p>Branch–stem model design. The figure shows different branch–stem junction models. (<b>A1</b>) represents a cylindrical connection model without tree fork characteristics, simulating a basic branch–stem junction. (<b>A2</b>) incorporates a tree fork connection. (<b>B1</b>) simulates a double structure with surface attachment. (<b>B2</b>) represents a tapered insertion model simulating the independent connection between the trunk and the branch. (<b>C1</b>) mimics the natural external shape and tapered insertion of a tree but does not include internal connections formed through alternating growth. (<b>C2</b>) has the same external shape as C1 but simulates the internal reinforcement resulting from alternating growth. (<b>C3</b>) accounts for the material hardening properties of resinification and heartwood formation in the side branch, simulating the effect of structural strengthening.</p>
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<p>This figure illustrates the process of extracting key mechanical parameters from finite element analysis (FEA) results, highlighting critical sampling regions and data mapping. Red-highlighted areas denote specific structural locations where stress and displacement values were recorded, underscoring their importance in evaluating mechanical performance. Sampling was conducted at four distinct locations: two on the surface—the upper surface (a) and lower surface (b)—and two internal regions—the interface area (c) and the branch axis (d). The extracted data were subsequently mapped and visualized to compare stress patterns and total displacement across models.</p>
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43 pages, 6510 KiB  
Article
Structural Fatigue Life Monitoring with Piezoelectric-Based Sensors: Fundamentals, Current Advances, and Future Directions
by Aliakbar Ghaderiaram, Erik Schlangen and Mohammad Fotouhi
Sensors 2025, 25(2), 334; https://doi.org/10.3390/s25020334 - 8 Jan 2025
Viewed by 299
Abstract
Structural fatigue can lead to catastrophic failures in various engineering applications and must be properly monitored and effectively managed. This paper provides a state-of-the-art review of recent developments in structural fatigue monitoring using piezoelectric-based sensors. Compared to alternative sensing technologies, piezoelectric sensors offer [...] Read more.
Structural fatigue can lead to catastrophic failures in various engineering applications and must be properly monitored and effectively managed. This paper provides a state-of-the-art review of recent developments in structural fatigue monitoring using piezoelectric-based sensors. Compared to alternative sensing technologies, piezoelectric sensors offer distinct advantages, including compact size, lightweight design, low cost, flexible formats, and high sensitivity to dynamic loads. The paper reviews the working principles and recent advancements in passive piezoelectric-based sensors, such as acoustic emission wave and strain measurements, and active piezoelectric-based sensors, including ultrasonic wave and dynamic characteristic measurements. These measurements, captured under in-service dynamic strain, can be correlated to the remaining structural fatigue life. Case studies are presented, highlighting applications of fatigue life monitoring in metals, polymeric composites, and reinforced concrete structures. The paper concludes by identifying challenges and opportunities for advancing piezoelectric-based sensors for fatigue life monitoring in engineering structures. Full article
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<p>Fatigue-induced damage mechanisms in different engineering materials. (<b>a</b>) Damage evolution of a titanium alloy [<a href="#B6-sensors-25-00334" class="html-bibr">6</a>], (<b>b</b>) damage evolution in fiber-reinforced concrete [<a href="#B7-sensors-25-00334" class="html-bibr">7</a>], and (<b>c</b>) damage evolution of a polymer composite [<a href="#B8-sensors-25-00334" class="html-bibr">8</a>].</p>
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<p>Examples of disastrous events caused by fatigue failure. (<b>a</b>) De Havilland comet plane crashes, metallic materials, 1954. (<b>b</b>) A highway bridge collapses, reinforced concrete, 2018. (<b>c</b>) Titan vessel crashes, fiber-reinforced composite, 2023.</p>
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<p>Comparison of the piezoelectric coefficient in different types of piezoelectric materials [<a href="#B32-sensors-25-00334" class="html-bibr">32</a>].</p>
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<p>The piezoelectric sensor’s working principle under compression.</p>
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<p>Comparative analysis of FLM techniques employing piezoelectric sensors.</p>
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<p>The proportion of piezoelectric sensor types used for FLM in the reviewed papers.</p>
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<p>A simple demonstration of the UT NDT method principle in the pitch–catch mode.</p>
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<p>Schematic of the position of the UT sensors placed on the Al specimen.</p>
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<p>Comparison of linear and nonlinear UT measurements in FLM.</p>
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<p>Schematic illustration of the system for monitoring the depth of a surface crack on a structure with Rayleigh waves [<a href="#B70-sensors-25-00334" class="html-bibr">70</a>].</p>
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<p>(<b>a</b>) A simple sketch of the AE experimental setup, and (<b>b</b>) a schematic of an AE waveform [<a href="#B80-sensors-25-00334" class="html-bibr">80</a>].</p>
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<p>(<b>a</b>) The AE event localization procedures. (<b>b</b>) Prediction of fatigue-induced delamination size in laminated composites by AE [<a href="#B79-sensors-25-00334" class="html-bibr">79</a>].</p>
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<p>The underlying principle of the EMI method [<a href="#B97-sensors-25-00334" class="html-bibr">97</a>].</p>
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<p>Strain profile measurement and its link with the S-N curves, considering the variation in the applied stress and frequency of the stress.</p>
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<p>(<b>a</b>) Fatigue test setup schematic. (<b>b</b>) PZT sensor output in fatigue loading [<a href="#B108-sensors-25-00334" class="html-bibr">108</a>].</p>
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17 pages, 6888 KiB  
Article
Influencing Factors of Shear Instability Characteristics of Rock Joints: Experimental and Theoretical Study
by Hangyu Dong, Baohua Guo, Qing Ma, Kai Si and Hongjian Wang
Appl. Sci. 2025, 15(2), 561; https://doi.org/10.3390/app15020561 - 8 Jan 2025
Viewed by 302
Abstract
To investigate the influencing factors of the shear failure behavior of rock joints, especially the shear instability characteristics, direct shear tests were performed on marble joints with various grain sizes under different constant normal loads (CNLs). The experimental results show that [...] Read more.
To investigate the influencing factors of the shear failure behavior of rock joints, especially the shear instability characteristics, direct shear tests were performed on marble joints with various grain sizes under different constant normal loads (CNLs). The experimental results show that the grain size and CNL have significant effects on the shear mechanical properties of rock joints. The peak shear strength (τp), peak shear displacement (up), post-peak modulus (S), and stress drop (Δτ) of rock joints all increase first and then decrease with the increase in grain size, but they increase with the increase in CNL. The mineral composition and microstructure also have a certain influence on the shear mechanical properties of rock joints. In addition, the post-peak soften modulus (Sp) was proposed to describe the shear instability characteristics of rock joints, and its relationship with grain size and CNL was established. The mechanical model of the shear instability of rock joints was established, and the shear instability criterion of rock joints was proposed based on the stiffness criterion and the proposed post-peak soften modulus (Sp). This paper further reveals the shear instability mechanism of rock joints, which can provide a reference for the stability analysis of jointed rockmass. Full article
(This article belongs to the Special Issue Advances and Challenges in Rock Mechanics and Rock Engineering)
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<p>RDS-200 direct shear test system of GCTS. (<b>a</b>) Direct shear test apparatus; (<b>b</b>) three-view drawing of shear boxes.</p>
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<p>Microscopic pictures of thin sections of marble specimens under cross-polarized light.</p>
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<p>Artificial rock joint and its relationship between grain size and <span class="html-italic">JRC</span>. (<b>a</b>) Split tool, (<b>b</b>) Tensile fractures, (<b>c</b>) 3D morphology scan and analysis system, (<b>d</b>) Relationship between <span class="html-italic">JRC</span> and grain size.</p>
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<p>Comparison of shear stress–displacement curves under different normal loads (<span class="html-italic">CNLs</span>).</p>
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<p>Comparison of shear stress–displacement curves under different normal loads (<span class="html-italic">CNLs</span>).</p>
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<p>Comparison of shear stress–displacement curves with various grain sizes (<span class="html-italic">d</span>).</p>
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<p>Model curve for shear stress vs. shear displacement.</p>
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<p>Influence of grain size (<span class="html-italic">d</span>) on shear mechanical properties of rock joints. (<b>a</b>) Peak shear displacement <span class="html-italic">τ<sub>p</sub></span>, (<b>b</b>) peak shear displacement <span class="html-italic">u<sub>p</sub></span>, (<b>c</b>) post-peak modulus <span class="html-italic">S</span>, and (<b>d</b>) stress drop Δ<span class="html-italic">τ</span>.</p>
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<p>Influence of <span class="html-italic">CNL</span> on shear mechanical properties of rock joints. (<b>a</b>) Peak shear displacement <span class="html-italic">τ<sub>p</sub></span>, (<b>b</b>) peak shear displacement <span class="html-italic">u<sub>p</sub></span>, (<b>c</b>) post-peak modulus <span class="html-italic">S</span>, and (<b>d</b>) stress drop Δ<span class="html-italic">τ</span>.</p>
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<p>Schematic diagram of shear instability mechanism of rock joints. (<b>a</b>) Initial stage <span class="html-italic">o</span>, (<b>b</b>) Elastic stage <span class="html-italic">o–a</span>, (<b>c</b>) Yield stage <span class="html-italic">a–b</span>, (<b>d</b>) Soften stage <span class="html-italic">b–c</span>, (<b>e</b>) Residual stage <span class="html-italic">c–d</span>, (<b>f</b>) Shear stress–plastic shear displacement.</p>
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<p>Shear stress–plastic displacement curves of marble joints under different <span class="html-italic">CNLs</span>.</p>
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<p>Influence of <span class="html-italic">CNL</span> and <span class="html-italic">d</span> on <span class="html-italic">S<sub>p</sub></span> of rock joints. (<b>a</b>) Relationship between <span class="html-italic">S<sub>p</sub></span> and <span class="html-italic">d</span>, (<b>b</b>) Relationship between <span class="html-italic">S<sub>p</sub></span> and <span class="html-italic">CNL</span>.</p>
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<p>Mechanical model of shear instability of rock joints.</p>
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<p>Three-dimensional surface for shear instability parameter and its influencing factors.</p>
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<p>Shear instability model derivation and discriminant logical diagram.</p>
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23 pages, 5985 KiB  
Article
A Multi-Branch Convolution and Dynamic Weighting Method for Bearing Fault Diagnosis Based on Acoustic–Vibration Information Fusion
by Xianming Sun, Yuhang Yang, Changzheng Chen, Miao Tian, Shengnan Du and Zhengqi Wang
Actuators 2025, 14(1), 17; https://doi.org/10.3390/act14010017 - 7 Jan 2025
Viewed by 230
Abstract
Rolling bearings, as critical components of rotating machinery, directly affect the reliability and efficiency of the system. Due to extended operation under high load, harsh environmental conditions, and continuous use, bearings become more susceptible to failure, leading to a higher likelihood of malfunction. [...] Read more.
Rolling bearings, as critical components of rotating machinery, directly affect the reliability and efficiency of the system. Due to extended operation under high load, harsh environmental conditions, and continuous use, bearings become more susceptible to failure, leading to a higher likelihood of malfunction. To prevent sudden failures, reduce downtime, and optimize maintenance strategies, early and accurate diagnosis of rolling bearing faults is essential. Although existing methods have achieved certain success in processing acoustic and vibration signals, they still face challenges such as insufficient feature fusion, inflexible weight allocation, lack of effective feature selection mechanisms, and low computational efficiency. To address these challenges, we propose a dynamic weighted multimodal fault diagnosis model based on the fusion of acoustic and vibration information. This model aims to enhance feature fusion, dynamically adapt to signal characteristics, optimize feature selection, and reduce computational complexity. The model incorporates an adaptive fusion method based on a multi-branch convolutional structure, enabling unified processing of both acoustic and vibration signals. At the same time, a cross-modal dynamic weighted fusion mechanism is employed, allowing the real-time adjustment of weight distribution based on signal characteristics. By utilizing an attention mechanism for dynamic feature selection and weighting, the robustness of classification is further improved. Additionally, when processing acoustic signals, a depthwise separable convolutional network is used, effectively reducing computational complexity. Experimental results demonstrate that our method significantly outperforms other algorithms in terms of convergence speed and final performance. Additionally, the accuracy curve during training showed minimal fluctuation, reflecting higher robustness. The model achieved over 99% diagnostic accuracy under all signal-to-noise ratio (SNR) conditions, showcasing exceptional robustness and noise resistance in both noisy and high-SNR environments. Furthermore, its superiority across different data scales, especially in small-sample learning and stability, highlights its strong generalization capability. Full article
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<p>Structure of a convolutional neural network.</p>
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<p>Structure diagram of a depthwise separable convolutional neural network.</p>
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<p>Residual neural network structure diagram.</p>
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<p>CBAM Module.</p>
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<p>MFDM-AVDW Model.</p>
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<p>Test rig.</p>
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<p>Time-domain signal plots of acoustic signals for seven different fault types.</p>
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<p>Time-domain signal plots of vibration signals for seven different fault types.</p>
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<p>Test accuracy and loss.</p>
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<p>Test accuracy and loss rate.</p>
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<p>Comparison of confusion matrices for different algorithms.</p>
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<p>t-SNE feature visualization under −5 dB.</p>
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15 pages, 6899 KiB  
Article
Influence of Potting Radius on the Structural Performance and Failure Mechanism of Inserts in Sandwich Structures
by Filippos Filippou and Alexis Τ. Kermanidis
Machines 2025, 13(1), 34; https://doi.org/10.3390/machines13010034 - 7 Jan 2025
Viewed by 235
Abstract
In this study, the mechanical performance and failure modes of cold-potted inserts within sandwich structures were examined, focusing on the influence of the potting radius, while maintaining constant insert radius and specimen characteristics. In this research, destructive testing was used to evaluate the [...] Read more.
In this study, the mechanical performance and failure modes of cold-potted inserts within sandwich structures were examined, focusing on the influence of the potting radius, while maintaining constant insert radius and specimen characteristics. In this research, destructive testing was used to evaluate the pull out, load-carrying capacity, and failure mechanisms of the inserts. The methods of stiffness degradation and acoustic emissions (AE) were employed for structural health monitoring to capture real-time data on failure progression, including core buckling, core rupture, and skin delamination. The results indicated that increasing the potting radius significantly altered the failure modes and critical failure load of the insert system. A critical potting radius was identified where maximum stiffness was achieved. Beyond this point, insert fracture became the dominant failure mode, with minimal damage to the surrounding core and CFRP skins. Larger potting radii also led to reduced displacement at failure, increased ultimate loads, and elevated stiffness, which were maintained until sudden structural failure. Through detailed isolation and observation of each failure event and with the use of AE data, precise identification of system damage in real time was allowed, offering insights into the progression and causes of failure. Full article
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<p>Potting geometry.</p>
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<p>(<b>a</b>) Initial cut; (<b>b</b>) Undercutting; (<b>c</b>) Potting of the insert; (<b>d</b>) Final potted insert system.</p>
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<p>(<b>a</b>) Insert tensile out-of-plane loading fixture front view; (<b>b</b>) Test fixture side view.</p>
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<p>Distance (e) from free edge.</p>
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<p>(<b>a</b>,<b>b</b>) Characteristic force, displacement and acoustic data diagrams for R<sub>p</sub> = 8.6 mm and R<sub>p</sub> = 9.3 mm, respectively.</p>
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<p>(<b>a</b>,<b>b</b>) Characteristic lower skin indentation for R<sub>p</sub> = 8.6 mm and R<sub>p</sub> = 9.3 mm, respectively; (<b>c</b>) Upper skin extrusion.</p>
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<p>(<b>a</b>,<b>b</b>) Characteristic cross-sections for R<sub>p</sub> = 8.6 mm and R<sub>p</sub> = 9.3 mm, respectively; (<b>c</b>) Stereoscopical observation of core buckling; (<b>d</b>) Stereoscopical observation of shear rupture and skin delamination.</p>
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<p>Characteristic force, displacement and acoustic data diagram for R<sub>p</sub> = 10 mm.</p>
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<p>(<b>a</b>) Lower skin indentation; (<b>b</b>,<b>c</b>) Insert extrusion.</p>
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<p>(<b>a</b>) Characteristic cross-sections of R<sub>p</sub> = 10 mm; (<b>b</b>) Stereoscopical observation of core buckling; (<b>c</b>) Stereoscopical observation of shear rupture; (<b>d</b>) Absence of delamination; (<b>e</b>) Stereoscopical observation of insert lower flange failure.</p>
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<p>(<b>a</b>,<b>b</b>) Characteristic force, displacement and acoustic data diagrams for R<sub>p</sub> = 11 mm and R<sub>p</sub> = 12.5 mm, respectively.</p>
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<p>(<b>a</b>,<b>b</b>) Characteristic lower skin indentation absence for R<sub>p</sub> = 11 mm and R<sub>p</sub> = 12.5 mm, respectively; (<b>c</b>,<b>d</b>) Insert extrusion.</p>
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<p>(<b>a</b>,<b>b</b>) Characteristic cross-sections for R<sub>p</sub> = 11 mm and R<sub>p</sub> = 12.5 mm, respectively; (<b>c</b>) Stereoscopical observation of insert lower flange failure; (<b>d</b>) Stereoscopical observation of core buckling.</p>
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<p>(<b>a</b>,<b>b</b>) Average experimental results for each potting radius (R<sub>p</sub>).</p>
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24 pages, 38222 KiB  
Article
Borehole Failure Mechanics and Influencing Factors in a Gas-Bearing Soft Coal Seam Under Complex Geological Conditions
by Xuexi Chen, Zhilong Yan, Jiaying Hu, Tao Yang, Jihong Sun, Yunqi Tao and Xingyu Chen
Processes 2025, 13(1), 146; https://doi.org/10.3390/pr13010146 - 7 Jan 2025
Viewed by 264
Abstract
The present research focuses on the mechanical properties and stress evolution of gas-bearing soft coal seams during drilling, which are affected by a multitude of complex factors such as high ground stress, gas pressure, and pre-existing fractures. In this study, a combination of [...] Read more.
The present research focuses on the mechanical properties and stress evolution of gas-bearing soft coal seams during drilling, which are affected by a multitude of complex factors such as high ground stress, gas pressure, and pre-existing fractures. In this study, a combination of PFC2D (Particle Flow Code in 2 Dimensions) numerical simulation and theoretical analysis is employed to investigate the borehole mechanics and fracture evolution characteristics under diverse complex conditions and to determine the factors influencing different forms of borehole failure in soft coal seams. The principal outcomes are as follows: (1) At a horizontal displacement of 0.1 m from the borehole orifice of the soft coal seam, a stress peak value of 13.9 MPa is attained; the peak value of the coal body contact force is 15.8 MPa; the peak value of the displacement is 0.008 m; and the porosity of the coal body around the borehole ranges from 0.14 to 0.35. (2) With an increase in the number of pre-existing fractures, the inclination progressively aligns with that of the pre-existing fractures. Maximum values of contact force (5.13–51.9 MPa), stress (3.19–37.2 MPa), shape dimension, and fracture angle (140–150°) are achieved under the highest lateral pressure coefficient and gas pressure (1.5 MPa). (3) The borehole energy is directly proportional to the number of pre-existing fractures, the lateral pressure coefficient, and gas pressure. The number of pre-existing fractures has the most significant impact on the damage degree, followed by the lateral pressure coefficient and then the gas pressure. (4) Two types of failure are identified: fracture-dominated failure, which is controlled by the geometric distribution of pre-existing fractures, and stress-dominated failure, wherein the failure zone gradually extends both upward and downward with an increasing lateral pressure coefficient. Full article
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<p>Distribution of (<b>a</b>) axial and (<b>b</b>) radial stresses around a borehole in a coal body.</p>
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<p>Schematic of the mechanical strength reduction in gas-bearing coal.</p>
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<p>Principle of calculating fractal dimensions via the two-dimensional box-counting method: (<b>a</b>) Fracture image; (<b>b</b>) Image binarization; (<b>c</b>) Fracture extraction results; (<b>d</b>) Grid division with side length <span class="html-italic">r</span>; (<b>e</b>) Grid division with side length <span class="html-italic">r</span>/2; (<b>f</b>) Fractal dimension calculation.</p>
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<p>Calibration results of the uniaxial stress–strain curve.</p>
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<p>Borehole numerical model and distribution of measurement circles: (<b>a</b>) Borehole numerical model; (<b>b</b>) Measurement circle distribution; (<b>c</b>) Number of measurement circle settings per row.</p>
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<p>Schematic of the mechanical state evolution around the borehole: (<b>a</b>) force chains; (<b>b</b>) displacement vector; (<b>c</b>) stress; and (<b>d</b>) strain.</p>
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<p>Distribution of fractures around the borehole: (<b>a</b>) fractures; (<b>b</b>) distribution of fracture hot spots; and (<b>c</b>) porosity.</p>
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<p>Discrete fracture network models for a coal body with different numbers of pre-existing fractures: (<b>a</b>) one; (<b>b</b>) three; and (<b>c</b>) six.</p>
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<p>Mechanical state evolution around a borehole with pre-existing fractures. (<b>a</b>) Force chain, (<b>b</b>) coal displacement, and (<b>c</b>) stress and strain evolution in a coal body with an increasing number of pre-existing fractures. (<b>a-1</b>,<b>b-1</b>), (<b>a-2</b>,<b>b-2</b>), and (<b>a-3</b>,<b>b-3</b>) correspond to the models in <a href="#processes-13-00146-f008" class="html-fig">Figure 8</a>a, b, and c, respectively.</p>
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<p>Fracture distribution around a borehole with pre-existing fractures. (<b>a</b>) Fracture distribution and (<b>b</b>) fracture quantity in a coal body with an increasing number of pre-existing fractures. (<b>a-1</b>,<b>b-1</b>), (<b>a-2</b>,<b>b-2</b>), and (<b>a-3</b>,<b>b-3</b>) correspond to the models in <a href="#processes-13-00146-f008" class="html-fig">Figure 8</a>a, b, and c, respectively.</p>
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<p>Rose map of tension and shear fracture development around a borehole in a coal body with (<b>a</b>) one, (<b>b</b>) three, and (<b>c</b>) six pre-existing fractures.</p>
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<p>Fractal dimension changes in a coal body with (<b>a</b>) one, (<b>b</b>) three, and (<b>c</b>) six pre-existing fractures. (<b>d</b>) Number of tensile and shear fractures with an increasing number of pre-existing fractures.</p>
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<p>Mechanical state evolution around a borehole with different lateral pressure coefficients. (<b>a</b>) Force chain, (<b>b</b>) coal displacement, and (<b>c</b>) stress and strain evolution in a coal body with different lateral pressure coefficients. (<b>a-1</b>,<b>b-1</b>), (<b>a-2</b>,<b>b-2</b>), and (<b>a-3</b>,<b>b-3</b>) correspond to lateral pressure coefficients of 1, 2, and 4, respectively.</p>
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<p>Fracture distribution around a borehole with different lateral pressure coefficients. (<b>a</b>) Fracture distribution and (<b>b</b>) fracture quantity in a coal body, where (<b>a-1</b>,<b>b-1</b>), (<b>a-2</b>,<b>b-2</b>), and (<b>a-3</b>,<b>b-3</b>) correspond to lateral pressure coefficients of 1, 2, and 4, respectively.</p>
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<p>Rose map of tension and shear fracture development around a borehole in a coal body with lateral pressure coefficients of (<b>a</b>) 1, (<b>b</b>) 2, and (<b>c</b>) 4.</p>
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<p>Fractal dimension changes in a coal body with lateral pressure coefficients of (<b>a</b>) 1, (<b>b</b>) 2, and (<b>c</b>) 4. (<b>d</b>) Number of tensile and shear fractures with increasing lateral pressure coefficient.</p>
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<p>Mechanical state evolution around a borehole with different gas pressures. (<b>a</b>) Force chain, (<b>b</b>) coal displacement, and (<b>c</b>) stress and strain evolution in a coal body, where (<b>a-1</b>,<b>b-1</b>), (<b>a-2</b>,<b>b-2</b>), and (<b>a-3</b>,<b>b-3</b>) correspond to gas pressures of 0.5, 1.0, and 1.5 MPa, respectively.</p>
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<p>Fracture distribution around a borehole with different gas pressures. (<b>a</b>) Fracture distribution and (<b>b</b>) fracture quantity in a coal body, where (<b>a-1</b>,<b>b-1</b>), (<b>a-2</b>,<b>b-2</b>), and (<b>a-3</b>,<b>b-3</b>) correspond to gas pressures of 0.5, 1.0, and 1.5 MPa, respectively.</p>
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<p>Rose map of tension and shear fracture development around a borehole in a coal body with gas pressures of (<b>a</b>) 0.5, (<b>b</b>) 1.0, and (<b>c</b>) 1.5 MPa.</p>
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<p>Fractal dimension changes in a coal body with gas pressures of (<b>a</b>) 0.5, (<b>b</b>) 1.0, and (<b>c</b>) 1.5 MPa. (<b>d</b>) Number of tensile and shear fractures with increasing gas pressure.</p>
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<p>Energy evolution characteristics during drilling in a gas-bearing soft coal seam according to (<b>a</b>) different numbers of pre-existing fractures; (<b>b</b>) different lateral pressure coefficients; and (<b>c</b>) different gas pressures.</p>
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<p>Plot of the average fracture number for a borehole in a gas-bearing soft coal seam under different geological conditions.</p>
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<p>Fracture-dominated failure in a gas-bearing soft coal seam.</p>
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<p>Stress-dominated failure in a gas-bearing soft coal seam.</p>
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16 pages, 8305 KiB  
Article
Investigating Fracture Behavior in Titanium Aluminides: Surface Roughness as an Indicator of Fracture Mechanisms in Ti-48Al-2Cr-2Nb Alloys
by Alessia Serena Perna, Lorenzo Savio, Michele Coppola and Fabio Scherillo
Metals 2025, 15(1), 49; https://doi.org/10.3390/met15010049 - 7 Jan 2025
Viewed by 220
Abstract
Titanium aluminides, particularly the Ti-48Al-2Cr-2Nb alloy, have drawn significant attention for their potential in high-temperature aerospace and automotive applications due to their exceptional performances and reduced density compared to nickel-based superalloys. However, their intermetallic nature poses challenges such as limited room-temperature ductility and [...] Read more.
Titanium aluminides, particularly the Ti-48Al-2Cr-2Nb alloy, have drawn significant attention for their potential in high-temperature aerospace and automotive applications due to their exceptional performances and reduced density compared to nickel-based superalloys. However, their intermetallic nature poses challenges such as limited room-temperature ductility and fracture toughness, limiting their widespread application. Additive manufacturing, specifically Electron Beam Melting (EBM), has emerged as a promising method for producing complex-shaped components of titanium aluminides, overcoming challenges associated with conventional production methods. This work investigates the fracture behavior of Ti-48Al-2Cr-2Nb specimens with different microstructures, including duplex and equiaxed, under tensile and high-cycle fatigue at elevated temperatures. Fracture surfaces were analyzed to distinguish between static and dynamic fracture modes. A novel method, employing confocal microscopy acquisitions, is proposed to correlate surface roughness parameters with the causes of failure, offering new insights into the fracture mechanisms of titanium aluminides. The results reveal significant differences in roughness values between the propagation and fracture zones for all the temperatures and microstructure tested. At 650 °C, the crack propagation zone exhibits lower Sq values than the fracture zone, with the fracture zone showing more pronounced roughness, particularly for the equiaxed microstructure. However, at 760 °C, the difference in Sq values between the propagation and fracture zones becomes more pronounced, with a more substantial increase in Sq values in the fracture zone. These findings contribute to understanding fracture behavior in titanium aluminides and provide a predictive framework for assessing structural integrity based on surface characteristics. Full article
(This article belongs to the Special Issue Research on Fatigue Behavior of Additively Manufactured Materials)
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<p>SEM images of the microstructure of specimens obtained via EBM technology and subjected to HIP treatment characterized by of (<b>a</b>) equiaxed microstructure (<b>b</b>) duplex microstructure.</p>
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<p>Stereomicroscope images of the specimens subjected to tensile tests characterized by: (<b>a</b>) duplex microstructure tested at 650 °C; (<b>b</b>) duplex microstructure tested at 760 °C; (<b>c</b>) equiaxial microstructure tested at 650 °C; (<b>d</b>) equiaxed microstructure tested at 760 °C.</p>
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<p>Comparison of fracture surfaces at 500× magnification, following tensile testing for specimens characterized by: (<b>a</b>) duplex microstructure tested at 650 °C; (<b>b</b>) duplex microstructure tested at 760 °C; (<b>c</b>) equiaxed microstructure tested at 650 °C; (<b>d</b>) equiaxed microstructure tested at 760 °C.</p>
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<p>Comparison of fracture surfaces at 1500× magnification, following tensile testing for specimens characterized by: (<b>a</b>) duplex microstructure tested at 650 °C; (<b>b</b>) duplex microstructure tested at 760 °C; (<b>c</b>) equiaxed microstructure tested at 650 °C; (<b>d</b>) equiaxed microstructure tested at 760 °C.</p>
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<p>Confocal acquisition of fracture surfaces of specimens after tensile testing: (<b>a</b>) Waviness surface of a duplex specimen at 650 °C; (<b>b</b>) Roughness surface of a duplex specimen at 650 °C; (<b>c</b>) Waviness surface of a duplex specimen at 760 °C; (<b>d</b>) Roughness surface of a duplex specimen at 760 °C.</p>
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<p>Stereomicroscope images of the specimens subjected to HCF tests characterized by: (<b>a</b>) duplex microstructure tested at 650 °C; (<b>b</b>) duplex microstructure tested at 760 °C; (<b>c</b>) equiaxed microstructure tested at 650 °C; (<b>d</b>) equiaxed microstructure tested at 760 °C.</p>
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<p>Comparison of fracture surfaces at 500× magnification, following HCF tests for specimens characterized by: (<b>a</b>) duplex microstructure tested at 650 °C; (<b>b</b>) duplex microstructure tested at 760 °C; (<b>c</b>) equiaxed microstructure tested at 650 °C; (<b>d</b>) equiaxed microstructure tested at 760 °C.</p>
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<p>Comparison of fracture surfaces at 1500× magnification, following HCF tests for specimens characterized by: (<b>a</b>) duplex microstructure tested at 650 °C; (<b>b</b>) duplex microstructure tested at 760 °C; (<b>c</b>) equiaxed microstructure tested at 650 °C; (<b>d</b>) equiaxed microstructure tested at 760 °C.</p>
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<p>Confocal acquisition of fracture surfaces of specimens after HCF testing: (<b>a</b>) Waviness surface of a duplex specimen at 650 °C; (<b>b</b>) Roughness surface of a duplex specimen at 650 °C; (<b>c</b>) Waviness surface of a duplex specimen at 760 °C; (<b>d</b>) Roughness surface of a duplex specimen at 760 °C.</p>
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<p>Comparison of surface roughness values (Sq) for the propagation and fracture zones of specimens tested for HCF at 650 °C and 760 °C: (<b>a</b>) specimens with a duplex microstructure; (<b>b</b>) specimens with an equiaxed microstructure.</p>
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29 pages, 7996 KiB  
Review
Signatures of Plastic Instabilities and Strain Localization in Acoustic Emission Time-Series
by Alexey Vinogradov
Metals 2025, 15(1), 46; https://doi.org/10.3390/met15010046 - 6 Jan 2025
Viewed by 247
Abstract
Acoustic emission (AE) is a powerful tool for investigating the intermittency of plastic flow by capturing elastic waves generated by dislocation rearrangements under load. This study explores the correlation between AE and plastic instabilities, such as Lüders bands, the Portevin–Le Chatelier (PLC) effect, [...] Read more.
Acoustic emission (AE) is a powerful tool for investigating the intermittency of plastic flow by capturing elastic waves generated by dislocation rearrangements under load. This study explores the correlation between AE and plastic instabilities, such as Lüders bands, the Portevin–Le Chatelier (PLC) effect, and necking, each showing distinct AE signatures. Lüders and PLC bands generate significant AE during discontinuous yielding, with a sharp rise in AE levels and a shift in the spectrum to lower frequencies—characteristic of localized deformation. In contrast, necking exhibits limited AE activity, due to reduced strain hardening and dislocation mobility during late-stage deformation. A phenomenological model, based on dislocation dynamics and initially devised for uniform deformation, is discussed to explain the observed AE spectral features during localized plastic flow. This study underscores AE’s potential for non-destructive evaluation and failure prediction in structural metals, emphasizing its sensitivity to microstructural changes and instabilities. Understanding AE behavior across deformation stages offers valuable insights into improving material reliability and predicting failure. Full article
(This article belongs to the Special Issue Self-Organization in Plasticity of Metals and Alloys)
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<p>Schematic illustration of deformation curves and most common types of macroscopic plastic instabilities: necking (<b>a</b>), upper and lower yield points and plateau corresponding to the Lüders band propagation (<b>b</b>), and (<b>c</b>) serrated plastic flow associated with multiple Portevin–Le Chatelier bands.</p>
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<p>Typical continuously recorded AE waveforms (sampled at 2 Msamples/s): (<b>a</b>) background electrical noise, (<b>b</b>) continuous AE signal, (<b>c</b>) a flux of low-amplitude signals on a background of noise, (<b>d</b>) high-amplitude transient burst.</p>
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<p>The footprint of the scratch on the surface created by a Berkovich diamond indenter moving linearly edge-forward at 0.2 mm/s along the well-annealed copper polycrystal (EBSD image in inverse pole figure colours is superimposed to unveil the grain structure) (<b>a</b>), corresponding continuous AE signal (<b>b</b>) (see [<a href="#B15-metals-15-00046" class="html-bibr">15</a>] for details), transient AE time series revealed after denoising (<b>c</b>), and slip lines appearance ahead of the tip of the indenter (<b>d</b>); (adapted from ref. [<a href="#B15-metals-15-00046" class="html-bibr">15</a>], reproduced in a modified form with permission. Copyright 2108, Elsevier).</p>
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<p>The short fragment of the AE point process obtained by ϕ-phase-picking algorithm described in [<a href="#B41-metals-15-00046" class="html-bibr">41</a>] from the streaming data shown in <a href="#metals-15-00046-f003" class="html-fig">Figure 3</a>c for the scratch test of copper polycrystal.</p>
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<p>Typical AE waiting time distribution (squares) and the expected Poisson distribution (solid line) obtained for 1 s fragment of the AE time series obtained during scratching of pure copper and shown in <a href="#metals-15-00046-f003" class="html-fig">Figure 3</a>.</p>
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<p>The interevent time distribution (squares) obtained for dislocation avalanches observed in DDD simulations carried out by Devincre et al. [<a href="#B53-metals-15-00046" class="html-bibr">53</a>]; the experimental data are taken from Ref. [<a href="#B53-metals-15-00046" class="html-bibr">53</a>] and the expected Poisson exponential function (solid line) is plotted for comparison.</p>
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<p>Illustration of the phase-picking algorithm described in [<a href="#B39-metals-15-00046" class="html-bibr">39</a>] and tested against low amplitude AE signals buried in the background electrical noise: (<b>a</b>) original noise-like synthetic waveform with pre-seeded low-amplitude AE transients, (<b>b</b>) the same signal processed by the phase-picking algorithm, and (<b>c</b>) the corresponding probability of detection of transients.</p>
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<p>A fragment of AE at the onset of deformation of Cu30Zn alloy; see [<a href="#B17-metals-15-00046" class="html-bibr">17</a>] for experimental details. Arrows point to the strain at which the DIC images were taken; corresponding strain is shown above each image.</p>
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<p>A-type serrations on the stress–strain curve and corresponding AE response in Cu30Zn alloy tested in tension at the nominal strain rate <math display="inline"><semantics> <mover accent="true"> <mi>ε</mi> <mo>˙</mo> </mover> </semantics></math> = 3 × 10<sup>−2</sup> s<sup>−1</sup>; see [<a href="#B17-metals-15-00046" class="html-bibr">17</a>] for details; dashed line represents the strain hardening rate <math display="inline"><semantics> <mi>θ</mi> </semantics></math>. DIC images represent the local strain rate distribution in the PLC band initiated at the lower shoulder; it was then expanding and propagating towards the upper edge, where it was “reflected” and moved backward. The leftmost DIC image represents uniform deformation stage. Arrows point to the strain at which the DIC images were taken; corresponding strain is shown above each image.</p>
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<p>Fragments of stress–strain curves and corresponding AE behavior in Cu Zn solid solution alloys around the necking point: (<b>a</b>) Cu5Zn, (<b>b</b>) Cu10Zn and (<b>c</b>) Cu15Zn. The Considère point is indicated by the grey circle. AE was detected using a wideband Micro-F30 sensor from MISTRAS Ltd. (Princeton, NJ, USA). See [<a href="#B17-metals-15-00046" class="html-bibr">17</a>] for experimental details and data processing.</p>
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<p>Successive optical images (200 ms intervals between the snapshots) illustrating the final stage of the propagation of the neck and the development of ductile microcracks in pure copper polycrystal deformed in uniaxial tension.</p>
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<p>Fragment of the AE behavior recorded continuously by two sensors, Micro-F30 (same as that in <a href="#metals-15-00046-f010" class="html-fig">Figure 10</a>) and Nano-30, attached to the same tensile tested specimen of the Cu20Zn specimen. The grey circle indicates the Considère point on the stress–strain curve. Arrows point to the strain at which the DIC images were taken; corresponding strain is shown above each image.</p>
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18 pages, 16485 KiB  
Article
Study on Deformation Mechanism and Surrounding Rock Strata Control in End-Mining Retracement Roadway in Closely Spaced Coal Seams
by Bin Wang, Hui Liu, Dong Liu, Jie Zhang and Haifei Lin
Appl. Sci. 2025, 15(1), 436; https://doi.org/10.3390/app15010436 - 5 Jan 2025
Viewed by 498
Abstract
This paper aims to address the issue of hydraulic support crushing accidents or support failures in the retracement roadway (RR) that frequently occurs when a fully mechanized mining face is retraced during the end-mining stage. The deformation and instability mechanism of surrounding rock [...] Read more.
This paper aims to address the issue of hydraulic support crushing accidents or support failures in the retracement roadway (RR) that frequently occurs when a fully mechanized mining face is retraced during the end-mining stage. The deformation and instability mechanism of surrounding rock in the RR during the end mining of a fully mechanized mining face at the Hanjiawan Coal Mine located in the northern Shaanxi mining area is explored through field measurement, theoretical analysis, similar simulation, and numerical simulation. The results reveal that the stability of the remaining coal pillar (RCP) and the fracture position of the main roof are the main factors contributing to large-scale dynamic load pressure in the RR during the end-mining stage. The plastic zone width limit of the RCP is identified to be 5.5 m. Furthermore, the stress distribution within the RCP during the end-mining stage is determined, and the linear relationship between the load borne by the RCP and the strength of the coal pillar is quantified. A similar simulation experiment is conducted to examine the collapse and instability characteristics of the overlying rock structure during the end-mining stage. UDEC (v.5.0) software is utilized to optimize the roof support parameters of the RR. A surrounding rock control technology that integrates the anchor net cable and hydraulic chock is proposed to ensure RR stability. Meanwhile, a method involving ceasing mining operations and waiting pressure is adopted to ensure a safe and smooth connection between the working face and the RR. This study provides a reference for the surrounding rock control of the RR during end mining in shallow, closely-spaced coal seams under similar conditions. Full article
(This article belongs to the Special Issue Advances in Green Coal Mining Technologies)
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<p>The measuring point arrangement of the RR at the end of LW 3301.</p>
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<p>Deformation characteristics of surrounding rock in stope when the distance from the RR is 10 m.</p>
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<p>Characteristics of main roof fracture in the end mining stage of the working face.</p>
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<p>Stress distribution of coal pillar in working face. <span class="html-italic">R</span><sub>1</sub>—RR side plastic zone; <span class="html-italic">R</span><sub>2</sub>—working face side plastic zone; <span class="html-italic">k</span><sub>1</sub>, <span class="html-italic">k</span><sub>2</sub>—stress concentration factor; <span class="html-italic">γ</span>—overburden rock bulk density; <span class="html-italic">h</span>—coal seam buried depth.</p>
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<p>The schematic diagram of caving arch above the goaf of the working face. B—solid coal; <span class="html-italic">a</span>—RR width, 4.5 m; <span class="html-italic">b</span>—RR height; <span class="html-italic">L</span>—width of RCP in working face; <span class="html-italic">D</span>—support control distance, 5.7 m; H—falling arch height; M—mining height, 2.9 m.</p>
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<p>Stress distribution law of residual coal pillar in the working face.</p>
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<p>Physical similarity model design diagram.</p>
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<p>Excavation process simulation with different distances.</p>
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<p>Caving characteristics of overlying strata 40 m away from withdrawal roadway in the working face.</p>
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<p>Scheme 1 shows the stress distribution and displacement change cloud diagram of overlying strata during the end mining of the working face.</p>
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<p>Scheme 2 shows the stress distribution and displacement change cloud diagram of overlying strata during the end mining of the working face.</p>
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<p>Scheme 3 shows the stress distribution and displacement change cloud diagram of overlying strata during the end mining of the working face.</p>
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<p>Sectional diagram of support parameters of RR before end mining breakthrough.</p>
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<p>Surrounding rock deformation curve of ERR.</p>
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23 pages, 9139 KiB  
Article
Experimental and Numerical Simulation Study on the Mechanical Properties of Integrated Sleeve Mortise and Tenon Steel–Wood Composite Joints
by Zhanguang Wang, Weihan Yang, Zhenyu Gao, Jianhua Shao and Dongmei Li
Buildings 2025, 15(1), 137; https://doi.org/10.3390/buildings15010137 - 4 Jan 2025
Viewed by 539
Abstract
In view of the application status and technical challenges of steel–wood composite joints in architecture, this paper proposes an innovative connection technology to solve issues such as susceptibility to pry-out at beam–column joints and low load-bearing capacity and to provide various reinforcement methods [...] Read more.
In view of the application status and technical challenges of steel–wood composite joints in architecture, this paper proposes an innovative connection technology to solve issues such as susceptibility to pry-out at beam–column joints and low load-bearing capacity and to provide various reinforcement methods in order to meet the different structural requirements and economic benefits. By designing and manufacturing four groups of beam–column joint specimens with different reinforcement methods, including no reinforcement, structural adhesive and angle steel reinforcement, 4 mm thick steel sleeve reinforcement, and 6 mm thick steel sleeve reinforcement, monotonic loading tests and finite element simulations were carried out, respectively. This research found that unreinforced specimens and structural adhesive angle steel-reinforced joints exhibited obvious mortise and tenon compression deformation and, moreover, tenon pulling phenomena at load values of approximately 2 kN and 2.6 kN, respectively. However, the joint reinforced by a steel sleeve showed a significant improvement in the tenon pulling phenomenon and demonstrated excellent initial stiffness characteristics. The failure mode of the steel sleeve-reinforced joints is primarily characterized by the propagation of cracks at the edges of the steel plate and the tearing of the wood, but the overall structure remains intact. The initial rotational stiffness of the joints reinforced with angle steel and self-tapping screws, the joints reinforced with 4 mm thick steel sleeves, and the joints reinforced with 6 mm thick steel sleeves are 3.96, 6.99, and 13.62 times that of the pure wooden joints, while the ultimate bending moments are 1.97, 7.11, and 7.39 times, respectively. Using finite element software to simulate four groups of joints to observe their stress changes, the areas with high stress in the joints without sleeve reinforcement are mainly located at the upper and lower ends of the tenon, where the compressive stress at the upper edge of the tenon and the tensile stress at the lower flange are both distributed along the grain direction of the beam. The stress on the column sleeve of the joints reinforced with steel sleeves and bolts is relatively low, while the areas with high strain in the beam sleeve are mainly concentrated on the side with the welded stiffeners and its surroundings; the strain around the bolt holes is also quite noticeable. Full article
(This article belongs to the Section Building Structures)
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<p>Nodes J-1 to J-4. (<b>a</b>) J-1, (<b>b</b>) J-2, and (<b>c</b>) J-3 and J-4.</p>
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<p>Design drawings of node J-3 and node J-4. (<b>a</b>) Left view of the steel sleeve of the column, (<b>b</b>) front view of the steel sleeve of the column, (<b>c</b>) left view of the steel sleeve of the beam, and (<b>d</b>) front view of the steel sleeve of the beam. (<b>e</b>) Completed node assembly diagram.</p>
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<p>Three-dimensional view of the loading device.</p>
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<p>Loading regime.</p>
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<p>Layout drawing of measuring points. (<b>a</b>) J-1, (<b>b</b>) J-2, and (<b>c</b>) J-3 and J-4.</p>
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<p>Distribution diagram of displacement meters. (<b>a</b>) D1, D2, and D3; (<b>b</b>) D4 and D5.</p>
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<p>Deformation diagram of node J-1. (<b>a</b>) Overall view before the test, (<b>b</b>) tenon pulling diagram of the lower flange of the beam, and (<b>c</b>) deformation diagram of the specimen after failure.</p>
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<p>Deformation diagram of node J-2. (<b>a</b>) Deformation diagram with an increased degree of tenon pulling and (<b>b</b>) deformation diagram with a tenon pulling amount of 2.5 cm.</p>
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<p>Deformation diagram of node J-3. (<b>a</b>) Deformation diagram of the wooden beam under extrusion and tilting, (<b>b</b>) deformation diagram of the edge fracture of the upper flange, and (<b>c</b>) deformation diagram of the extrusion of the lower flange.</p>
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<p>Deformation diagram of the J-4 joint. (<b>a</b>) Squeezing deformation diagram of the upper flange, (<b>b</b>) fracture diagram of the upper flange of the wooden beam and the steel plate, and (<b>c</b>) deformation diagram of the wooden beam.</p>
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<p>Comparison of the node moment—rotation curves.</p>
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<p>Constitutive model of wood in terms of compression and tension along the grain.</p>
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<p>Constitutive model of wood in terms of compression across the grain.</p>
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<p>Constitutive model of steel.</p>
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<p>Schematic diagram of mesh generation.</p>
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<p>Distributed coupling constraints at the top and end of the column.</p>
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<p>Comparison between finite element analysis results and experimental results. (<b>a</b>) J-1, (<b>b</b>) J-2, (<b>c</b>) J-3, and (<b>d</b>) J-4.</p>
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<p>Stress nephogram of J-1 joint (unit: MPa). (<b>a</b>) Overall failure stress nephogram of the joint, (<b>b</b>) failure stress nephogram of the beam end, and (<b>c</b>) failure stress nephogram of the mortise hole at the column end.</p>
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<p>Stress nephogram of the J-2 joint (unit: MPa). (<b>a</b>) Failure stress nephogram of the column end; (<b>b</b>) failure nephogram of the beam end.</p>
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<p>Stress nephogram of the J-3 joint (unit: MPa). (<b>a</b>) Stress nephogram of overall joint failure, (<b>b</b>) stress nephogram of column end failure, (<b>c</b>) stress nephogram of beam end failure, (<b>d</b>) failure stress nephogram of the sleeve, and (<b>e</b>) failure stress nephogram of the bolt.</p>
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<p>Stress nephogram of the J-4 joint (unit: MPa). (<b>a</b>) Overall failure stress nephogram of the joint, (<b>b</b>) failure stress nephogram of the beam end, and (<b>c</b>) failure stress nephogram of the sleeve.</p>
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17 pages, 6637 KiB  
Article
Influence of Cell Geometry on the Mechanical and Failure Characteristics of 3D Voronoi Hydroxyapatite Through the Stereolithography Technique
by Ali Arab, Zhwan Dilshad Ibrahim Sktani, Zainab Alknery and Chunwei Zhang
Ceramics 2025, 8(1), 4; https://doi.org/10.3390/ceramics8010004 - 4 Jan 2025
Viewed by 336
Abstract
This study investigates the design and mechanical evaluation of hydroxyapatite (HAp) scaffolds for bone tissue engineering, using stereolithography (SLA) to fabricate homogeneous and hollow elongated Voronoi structures. HAp, known for its biocompatibility and biodegradability, was selected to create scaffolds with a structure that [...] Read more.
This study investigates the design and mechanical evaluation of hydroxyapatite (HAp) scaffolds for bone tissue engineering, using stereolithography (SLA) to fabricate homogeneous and hollow elongated Voronoi structures. HAp, known for its biocompatibility and biodegradability, was selected to create scaffolds with a structure that supports cell growth. Both scaffold designs were tested under compression to measure key properties, including compressive strength, Young’s modulus, stiffness, and energy absorption. The homogeneous design demonstrated superior mechanical properties, achieving a maximum load of 913.6 N at a displacement of 0.166 mm and a stiffness of 5162.8 N/mm, indicating a higher load-bearing capacity and energy absorption compared to the hollow design. Despite these strengths, failure analysis revealed early fractures at strut junctions, particularly in slender areas, leading to fluctuations in the load–displacement curve and suggesting a risk to neighboring tissues in practical applications. These findings underscore the potential of Voronoi-based scaffolds for orthopedic use, while also highlighting the need for structural refinements to improve scaffold durability and clinical effectiveness. Full article
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<p>(<b>a</b>,<b>b</b>) Three-dimensional printed samples after sintering. (<b>c</b>,<b>d</b>) SEM images of samples at different magnifications. (<b>e</b>) Compressive test setup with a microscopic lens.</p>
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<p>High-magnification SEM image of the HAp scaffold after sintering, showing well-bonded layers and smooth interparticle connections.</p>
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<p>(<b>a</b>) Load–displacement curve (numbers 1 through 6 represent the same corresponding failure stages in <a href="#ceramics-08-00004-f004" class="html-fig">Figure 4</a>); (<b>b</b>) stress–strain curve; (<b>c</b>) energy–displacement curve; (<b>d</b>) the specific energy of the two printed samples.</p>
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<p>(<b>a</b>) Failure stages of the homogeneous sample. (<b>b</b>) Failure stages of the hollow sample. The numbers 1–6 refer to points 1–6 on the stress–strain curve in <a href="#ceramics-08-00004-f003" class="html-fig">Figure 3</a>.</p>
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<p>(<b>a</b>–<b>c</b>) Stages of load distribution through DIC analysis of the von Mises’ strain of the homogeneous sample.</p>
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<p>High magnification image of structures during loading: (<b>a</b>) homogeneous sample; (<b>b</b>) hollow sample.</p>
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<p>(<b>a</b>,<b>b</b>): SEM analysis of crack propagation in hollow sample at strain of 0.005.</p>
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<p>Grasshopper script for the homogeneous elongated Voronoi structure, showing the use of the Scale NU plugin to elongate Voronoi cells uniformly along the Z-direction with controlled thickness.</p>
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<p>Grasshopper script for the hollow elongated Voronoi structure, demonstrating the hollowing of cell struts using the Pipe plugin while maintaining Z-direction elongation and consistent volume.</p>
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25 pages, 7882 KiB  
Article
The Anchorage Performance and Mechanism of Prefabricated Concrete Shear Walls with Closed-Loop Rebar
by Yufen Gao, Zheng Yang, Lu Chen, Shengzhao Cheng and Zhongshan Zhang
Buildings 2025, 15(1), 131; https://doi.org/10.3390/buildings15010131 - 4 Jan 2025
Viewed by 354
Abstract
To thoroughly investigate the anchorage performance of a novel prefabricated concrete shear wall system assembled by anchoring closed-loop rebar, rebar pull-out tests were conducted. The effects of different rebar distribution forms, closed-loop rebar anchoring heights, and dowel rebar diameters on anchorage performance were [...] Read more.
To thoroughly investigate the anchorage performance of a novel prefabricated concrete shear wall system assembled by anchoring closed-loop rebar, rebar pull-out tests were conducted. The effects of different rebar distribution forms, closed-loop rebar anchoring heights, and dowel rebar diameters on anchorage performance were considered. Strain measurements at key points were taken, and the failure modes and peak loads of shear walls with various closed-loop rebar assemblies were obtained. The results indicated that the rebars in all specimens fractured, with peak loads ranging from 90 kN to 100 kN, satisfying the anchorage requirements of the rebar. This demonstrates that even when the anchorage length of the rebar is less than specified, the method of assembling by anchoring closed-loop rebar can still provide good anchorage performance. Moreover, steel bars and concrete have different damage and failure characteristics under different load levels. This research also indicates that specimens with uniformly distributed closed-loop rebar exhibit superior anchorage performance compared to those with adjacent distribution. Furthermore, increasing the overlapping height of the closed-loop rebar contributed to enhancing the safety margin of the anchorage, while the diameter of the dowel rebar (similar to stirrups) had a relatively minor effect on the anchorage performance. These findings provide a scientific basis for the design and construction of prefabricated concrete shear walls with closed-loop rebar. Full article
(This article belongs to the Special Issue Advances in Structural Techniques for Prefabricated Modular Buildings)
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<p>Schematic diagram of the coupled hoop connection method.</p>
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<p>Schematic diagram of the specimen.</p>
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<p>Diagrams of different rebar binding configurations.</p>
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<p>Schematic diagram of the test loading system.</p>
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<p>Arrangement of strain gauges.</p>
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<p>Finite element meshes, constraints, and loads.</p>
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<p>Concrete damage area and rebar fracture morphology of different specimens.</p>
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<p>Total load and point strain curve for Specimen I-1.</p>
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<p>Total load and point strain curve for monitoring points 3 and 4 of Specimens II-1 and III-1.</p>
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<p>Total load and point strain curve for points 3 and 4 of different specimens.</p>
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<p>Experimental value and simulated value of key points.</p>
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<p>Rebar stress under different thresholds.</p>
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<p>Stress and damage of concrete.</p>
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<p>Stress of rebar at different depths under different conditions.</p>
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<p>Stress of rebar at different depths under different conditions.</p>
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<p>Simulation of the pull-out of a single U-shaped rebar shear wall.</p>
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