Search Results (301)

This study investigates the mechanical behaviour of timber–concrete composite (TCC) floor members with an innovative adhesive connection reinforced by granite chips, glass fibre yarn net in the epoxy adhesive layer, and polypropylene (PP) fibres in the concrete layer. Laboratory tests involved three groups of specimens subjected to three-point bending over a span of 500 mm with specimen lengths of 550 mm. Group A specimens exhibited crack initiation at approximately 8 kN and partial disintegration at an average load of 11.17 kN, with maximum vertical displacements ranging from 1.7 to 2.5 mm at 8 kN load, increasing rapidly to 4.3 to 5 mm post-cracking. The addition of reinforcing fibres decreased the brittleness of the adhesive connection and improved load-bearing capacity. Finite element modeling using the newly developed Verisim4D software (2025 v 0.6) and analytical micromechanics approaches demonstrated satisfactory accuracy in predicting the composite behavior. This research highlights the potential of reinforcing the adhesive layer and concrete with fibres to enhance the ductility and durability of TCC members under flexural loading. Full article

Due to the potential to integrate structural load bearing and energy storage within one single composite structural component, the development of carbon fiber (CF)-based structural power composites (SPCs) has garnered significant attention in electric aircraft, electric vehicles, etc. Building upon our previous investigation of the electrochemical performance of SPCs, this work focuses on elastic–plastic behaviors of the bicontinuous structural electrolyte matrices (BSEMs) and carbon fiber composite electrodes (CFCEs) in SPCs. Representative volume element (RVE) models of the BSEMs were numerically generated based on the Cahn–Hilliard equation. Furthermore, RVE models of the CFCEs were established, consisting of the BSEM and randomly distributed CFs. The moduli of BSEMs and the transverse moduli of CFCEs with different functional pore phase volume fractions were predicted and validated against experimental results. Additionally, the local plasticity of BSEMs and CFCEs in the tensile process was analyzed. The work indicates that the presence of the bicontinuous structure prolongs the plasticity evolution process, compared with the traditional polymer matrix, which could be used to explain the brittle-ductile transition observed in the matrix-dominated load-bearing process of CFCEs in the previous literature. This work is a step forward in the comprehensive interpretation of the elastic–plastic behaviors of bicontinuous matrices and multifunctional SPCs for realistic engineering applications. Full article

Predicting the mechanical performance of Engineered Cementitious Composite (ECC)-strengthened reinforced concrete (RC) beams is both meaningful and challenging. Although existing methods each have their advantages, traditional numerical simulations struggle to capture the complex micro-mechanical behavior of ECC, experimental approaches are costly, and data-driven methods heavily depend on large, high-quality datasets. This study proposes a novel physics-informed machine learning framework that integrates domain-specific empirical knowledge and physical laws into a neural network architecture to enhance predictive accuracy and interpretability. The approach leverages outputs from physics-based simulations and experimental insights as weak supervision and incorporates physically consistent loss terms into the training process to guide the model toward scientifically valid solutions, even for unlabeled or sparse data regimes. While the proposed physics-informed model yields slightly lower accuracy than purely data-driven models (mean squared errors of 0.101 VS. 0.091 on the test set), it demonstrates superior physical consistency and significantly better generalization. This trade-off ensures more robust and scientifically reliable predictions, especially under limited data conditions. The results indicate that the empirical-guided framework is a practical and reliable tool for evaluating the structural performance of ECC-strengthened RC beams, supporting their design, retrofitting, and safety assessment. Full article

Conglomerate reservoirs present significant technical challenges during drilling operations due to their complex mineral composition and heterogeneous characteristics, yet the quantitative relationships between mineral composition and microscopic mechanical behavior remain poorly understood. To elucidate the variation patterns of conglomerate micromechanical properties and their mineralogical control mechanisms, this study develops a novel multi-scale characterization methodology. This approach uniquely couples nanoindentation technology, micro-zone X-ray diffraction analysis, and machine learning algorithms to systematically investigate micromechanical properties of conglomerate samples from different regions. Hierarchical clustering algorithms successfully classified conglomerate micro-regions into three lithofacies categories with distinct mechanical differences: hard (elastic modulus: 81.90 GPa, hardness: 7.83 GPa), medium-hard (elastic modulus: 54.97 GPa, hardness: 3.87 GPa), and soft lithofacies (elastic modulus: 25.21 GPa, hardness: 1.15 GPa). Correlation analysis reveals that quartz (SiO₂) content shows significant positive correlation with elastic modulus (r = 0.52) and hardness (r = 0.51), while clay minerals (r = −0.37) and plagioclase content (r = −0.48) exhibit negative correlations with elastic modulus. Mineral phase spatial distribution patterns control the heterogeneous characteristics of conglomerate micromechanical properties. Additionally, a random forest regression model successfully predicts mineral content based on hardness and elastic modulus measurements with high accuracy. These findings bridge the gap between microscopic mineral properties and macroscopic drilling performance, enabling real-time formation strength assessment and providing scientific foundation for optimizing drilling strategies in heterogeneous conglomerate formations. Full article

Coal gangue concrete (CGC), a recycled cementitious material derived from industrial solid waste, presents both opportunities and challenges for structural applications due to its heterogeneous composition and variable mechanical behavior. This study develops an ensemble learning framework—incorporating XGBoost, LightGBM, and CatBoost—to predict four key constitutive parameters based on experimental data. The predicted parameters are subsequently incorporated into an ABAQUS finite element model to simulate the compressive–bending response of CGC columns, with simulation results aligning well with experimental observations in terms of failure mode, load development, and deformation characteristics. To enhance model interpretability, a hybrid approach is adopted, combining permutation-based global feature importance analysis with SHAP (SHapley Additive exPlanations)-derived local explanations. This joint framework captures both the overall influence of each feature and its context-dependent effects, revealing a three-stage stiffness evolution pattern—brittle, quasi-ductile, and re-brittle—governed by gangue replacement levels and consistent with micromechanical mechanisms and numerical responses. Coupled feature interactions, such as between gangue content and crush index, are shown to exacerbate stiffness loss through interfacial weakening and pore development. This integrated approach delivers both predictive accuracy and mechanistic transparency, providing a reference for developing physically interpretable, data-driven constitutive models and offering guidance for tailoring CGC toward ductile, energy-absorbing structural materials in seismic and sustainability-focused engineering. Full article

Rock creep, a key factor in the long-term stability of deep geotechnical engineering, remains challenging to study due to the complexity of its microscopic damage mechanisms. Laboratory creep tests are limited by long durations and scale effects, while phenomenological models cannot fully capture the underlying processes. This study employs the parallel-bonded stress corrosion (PSC) model in PFC2D to simulate sandy mudstone’s creep behavior, systematically correlating macroscopic creep deformation with microscopic damage evolution and energy conversion. The model reproduces the four stages of the idealized creep curve and quantifies the effects of axial stress level and confining pressure on creep lifetime, rate, and failure mode. Increasing axial stress shortens creep lifetime; every 10% increase raises the creep rate by a factor of 4–14, and high stress enhances nonlinear deformation, producing stair-stepping curves due to unstable microcrack propagation. In contrast, confining pressure prolongs lifetime; at 90% uniaxial compressive strength (UCS), 15 MPa extends it from 2.78 h to ~25 years. Confinement also enhances ductility by suppressing tensile stresses and delaying damage accumulation. This study reveals the coupling mechanism of stress-corrosion-induced subcritical crack propagation and energy dissipation, clarifies the microscopic origin of stepped creep curves, and provides a micromechanical framework for long-term stability evaluation in deep geotechnical engineering. Full article

This study investigated the fracture behavior of asphalt mixtures under indirect tensile loading by comparing the performance of homogenized and micromechanical finite element (FEMs) models based on the cohesive zone model (CZM). Five asphalt mixture types were tested experimentally, and both models were calibrated and validated using load–displacement curves from indirect tensile tests (IDTs). The micromechanical model, incorporating random aggregate generation and three-phase material definition, exhibited significantly higher predictive accuracy (R² = 0.86–0.98) than the homogenized model (R² = 0.66–0.77). The validated micromechanical model was further applied to quantify the impact of construction-related deficiencies—namely, increased air voids, non-continuous gradation, and aggregate segregation. The simulation results showed that higher void content (from 4% to 10%) reduced peak load by up to 35% and increased localized stress concentrations by up to 40%. Discontinuous gradation and uneven aggregate distribution also led to premature crack initiation and more complex fracture paths. These findings demonstrated the value of micromechanical modeling for evaluating sensitivity to mix design and compaction quality, providing a foundation for performance-based asphalt mixture optimization and durability improvement. Full article

Lateritic soils in tropical regions feature cohesive textures and high specific resistance, driving up energy demands for tillage and harvesting machinery. However, current equipment designs lack discrete element models that account for soil moisture variations, and the microscopic effects of water content on lateritic soil deformation remain poorly understood. This study aims to calibrate and validate discrete element method (DEM) models of lateritic soil at varying moisture contents of 20.51%, 22.39%, 24.53%, 26.28%, and 28.04% by integrating the Hertz–Mindlin contact mechanics with bonding and JKR cohesion models. Key parameters in the simulations were calibrated through systematic experimentation. Using Plackett–Burman design, critical factors significantly affecting axial compressive force—including surface energy, normal bond stiffness, and tangential bond stiffness—were identified. Subsequently, Box–Behnken response surface methodology was employed to optimize these parameters by minimizing deviations between simulated and experimental maximum axial compressive forces under each moisture condition. The calibrated models demonstrated high fidelity, with average relative errors of 4.53%, 3.36%, 3.05%, 3.32%, and 7.60% for uniaxial compression simulations across the five moisture levels. Stress–strain analysis under axial loading revealed that at a given surface displacement, both fracture dimensions and stress transfer rates decreased progressively with increasing moisture content. These findings elucidate the moisture-dependent micromechanical behavior of lateritic soil and provide critical data support for DEM-based design optimization of soil-engaging agricultural implements in tropical environments. Full article

This research presents a comprehensive methodology for calibrating Discrete Element Method (DEM) parameters governing rice grain interactions with biodegradable Polylactic Acid (PLA) components in agricultural bucket elevator systems. Rice grains, a critical global food staple requiring efficient post-harvest handling, were modeled as three-sphere clusters to accurately represent their physical dimensions (6.5 mm length), while the Hertz–Mindlin contact model provided the theoretical framework for particle interactions. The calibration process employed a multi-phase experimental design integrating Plackett–Burmann screening, steepest ascent method, and Face Central Composite Design to systematically identify and optimize critical micro-mechanical parameters for agricultural material handling. Statistical analysis revealed the coefficient of static friction between rice and PLA as the dominant factor, contributing 96.49% to system performance—significantly higher than previously recognized in conventional agricultural processing designs. Response Surface Methodology generated predictive models achieving over 90% correlation with experimental results from 3D-printed PLA shear box tests. Validation through comparative velocity profile analysis during bucket elevator discharge operations confirmed excellent agreement between simulated and experimental behavior despite a 20% discharge velocity variance that warrants further investigation into agricultural material-specific phenomena. The established parameter set enables accurate virtual prototyping of sustainable agricultural handling equipment, offering post-harvest processing engineers a powerful tool for optimizing bulk material handling systems with reduced environmental impact. This integrated approach bridges fundamental agricultural material properties with sustainable engineering design principles, providing a scalable framework applicable across multiple agricultural processing operations using biodegradable components. Full article

The mechanical characteristics of soil–rock mixtures (S-RMs) are essential for ensuring geotechnical engineering stability and are significantly influenced by the microstructure’s contact network configuration. Due to the irregularity of particle shapes and the variability in particle grading with S-RMs, their macro-mechanical characteristics and mesoscopic contact skeleton distribution exhibit increased complexity. To further elucidate the macro-mesoscopic mechanical behavior of S-RMs, this study employed the DEM to develop a model incorporating irregular specimens representing various states, based on CT scan outlines, and applied flexible boundary conditions. A main skeleton system of contact force chains is an effective methodology for characterizing the dominant structural features that govern the mechanical behavior of soil–rock mixture specimens. The results demonstrate that the strength of S-RMs was significantly influenced by gravel content and consolidation state; however, the relationship is not merely linear but rather intricately associated with the strength and distinctiveness of the contact force chain skeleton. In the critical state, the mechanical behavior of S-RMs was predominantly governed by the characteristics of the principal contact force skeleton: the contact force skeleton formed by gravel–gravel, despite having fewer contact forces, exhibits strong contact characteristics and an exceptionally high-density distribution of weak contacts, conferring the highest shear strength to the specimens. Conversely, the principal skeleton formed through gravel–sand exhibits contact characteristics that are less distinct compared to those associated with strong contacts. Simultaneously, the probability density distribution of weak contacts diminishes, resulting in reduced shear strength. The contact skeleton dominated by sand–sand contact forces displays similar micro-mechanical characteristics yet possesses the weakest macroscopic behavior strength. Consequently, the concept of the main skeleton of contact force chains utilized in this study presents a novel research approach for elucidating the macro- and micro-mechanical characteristics of multiphase media. Full article

Granitic faults within the crystalline upper-to-middle continental crust play a critical role in accommodating tectonic deformation and controlling earthquake nucleation. To better understand their frictional behavior, we review experimental studies conducted under both dry and hydrothermal conditions using velocity-stepping (VS), constant-velocity (CV), and slide-hold-slide (SHS) tests. These approaches allow the quantification of frictional strength, velocity dependence, and healing behavior across a range of conditions. Our synthesis highlights that the friction coefficient of granite gouges decreases with increasing temperature and pore fluid pressure, decreasing slip velocity, and increasing slip displacement. The velocity-weakening regime shifts to higher temperatures with increasing slip velocity or decreasing pore fluid pressure. Temperature, normal stress, pore fluid pressure, and slip velocity interact to modulate frictional stability. In particular, microstructural observations reveal that grain size reduction, pressure solution creep, and fluid-assisted chemical processes are key mechanisms governing transitions between velocity-weakening and velocity-strengthening regimes. These insights support the growing application of microphysical-based models, which integrate micromechanical processes and offer improved extrapolation from the laboratory to natural fault systems compared to classical rate-and-state friction laws. The collective evidence underscores the importance of considering fault rheology in a temperature- and fluid-sensitive context, with implications for interpreting seismic cycle behavior in continental regions. Full article

The study of the mechanical properties of moderately consolidated sandstones is crucial for engineering safety assessments. As an effective research tool, the discrete element method (DEM) encounters challenges during the modeling phase, such as a large number of micromechanical parameters, low modeling efficiency, and unclear coupling mechanisms among multiple parameters. To address these issues, this paper proposes a calibration method for the micromechanical parameters of DEM models for moderately consolidated sandstones. By integrating orthogonal experimental design with a multivariate analysis of variance, the influence of micromechanical parameters on macroscopic mechanical properties is quantified, and a parameter prediction model is constructed using an intelligent dynamic regression selection mechanism, significantly improving the efficiency and accuracy of micromechanical parameter calibration. The results show that the macroscopic elastic modulus E is primarily controlled by the effective modulus ($\overline{E}$), stiffness ratio (k), and particle size ratio (R_max/R_min), following a linear relationship. The influence of the particle size ratio decreases significantly once it exceeds a threshold value. The macroscopic uniaxial compressive strength (UCS) is dominated by cohesion ($\overline{c}$) and tensile strength (${\overline{\mathrm{\sigma }}}_c$), exhibiting a polynomial relationship, where a stronger synergistic effect is generated when both parameters are at higher levels. Poisson’s ratio ($\mu$) is significantly correlated only with the stiffness ratio (k), following a logarithmic relationship. An iterative correction method for micromechanical parameter calibration is proposed. The errors between the three groups of simulation results and laboratory test results are all less than 10%, and the crack distribution patterns show a high degree of consistency. The findings of this study provide a theoretical foundation and technical means for exploring the mechanical behavior and damage mechanism of moderately consolidated sandstones. Full article

This article analyzes the residual stresses generated during the curing process of thermoset composites. Specifically, a numerical procedure is developed and implemented in Ansys 18.0 to evaluate, at the micromechanical level, the residual stresses in a carbon epoxy composite that undergoes the process of curing. The viscoelastic behavior of the epoxy material is modeled using a formulation recently published by the same authors. It accounts for the concurrent effect of curing and structural relaxation on epoxy’s relaxation times, assuming thermo-rheological and thermo-chemical simplicities. The model validated for the neat epoxy matrix is now tested against the composite application. Various representative volume element (RVE) arrangements and fiber fractions are examined. The proposed procedure can predict the evolution of mechanical properties (apparent stiffness and creep compliance) and the residual stresses that develop in each composite constituent during the cure. It demonstrates that the residual stresses in the matrix are a consistent fraction of an epoxy’s nominal strength and significantly influence the transverse mechanical properties of the composite. Full article

The enhancements of tax policies and their coordination have emerged as a significant way to promote corporate sustainability, especially in developing economies worldwide. Using panel data from Chinese non-financial A-share listed companies from 2009 to 2022, this study empirically explores the promoting effects of corporate income tax (CIT) incentives and environmental protection tax (EPT) levies on corporate ESG performance. We find that the CIT incentive has a notable positive impact on firms’ ESG behavior, acting on the micro-mechanisms of increasing corporate cash flow and reducing agency costs, and its promoting effect is more salient with regard to the social and governance dimensions. This study also traces the interactive effects between the EPT levy and CIT incentive policies, which boost corporate ESG behavior synergistically. Heterogeneity analyses reveal that these effects are more noticeable in manufacturing firms and non-state-owned firms with severe financing constraints. Environmental tests show that CIT incentive policies have positive effects on green technological innovation, and Chinese enterprises are still experiencing relatively serious negative impacts. The conclusions of this study are conducive to providing theoretical support and policy suggestions for encouraging the sustainable development of companies through the policy combination of environmental regulation and tax incentives. Full article

Understanding user interaction patterns during technology-triggered public discourse provides critical insights into how emerging technologies gain social meaning. This study develops an intelligent digital twin framework for modeling discourse dynamics around DeepSeek, an indigenous large language model that generated approximately 250,000 social media interactions during a 13-day period. By integrating LLM-enhanced semantic analysis with agent-based modeling, we create a comprehensive virtual representation that captures both content characteristics and behavioral dynamics. Our analysis identifies six distinct thematic domains that structure public engagement: Technological Competition, Technological Breakthrough, User Feedback, Financial Market, Social Influence, and Information Security. The agent-based simulation reveals distinctive participation and sentiment patterns across different user segments, with general users expressing stronger positive sentiments than domain experts and institutional accounts. Network analysis demonstrates the evolution from random-like initial connection patterns to scale-free structures with pronounced influence hubs. The simulation results illuminate how individual behaviors aggregate to produce complex discourse patterns, offering insights into the micro-mechanisms underlying technology reception. This research advances digital twin methodologies beyond physical systems into social phenomena, providing a framework for anticipating public responses to technological innovations and informing more effective communication strategies. Full article