Search Results (1,818)

Highly deformable adhesive materials are increasingly employed in engineering applications where flexibility, energy dissipation and damage tolerance are required. The mechanical characterisation of these materials, however, presents significant challenges due to their pronounced non-linear behaviour, large deformations, and, in many cases, time-dependent effects. This paper provides a critical review of experimental, constitutive and numerical approaches used for the characterisation of highly deformable adhesive materials, considered here as bulk materials independently of a specific joint configuration. The review covers mechanical testing methods under large strains, hyperelastic and visco-hyperelastic constitutive models, and the application of fracture mechanics concepts and numerical techniques as exploratory tools for material analysis and comparison. Particular attention is given to the capabilities and limitations of the different approaches, their domains of applicability and the assumptions involved in their use. By highlighting current practices, open challenges and recent developments, this work aims to support the selection of appropriate characterisation methodologies and modelling strategies for highly deformable adhesive materials. Full article

Remote sensing semantic segmentation is fundamental for fine-grained urban scene understanding, which in turn provides pixel-level semantic insights for urban development and environmental surveillance. However, existing hybrid segmentation architectures fail to incorporate intrinsic geometric and physical priors, inevitably leading to structural fragmentation, boundary ambiguity, and spatial misalignment of heterogeneous features. Therefore, we propose a Superpixel-Tokenized and Frequency-Modulated Hybrid CNN–Transformer network (SFCT-Net) for remote sensing semantic segmentation. The proposed network integrates superpixel tokens and high-frequency constraints to preserve structural integrity and boundary precision. First, our Superpixel-Tokenized Linear Position Attention (STLPA) module replaces rigid window tokens with semantic superpixels to ensure object integrity with linear computational complexity. Second, we construct a Frequency-Modulated Deformable Edge Refinement (FMDER) module that leverages high-frequency spectral priors to modulate deformable sampling, achieving robust boundary recovery. Finally, we develop the Spatial–Semantic Feature Coupling (SSFC) module, which employs a dual-branch strategy to correct spatial drift and align deep semantic features with shallow details. Experiments conducted on our self-built Taiyuan Satellite Remote Sensing Dataset (TSRSD) along with the ISPRS Vaihingen and Potsdam benchmark datasets demonstrate that our proposed SFCT-Net delivers state-of-the-art performance and efficiency by fusing superpixel and frequency priors for robust structural and boundary recovery. Full article

A Hybrid-Frequency Sampling Tactile Sensing System Based on a Flexible Piezoresistive Sensor Array is presented for reliable and real-time tactile perception under dynamic loading conditions. While recent studies have developed multi-channel tactile arrays, most systems remain limited by time-dependent drift in channel responses, inconsistent dynamic behavior, or insufficient temporal resolution under simultaneous loading. In this work, a system-level design integrating a flexible piezoresistive sensor array with a real-time data acquisition module is developed, incorporating a hybrid-frequency sampling strategy to reduce system complexity while preserving reliable dynamic response in key sensing channels. Register-Transfer Level (RTL) simulation verified that the hardware scheduler rigorously executed the deterministic scanning logic, demonstrating a strict one-to-one correspondence with the physical hardware signals. The array consists of 34 piezoresistive sensing nodes embedded in an elastomeric substrate. Under the implemented hybrid-frequency sampling scheme, the system achieves an overall effective acquisition bandwidth of approximately 36.9 kHz, while maintaining a repeatability better than 4.9% and robust mechanical durability under cyclic bending deformation. Dynamic loading validation was performed using a self-developed pressure comparison platform for measuring the normal contact force applied on the tactile surface, serving as ground-truth data to verify that the voltages acquired by the proposed system accurately correspond to the actual applied force. Quantitative analysis shows a strong linear correlation (R² ≈ 0.98) between the e-skin outputs and the reference forces. The recorded responses exhibit clear intensity-dependent trends and good temporal correspondence among sensing nodes, successfully distinguishing tactile stimuli such as gentle tapping, moderate pressing, and firm contact. The system also captures dynamic tactile responses during finger stroking, showing characteristic multi-unit activation patterns under spatiotemporally varying contact conditions. Compared with previously reported tactile systems typically operating below 100 Hz, the proposed design achieves an approximately 10× enhancement in effective sampling capability while significantly reducing system complexity through hybrid-frequency sampling, thereby supporting reliable dynamic tactile sensing in multi-unit arrays. These results demonstrate that the proposed system provides a practical and scalable hardware platform for dynamic tactile sensing in robotics, human–machine interaction, and wearable tactile systems. Full article

A focus-control mechanism is essential for maintaining the optical performance of spaceborne telescopes, the mirror alignment of which is degraded by gravity release, moisture desorption, and thermal distortion in orbit. Achieving submicrometer-level drive accuracy is challenging because bearing deformation and bolted-joint hysteresis introduce nonlinear behavior, which must be addressed in ultraprecision mechanisms. In this study, the 1D Computer-Aided Engineering (1DCAE) approach was applied to the early-phase design of a spaceborne focus-control mechanism for developing practical design equations that accurately represent the stiffness and deformation characteristics of key components. Modification functions derived from finite element analysis (FEA) and the indirect fictitious boundary integral method (IFBIM) were incorporated into the equations for a linear guide, rectangular spring, and bearing deformation. These equations showed excellent agreement with analytical solutions, numerical simulations, and experimental data, achieving accuracies within 3% and 2.5% for the linear guide and rectangular spring, respectively, and close correspondence with the IFBIM-based bearing deformation reference values. Integrating the equations into the 1DCAE model enabled accurate prediction of the nonlinear drive characteristics of the mechanism and improved the overall drive accuracy to one-fortieth that of the initial design. In conclusion, 1DCAE provides an effective and computationally efficient framework for optimizing ultraprecision mechanisms used in space applications. Full article

For geotechnical structures with a strict control requirement of deformation, the high modulus and non-linear attenuation characteristics of the surrounding soil under small-strain conditions cannot be ignored during performance evaluation; the HSS constitutive model offers significant advantages over conventional approaches (e.g., Mohr–Coulomb) to describe the above soil behaviors. In this study, the theoretical framework of the HSS model, i.e., the yield function, hardening laws, and flow rule, is first elucidated. Subsequently, it is numerically implemented into the finite element software FssiCAS. The reliability of the FssiCAS software (Version 3.5) incorporating the HSS model is validated through a triaxial test and a physical test involving the horizontal loading of the monopile. Finally, taking the four-monopile jacket foundation of an offshore wind turbine (OWT) in Lianjiang County, China, as a representative, the HSS model is adopted to describe the mechanical behaviors of a seabed foundation. The horizontal bearing characteristics of the jacket foundation–seabed system under multi-angle horizontal loading are investigated, and the influence of the horizontal loading angle on the horizontal bearing capacity, jacket displacement, and seabed deformation is quantitatively elucidated. The results indicate that (1) the horizontal bearing capacity of the jacket is minimal when horizontal loading is along the diagonal of the four piles, representing the most severe loading case, and therefore, the horizontal bearing capacity of the jacket foundation–seabed system should be evaluated based on this case; and (2) the FE software FssiCAS has good reliability when dealing with pile–soil interaction problems involving complex geometries and complex mechanical behaviors of seabed soils. This study could provide technical support and an analysis platform for the design of jacket foundations for complex marine structures, such as OWTs. Full article

Nickel-titanium (NiTi) alloys are attractive for functional and biomedical applications due to their shape memory effect, superelasticity, and favorable corrosion resistance and biocompatibility. In this work, the influence of strut size on the compressive response of laser-based powder bed fusion (PBF-LB/M) fabricated NiTi ortho-octahedral porous scaffolds was systematically investigated using combined experiments and finite element simulations. Four scaffold designs with identical unit-cell size (2 mm) but different strut sizes (280, 320, 360, and 400 μm) were fabricated, and their forming quality and deformation behaviors were examined. The as-built scaffolds exhibited high geometric fidelity to the CAD models and stable manufacturability across the investigated parameter range. Quasi-static compression tests revealed a typical three-stage response (linear-elastic regime, plateau/collapse regime, and densification), with both elastic modulus and compressive strength increasing markedly with strut size. Specifically, the modulus increased from 1.17 to 4.28 GPa and the compressive strength increased from 155 to 564 MPa as the strut size increased from 280 to 400 μm. A pronounced oscillatory plateau was observed for the 280 μm scaffolds, indicating progressive layer-by-layer collapse, whereas larger struts promoted a shear-band-dominated failure mode characterized by an approximately 45° fracture zone. Explicit quasi-static simulations reproduced the experimentally observed collapse sequence and demonstrated that stress preferentially concentrates at nodal junctions, with load transfer dominated by struts aligned with the loading direction. The agreement between experiments and simulations confirms the predictive capability of the proposed modeling framework and provides mechanistic insights into geometry-controlled failure. These findings establish a structure-property-failure relationship for PBF-LB/M-fabricated NiTi octahedral scaffolds and offer practical guidance for tailoring stiffness, strength, and collapse mode through strut-size design. Full article

The paw pad is an outstanding cushioning structure, which demonstrates nonlinear mechanical characteristics when subjected to pressure. Nonlinear mechanical characteristics are generally considered to be related to the viscoelastic properties of the material. However, the relationship between its nonlinear mechanical properties and the morphological characteristics of the paw pad remains unknown. In this study, morphological data, mechanical data, and finite element simulation methods were integrated to explore how the unique shape of the paw pads enables them to exhibit excellent cushioning performance. The research findings indicate that the paw pad exhibits an irregular morphology. Nevertheless, its cross-sectional area increases in proportion to the increase in the paw pad height, presenting a linear gradient relationship (R² = 0.99). Two comparison models with the same volume and height but different morphologies as the paw pad model, were designed for finite element simulation. The finite element static analysis shows that the influence of morphology is mainly reflected in the early deformation process, while the influence of viscoelastic material properties is reflected in the later load-bearing capacity. The finite element dynamic analysis shows that compared with the comparison models, the paw pad model has a more stable force during the impact process, without an instantaneous impact force at the initial contact moment. Moreover, the peak normal ground reaction force (GRF) component under different impact speeds is lower than that of the comparison models, demonstrating better buffering effects. The research results can provide inspiration and a biomechanical basis for the morphological design of buffering units. Full article

Granite in the deep crust undergoes long-term high-temperature and high-stress (HTHS) environments, and its mechanical properties are significantly different from those under conventional conditions. Granite is prone to thermal damage under the influence of thermo-mechanical (TM) coupling, resulting in complex nonlinear mechanical responses, which directly affect the stability of deep geotechnical engineering. However, due to the limitations of experimental conditions, TM coupling experiments under HTHS conditions are difficult to carry out effectively, resulting in the dominance of single-factor (temperature or stress alone) research and a lack of systematic analysis under TM coupling. This study employed a combined experimental and numerical approach to obtain the physical properties of granite under conventional conditions and the TM coupling response under HTHS conditions. The results indicate that elevated temperatures induce the formation of thermal cracks in granite, leading to thermal damage. This damage causes a transition in specimen deformation from linear elastic behavior under conventional conditions to plastic deformation. Meanwhile, increasing confining pressure effectively suppresses thermal damage by reducing the generation of thermal cracks. A model to predict how thermal damage affects the strength of granite was created, showing how the mechanical properties of granite change when exposed to thermal and mechanical conditions together. This study addresses the limitations of traditional single-factor experiments, which fail to accurately represent complex geological conditions, and provides a theoretical basis for deep engineering projects. Full article

The Hardening Soil model with small-strain stiffness (HSS) is widely adopted in the numerical analysis of deep excavations and tunneling due to its ability to capture non-linear deformation and stress-history dependency. However, the determination of its key stiffness parameters remains regionally uneven, limiting its application in distinct geological contexts. To address this gap, this study systematically analyzes the physical significance and experimental determination methods of key HSS parameters. Based on comprehensive laboratory testing, including standard consolidation, consolidated undrained triaxial, empirical correlations and quantitative normalized stiffness ratios among the three reference stiffness parameters ($E_{oed}^{ref}$, $E_{50}^{ref}$, $E_{ur}^{ref}$) and the small-strain shear modulus ($G_0^{ref}$) were established for typical soil layers in Taiyuan. Additionally, recommended values for the stress dependency exponent m were determined. The derived parameter ratios were implemented in a finite element analysis of a representative deep excavation project, where the predicted wall deflections and ground settlements showed good agreement with field monitoring data. The results demonstrate that the calibrated regional parameter system provides reliable deformation prediction and improves the transparency and consistency of HSS parameter selection. These findings not only provide a reference for HSS parameter selection in the Taiyuan region but also offer a highly applicable framework for establishing similar parameter systems in other geological contexts. Full article

The prevalence of ground fissures in deformation-affected areas has intensified, presenting serious risks to both operational safety and the local natural environment. Fissures in these disturbed terrains are typically characterized by elongated morphologies and large-scale variations, which pose substantial challenges to accurate feature extraction. To address these complexities, this paper proposes a semantic segmentation network termed MGF-UNet. In the shallow layers, we integrate multi-scale feature sensing (MFS) and grouped efficient multi-scale attention (EMA) to sharpen anisotropic textures and boundary details under high-resolution representations. For the deeper layers, a Token-Selective Context Transformer (TSCT) is designed to perform selective global modeling on high-level semantic features, effectively capturing long-range dependencies while preserving the structural integrity of elongated fissures. Meanwhile, we employ feature-wise linear modulation (FiLM) to derive pixel-wise affine parameters from shallow structures, which pre-modulate deep features and strengthen cross-level interactions. In the decoder, a Fourier transform-based adaptive feature fusion (AFF) module suppresses background noise and enhances boundary contrast, followed by cross-scale aggregation for final prediction.Benchmark tests conducted on the mining-area fissure dataset (MFD) and road-based datasets demonstrate that MGF-UNet achieves an accuracy of 78.2%, a Dice score of 81.4%, and an IoU of 68.6%, outperforming existing mainstream networks. The results confirm that MGF-UNet provides an effective solution for automatic fissure extraction in deformation-prone environments, offering significant potential for geohazard monitoring and ecological restoration. Full article

Concentrated solutions of polycarbosilane (PCS) are critically important for the development of continuous SiC precursor fibers, where solvent–polymer interactions govern rheology, viscoelastic stability, and spinnability. In this work, PCS solutions in two nonpolar hydrocarbon solvents with different molecular architectures as linear n-heptadecane and bicyclic decalin were systematically investigated over a wide concentration range, with emphasis on the semi-dilute entangled and concentrated regimes relevant to solution-based fiber spinning. A combined experimental approach involving steady and oscillatory rheometry and Fourier transform infrared (FTIR) spectroscopy was used to elucidate the influence of solvent structure on solvation, viscoelastic response, microstructural organization, and local intermolecular interactions. Despite similar dilute-solution interaction parameters, the concentrated regimes exhibit pronounced solvent-dependent differences in elasticity and flow behavior. For the first time, linear heptadecane is identified as a viable and technologically promising solvent for PCS, enabling the formation of thermostable homogeneous concentrated solutions with enhanced deformability. This behavior opens a realistic pathway toward a new solution-based fiber-spinning route based on elasticity-controlled processing. The results demonstrate that solvent molecular geometry governs the structure–rheology–processability relationship of concentrated PCS systems rather than solubility parameters alone, providing a new framework for solvent selection in SiC precursor fiber technologies. Full article

Full-arch zirconia prostheses for mandibular All-on-4 rehabilitations are provided as screw-retained monolithic zirconia (Zr-Mono) or as a zirconia suprastructure luted to a CAD/CAM titanium bar (Zr-TiBar). Because zirconia is a brittle and flaw-sensitive ceramic, design assessment should incorporate stress-field-weighted fracture risk. This in silico study compared zirconia tensile stress, deformation, and Weibull-based reliability between Zr-Mono and Zr-TiBar designs in a standardized edentulous mandibular All-on-4 model (posterior implants tilted 30°) using linear static finite element analysis. Accordingly, 300 N posterior unilateral loads were applied at the first molar (axial; 45° oblique). Outcomes were maximum principal tensile stress in zirconia (S1max), total prosthesis deformation, and Weibull-predicted fracture probability (Pf) derived from the tensile S1 field. Under axial loading, S1max was essentially identical between designs (~277 MPa). Under oblique loading, S1max was modestly lower for Zr-TiBar (~227 MPa) than for Zr-Mono (~234 MPa), and deformation was slightly lower for Zr-TiBar (<0.07 mm in all cases). Pf remained very low for both designs (10⁻⁶–10⁻⁷ range) and differed only slightly between them. Under the modeled single 300 N posterior load case, the titanium-bar support reduced deformation and modestly reduced oblique-load peak tensile stress but did not materially reduce the predicted zirconia Pf compared with monolithic zirconia. Full article

Modal superposition enables efficient estimation of full-field structural displacements from sparse measurements, forming a keystone of structural health monitoring (SHM) in linear elastic systems. Accurate reconstruction critically depends on selection of the most relevant vibration modes, traditionally guided by the Internal Strain Potential Energy Criterion (ISPEC), which identifies modes contributing most to internal strain energy. However, the purely analytical formulation of ISPEC requires full knowledge of the deformation field, limiting its applicability in real-time monitoring. This study extends ISPEC using supervised machine learning to enable adaptive mode selection for previously unseen deformation states. A Random Forest classifier is trained on synthetic deformation data generated from a finite element model of a square steel plate. Measurement signals are obtained from a transient analysis in which harmonic displacements are applied to four nodes at the plate plane. Reconstruction performance is evaluated numerically by comparing predicted displacements against reference finite element solutions, using instantaneous residuals, normalised root-mean-square error (NRMSE) and normalised cross-correlation. Results demonstrate that the hybrid ISPEC–machine learning approach accurately reconstructs full-field deflections from eight measurement nodes, with NRMSE typically below 5% and cross-correlation above 0.75. Minor overestimation at peak deflections indicates conservative predictions, while computational efficiency allows real-time implementation. Full article

Aiming to solve the persistent problem of edge cracking in magnesium alloy cast-rolling, this numerical simulation study introduces an innovative magnetic field-assisted approach. Utilizing Lorentz force, the process dynamically transforms the solidification front morphology from an arc-shaped (“Ɔ”) to a linear (“1”) configuration. Simulation results reveal that, while magnetic field-induced thermal effects minimally impact the solidification front, the Lorentz force fundamentally alters the flow field dynamics. This modification yields a more uniform temperature distribution and reduces velocity gradients between the symmetric center and edge regions, thereby promoting the transition to a linear solidification front essential for synchronous solidification and deformation across the entire plate width. Furthermore, variations in magnetic field intensity and frequency critically influence vortex flow position and density within the cast-rolling zone. The optimization goal was to maximize the angle α between the side surface and solidification front, which characterizes the linearity of the front. With optimized parameters of 0.49 T magnetic field intensity and 8 Hz frequency, angle α reaches 65°. This marks a 62.5% increase compared to the conventional (non-magnetic) cast-rolling scenario and achieves a near-linear (“1”) solidification profile. Full article

With the rapid evolution of Generative Adversarial Networks (GANs) and diffusion models (DMs), the detection of synthetic images faces significant challenges due to non-rigid artifacts and complex frequency biases. In this paper, we propose SIDWA, a novel dual-branch detection framework that leverages the synergy between frequency and spatial domains. Within the spatial branch, we design a Deformable Sliding Window Cross-Attention (DSWA) module, which utilizes a learnable offset mechanism to dynamically warp the receptive field, effectively capturing distorted edges and non-linear texture features. Simultaneously, the Discrete Wavelet Transform (DWT) Stem decomposes input images into multi-scale sub-bands to preserve crucial high-frequency residues. Through a Frequency-Semantic Resonance Projector (FSRP) strategy, the semantic priors from the spatial branch act as queries to guide the model toward localized frequency anomalies, achieving a unified “where to look” and “how to analyze” approach. Experimental results for the SIDataset (SIDset) benchmark demonstrate that Synthetic Image Detection based on Discrete Wavelet Transform Stem and Deformable Sliding Window Cross-Attention (SIDWA) achieves superior performance, with an average accuracy exceeding 95% and a competitive inference time of 18.2 ms on an NVIDIA A100 GPU. Ablation studies further validate the critical role of learnable offsets and frequency integration in enhancing robustness and generalization. SIDWA offers an efficient and reliable forensic solution for combating the growing threats of sophisticated generative forgeries. Full article