Search Results (1,092)

Initial excavation-induced damage may alter water-driven weakening and failure in bedded shale, yet direct experimental evidence from comparable loading–hydration routes remains limited. In this study, uniaxial compression tests with acoustic emission (AE) monitoring were conducted on bedded shale from the Longmaxi Formation in the Sichuan Basin, China, under two routes, i.e., direct saturation (DS) and pre-damage followed by saturation (PDRS), across seven bedding orientations from 0° to 90°. Pre-damage was introduced by loading–unloading to 0.6 of the orientation-dependent peak strength, producing measurable defects and reducing P-wave velocity by an average of 1.23% while preserving the overall anisotropic pattern of wave propagation. Compared with DS, PDRS caused clear mechanical deterioration, with mean reductions of 37.63% in peak strength and 31.14% in elastic modulus. Both routes retained pronounced bedding-angle dependence, although the locations of minimum strength and stiffness differed between them. AE activity in the PDRS group generally initiated earlier and accumulated more persistently before peak stress. RA–AF analysis showed that tensile-like cracking dominated across all bedding orientations in PDRS, whereas the DS group exhibited stronger orientation-dependent variation in cracking mode. The b-value range was also narrower in PDRS than in DS, indicating reduced dispersion of event-size statistics among orientations. Macroscopically, failure evolved from more distributed multi-crack and mixed-mode patterns in DS to more localized dominant-fracture failure with reduced branching in PDRS. Overall, the results suggest that pre-damage before saturation changes the subsequent weakening and fracture development of bedded shale during reloading. Full article

Liquid metal embrittlement (LME) during hot stamping of Zn-coated high-strength steels poses significant challenges to the long-term durability of automotive components. This study investigates how ~30 μm deep LME cracks affect the mechanical behavior of Zn-coated high-strength boron steel. LME-free flat specimens were compared with hat-shaped specimens containing LME cracks. While tensile strength and ductility exhibited minimal changes, the high-cycle fatigue limit (R = −1, 10⁷ cycles) decreased by 10.9% from 550 MPa to 490 MPa in hat-shaped specimens. Fractographic examination revealed distinct stress-dependent crack initiation mechanisms: at high stress amplitudes (≥690 MPa), LME cracks competed with intrinsic substrate defects but did not dominate fatigue failure. In contrast, at moderate-to-low stress amplitudes (≤630 MPa), LME cracks dominated fatigue degradation through a multi-site crack initiation tendency. El Haddad analysis positioned these cracks at the short-to-long crack transition boundary (l ≈ l₀). Preliminary fracture mechanics analysis reveals that conventional single-crack LEFM models systematically overestimate the fatigue threshold stress for LME-affected specimens, a discrepancy qualitatively attributed to the high surface density and morphological complexity of LME crack networks and to chemically assisted grain boundary weakening induced by liquid Zn infiltration—effects not captured by standard fracture mechanics frameworks. These results establish the stress-dependent mechanisms governing LME crack-induced fatigue degradation and provide a mechanistic basis for the development of more accurate fatigue life prediction methods for Zn-coated hot-stamped high-strength steels. Full article

Urban rail wheels endure prolonged exposure to frequent starts and stops, heavy cyclic loads, and complex track conditions, which often lead to premature failure modes such as wear, fatigue cracking, and corrosion in conventional wheel materials. These limitations restrict their ability to meet the evolving demands of modern rail systems for enhanced durability and performance. To address this, the present study uses laser cladding to deposit high-entropy alloy coatings with systematically varied aluminium content onto wheel substrates. The study compares phase composition, microstructure, and mechanical properties across the different coatings. Results show that increasing Al content transforms the coating microstructure from a single face-centred cubic (FCC) phase to a dual-phase structure of FCC and body-centred cubic (BCC) phases, accompanied by notable grain refinement. Among the variants, the CrMnFeCoNi(Al)₈ coating has the densest microstructure and the most favourable mechanical performance. It achieves a microhardness of 399.62 HV_0.5 in the as-clad state and 450 ± 5 HV_0.5 after heat treatment, representing an increase of approximately 12.6%. This coating also demonstrates improved corrosion resistance, with an open-circuit potential 0.07 V higher than the CL60 substrate. Multi-body dynamics simulations confirm that the clad wheels maintain excellent operational stability and safety under service conditions. Full article

This study evaluates the multi-physical effects of fiber type, length, and dosage on the fresh properties, mechanical performance, and environmental impact of roller-compacted concrete (RCC). Industrial steel (S), polypropylene (PP), and waste steel (WS) fibers with lengths of 30 mm and 60 mm were incorporated into RCC mixtures at volume fractions ranging from 0% to 1.25%. The experimental program included Vebe consistency tests, mechanical strength assessments, and fracture energy measurements, complemented by a simplified cradle-to-gate Global Warming Potential (GWP) analysis. Furthermore, Taguchi and ANOVA methods were employed to statistically determine the hierarchy of influential parameters. The statistical analysis revealed that fiber dosage was the most dominant factor, contributing approximately 68–78% to the variation in compressive, splitting tensile, and flexural strengths, whereas fiber type governed the consistency. Experimentally, S and WS fibers significantly enhanced the post-cracking behavior and fracture energy compared to the brittle control mix, although they imposed a greater penalty on workability than PP fibers. Notably, at comparable dosages, WS fibers exhibited mechanical interlock and toughness performance nearly identical to industrial steel fibers. The environmental analysis demonstrated that replacing industrial steel fibers with WS fibers reduces the embodied carbon by approximately 240 kgCO₂-eq/m³ at the maximum dosage, without compromising mechanical reliability. These findings suggest that waste steel fibers offer a superior performance-to-carbon ratio, making them a viable sustainable alternative for heavy-duty RCC pavements where crack control is prioritized. Full article

This paper presents a theoretical study on the seismic behavior and working mechanisms of eccentrically braced composite frames with vertical shear links. A theoretical model is established based on structural mechanics principles to analyze the internal force distribution and deformation patterns under lateral loading. Formulas for the lateral stiffness, bending moments in beams and columns, and joint rotations are derived. A multi-stage theoretical skeleton curve model is proposed, identifying key points such as cracking, yielding, peak strength, and failure, along with corresponding methods for calculating load and displacement values. The theoretical results show good agreement with experimental data, effectively predicting the structural stiffness, load-bearing capacity, and deformation behavior. Key design parameters affecting structural performance are identified, including the beam–column linear stiffness ratio, geometric properties of the shear link, and brace stiffness. The study provides a theoretical basis and practical methodology for the seismic design of such structures. Full article

Coastal wind turbines operate under severe salt spray, high humidity, and wind-driven erosion, which accelerate coating degradation and corrosion-induced cracking. In such environments, corrosion defects exhibit blurred boundaries, weak textures, and significant scale variations, challenging object detectors in small-target localization and precise boundary regression. To address these limitations, this study proposes CoastCor-Net, an enhanced YOLOv11-based framework that improves spatial–semantic alignment, boundary representation, and channel–spatial dependency modeling. The architecture integrates three complementary modules to enhance boundary sensitivity, spatial–semantic consistency, and cross-channel interaction: a Decoding-Driven Enhancement Block, a Complementary Feature Alignment Module, and a Channel-Transposed Coordinate Attention module. Extensive experiments on the Wind Turbine Blade Damage Dataset show that CoastCor-Net achieves 84.7% mAP@0.5 and 54.1% mAP@0.5:0.95, surpassing YOLOv13n by 3.2 percentage points in mAP@0.5 and improving AP_damage by 5.2 percentage points. The framework also demonstrates strong robustness under composite coastal perturbations. These findings highlight the practical effectiveness of structured multi-level feature enhancement for reliable and high-precision blade inspection in complex coastal environments. Full article

To address the challenges of excessive local thinning, poor surface quality, and low production efficiency in traditional multi-pass deep-drawn aluminum alloy fairings, this study investigates the effects of process parameters—including liquid chamber pressure, holding force, and differentiated lubrication schemes—on the liquid-filled forming performance and wall thickness distribution of a 460 × 280 × 1.5 mm thin-walled 2A12 aluminum alloy fairing. Employing an integrated liquid-filled forming technique combining a flexible punch with a rigid die, the research combines numerical simulation with experimental validation. The study demonstrates good consistency between experimental results and numerical simulations. The optimal forming process parameters are liquid chamber pressure of 10 MPa, holding force of 1100 kN, and a lubrication scheme (friction coefficients of 0.01 for the flange and forming zones and 0.06 for the transition radius zone). Under these parameters, part wrinkling and cracking are effectively suppressed, achieving optimal wall thickness uniformity in the formed parts, with a maximum thinning rate of only 6.6%. The proposed liquid-assisted forming process and differentiated lubrication scheme provide a new technical pathway for high-precision manufacturing of thin-walled complex curved components made of 2A12 aluminum alloy. Compared to traditional multi-stage drawing processes, both forming efficiency and quality are significantly improved. Full article

The intelligent detection of asphalt pavement cracks has become increasingly important for ensuring service performance of road infrastructure. Traditional manual detection has significant safety hazards and insufficient accuracy. Furthermore, existing deep learning models still face challenges, including missed detection, false alarms, and poor performance in small target detection under complex conditions. This investigation adopts unmanned aerial vehicles (UAVs) to acquire pavement distress information and develops an intelligent detection approach for asphalt pavement crack based on improved YOLOv8s. First, the Spatial Pyramid Pooling Fast (SPPF) module is replaced with the Spatial Pyramid Pooling Fast with Cross Stage Partial Connections (SPPFCSPC) module in the backbone network to enhance the multi-scale feature fusion capability. Secondly, the Convolutional Block Attention Module (CBAM) module is introduced to the neck network to optimize the feature weights in both channel and spatial attention. Meanwhile, the Efficient Intersection over Union (EIoU) loss is adopted to improve accuracy. Finally, the Crack_Dataset is established, and the ablation experiments are conducted to verify the reliability of the detection model. The research indicates that the improved model achieves Precision, Recall, and mAP@0.5 of 83.9%, 79.6%, and 83.9%, respectively, representing increases of 1.5%, 1.3%, and 1.4%, compared with the baseline model. In comparison with mainstream object detection algorithms such as YOLOv5s and YOLOv8s, the proposed method attains an F1-score, mAP@0.5, and mAP@[0.5–0.95] of 0.82, 83.9%, and 46.6%, respectively, demonstrating a performance improvement. Based on the improved detection model, a pavement crack detection system was designed and implemented using PyQt5. This system supports image, video, and real-time camera input and detection. Full article

This paper proposes a novel and efficient mesostructure generation framework integrating stochastic geometry with physically based packing. First, a random radius field (RRF) method is developed, utilizing multi-scale noise superposition and topology optimization to generate 3D aggregates with realistic and controllable morphologies. Second, a packing strategy based on Rigid Body Dynamics (RBD) is developed to simulate the physical casting process including gravity falling and vibration, achieving high-density aggregate skeletons. The framework is validated through the generation of a multi-phase mesostructure and the fracture simulation of recycled aggregate concrete (RAC). The simulation results successfully reproduced the crack propagation patterns and damage evolution paths associated with different aggregate shapes. These findings confirm the capacity and effectiveness of the proposed framework as a robust tool for the mesoscopic modeling of heterogeneous concrete materials. Full article

Reactive powder concrete (RPC) delivers outstanding mechanical performance and durability; however, it is commonly hindered by high cement consumption, elevated embodied carbon emissions, and high material costs. To mitigate these drawbacks, this study develops a low-carbon, cost-effective RPC incorporating high-volume class-F fly ash, a reduced silica fume dosage, conventional river sand, and an optimized steel fiber system. A systematic mix design framework, combining particle packing density with paste rheology optimization, was employed to balance workability, strength, and durability. The optimized mixtures were evaluated for compressive, splitting tensile, and flexural strength, as well as durability-related metrics, including water absorption rate and resistance to chloride penetration. Environmental impact and cost-effectiveness were further quantified via embodied carbon accounting and strength-normalized performance indices. The results show that well-designed high-volume fly ash RPC can achieve compressive strengths above 130 MPa while maintaining excellent impermeability, alongside substantial reductions in both material cost and carbon footprint relative to conventional RPC. In addition, mixed-size steel fibers further enhance mechanical performance through multi-scale crack bridging. Overall, this work provides a practical route to decouple ultra-high performance from high environmental burden, supporting the sustainable deployment of RPC in infrastructure engineering. Full article

Expanded polystyrene (EPS) foam concrete is attractive for lightweight building applications, yet its practical use is often limited by weak EPS–cement interfacial bonding, which promotes interfacial debonding and crack propagation and thereby compromises mechanical performance. Although nano-SiO₂ (NS) has been reported to improve EPS–cement compatibility, the interfacial strengthening mechanism is still not fully clarified across scales, especially the molecular-level interactions that govern the formation of a robust interfacial transition zone (ITZ). Herein, EPS particles were modified with NS and a multi-scale framework (macro tests, micro-characterization, and molecular dynamics (MD) simulations) was employed to establish a mechanistic linkage between interfacial chemistry/structure and macroscopic performance. The results show that an optimal NS dosage of 9% (by cement mass) increases the 28-day compressive strength and flexural strength of EPS concrete by up to 18.3% and 11.2%, respectively, compared with the unmodified system. SEM, XRD, and FTIR collectively indicate a denser interfacial microstructure, increased hydration-product accumulation near the EPS surface, refined interfacial porosity, and the occurrence of condensation-related reactions involving NS. MD simulations further reveal that NS facilitates the formation of molecular bridges between EPS and C–S–H through hydrogen bonding and ionic interactions, which enhances interfacial adhesion and contributes to improved ITZ thermal stability. This study provides a cross-scale mechanistic understanding for designing high-performance EPS foam concrete via targeted interfacial engineering. MD simulations further suggest that NS enhances interfacial bonding by increasing the occurrence of hydrogen-bond networks and ionic associations at the EPS/C–S–H interface, as evidenced by the intensified interaction-related distributions and peaks in the simulation outputs. Full article

Polypropylene fibers provide an innovative solution for enhancing the crack resistance of tunnel lining segments. However, existing macro-models obscure the distinct effects of fibers on the mortar and ITZ, while explicit meso-modeling remains computationally prohibitive. This study develops a multi-scale modeling framework to investigate PFRC segment fracture under bending. The framework integrates a 3D meso-scale module for calibrating fracture-related material properties, a 3D macro-scale module for predicting global displacements, and a 2D meso-scale module for resolving local fracture processes. A full-scale bending test was performed to validate the framework and to examine the effects of fiber content at both scales. Both the full-scale test and numerical simulations show that the segment response exhibits three stages: elastic, damage development, and cracking at the design load. Numerical simulations further reveal that an optimal fiber content of 0.4% reduces the vertical displacement at the load point by 9.8% and the horizontal displacement at the edge point by 2.9% relative to the fiber-free case. Meso-scale simulations show that 0.4% fibers decrease the bottom crack width from 0.0868 to 0.0770 mm (−11.29%) and limit internal crack connectivity. Although fibers may locally promote ITZ cracking due to reduced mortar–aggregate bonding, a strengthened mortar matrix suppresses crack penetration and connected crack networks. A pronounced high-damage peak in the ITZ near the failure threshold confirms the ITZ as the governing weak link; therefore, further improvements may require ITZ-strengthening strategies. Full article

Accurate assessment of cotton emergence rates is essential for precision agriculture management, and unmanned aerial vehicle (UAV) imagery provides a scalable means for field-level monitoring. However, cotton seedling detection from UAV images faces persistent challenges: individual seedlings appear as small targets with diverse morphologies across varying flight altitudes; strong plastic film reflections, weeds, and soil cracks introduce substantial background interference; and “missing seedling” targets, which manifest as negative space features, exhibit high similarity to background noise. Existing CNN–Transformer hybrid detection architectures are limited by fixed convolutional receptive fields that cannot adapt to multi-scale target variations, attention mechanisms that lack explicit directional geometric modeling, and interpolation-based upsampling that attenuates high-frequency edge details of small targets. To address these issues, this paper proposes DDF-DETR (Dynamic-Direction-Frequency Detection Transformer), a multi-scale spatial context detection method based on RT-DETR. The method incorporates three components: a Dynamic Gated Mixer Block (DGMB) for adaptive multi-scale feature extraction with background noise suppression, a Direction-Aware Adaptive Transformer Encoder (DAATE) for directional geometric feature modeling at linear computational complexity, and a Frequency-Aware Sub-pixel Upsampling Network (FASN) for high-frequency detail recovery in the feature pyramid. On the self-constructed Xinjiang cotton field dataset, DDF-DETR achieves 83.72% mAP@0.5 and 63.46% mAP@0.5:0.95, representing improvements of 2.38% and 5.28% over the baseline RT-DETR-R18, while reducing the parameter count by 30.6% and computational cost to 42.8 GFLOPs. Generalization experiments on the VisDrone2019 and TinyPerson datasets further validate the robustness of the proposed method for small target detection across different scenarios. Full article

In response to the challenges of low detection efficiency, high omission rate in small target detection and high model complexity in wood surface defect detection, this study proposes a lightweight detection model based on YOLO, which integrates a dual-path integrated attention network (DFA-Net). The model is built on the enhanced YOLOv5 framework and achieves a balance of accuracy and efficiency through the collaborative optimization of multiple modules. Specifically, this paper designs a dual-path downsampling convolutional module (DP-DCM), combining wavelet transform with dual-path feature fusion to improve multi-scale feature extraction capabilities while reducing the number of parameters. Next, a fusion attention module (FAM) is designed to dynamically focus on defect features in complex backgrounds through channel and spatial attention mechanisms. Furthermore, a focal modulation network (FMNet) is introduced to enhance the robustness of the augmentation model in detecting small defects. Finally, the NWD Loss function is used to mitigate the localization bias of small targets. Experimental results show that the improved model achieves a 92.8% mAP rate on five types of defect datasets (dead knots, live knots, cracks, notches, and marrow). Compared with the baseline model, YOLOv5s, the performance of this model has been improved by 6.5%. The model runs at a detection speed of 105 FPS, and the number of parameters is only 5.8 million, which is better than models such as YOLOv8 and YOLOv9-t. While maintaining a lightweight design, this method achieves high precision and real-time performance on a consumer-grade GPU platform, indicating its practical applicability in automated wood inspection scenarios. The proposed approach provides an efficient solution for intelligent wood sorting, contributing to improved wood utilization and enhanced processing automation. Full article

Accurate crack segmentation in civil infrastructure imagery remains challenging because of the prevalence of thin, low-contrast, and spatially discontinuous defects that often appear amid textured surfaces, shadows, and acquisition noise. Although Transformer-based models improve global context modeling, many existing solutions incur substantial computational and memory overhead, which limits their use in practical, resource-constrained inspection settings. In this work, we introduce an efficient hybrid segmentation architecture that combines a convolutional encoder for high-fidelity local representation with a lightweight Transformer bottleneck for global dependency modeling, followed by a progressive decoder that restores spatial resolution through multi-level skip-feature fusion. To better accommodate severe foreground sparsity and preserve fine crack structures, the framework is trained with a composite Dice–Binary Cross-Entropy objective and employs a tokenization strategy designed to preserve fine spatial details while enabling efficient global context modeling. We validate the proposed approach on four public benchmarks, demonstrating consistent improvements over representative convolutional, Transformer-based, and hybrid baselines, while ablation studies confirm the contribution of each design component. Finally, runtime profiling shows favorable latency and memory characteristics, supporting real-time or near real-time deployment on embedded and edge inspection platforms. Full article