Search Results (1,452)

This study investigates date palm fiber (DPF) and sheep wool hybrid polyester composites with fiber loadings of 0%, 10%, 20%, and 30% by weight, fabricated by compression molding, to develop a sustainable and reliable material system. Experimental data from prior work were modeled using Weibull analysis for reliability evaluation and response surface methodology (RSM) for multi-objective optimization. Weibull statistics fitted a two-parameter distribution to tensile strength and fracture toughness, extracting shape (η) and scale ($\beta$) parameters to quantify variability and failure probability. The analysis showed that 20% hybrid content achieved the highest scale values ($\beta$ = 28.85 MPa for tensile strength and $\beta$ = 15.03 $\mathrm{M}\mathrm{P}\mathrm{a}\sqrt{\mathrm{m}}$ for fracture toughness) and comparatively low scatter (η = 10.39 and 9.2, respectively), indicating superior reliability. RSM quadratic models were developed for tensile strength, fracture toughness, thermal conductivity, acoustic attenuation, and water absorption, and were combined using desirability functions. The RSM optimization was found at 18.97% fiber content with a desirability index of 0.673, predicting 25.89 MPa tensile strength, 14.23 $\mathrm{M}\mathrm{P}\mathrm{a}\sqrt{\mathrm{m}}$ fracture toughness, 0.08 W/m·K thermal conductivity, 20.49 dB acoustic attenuation, and 5.11% water absorption. Overlaying Weibull cumulative distribution functions with RSM desirability surfaces linked probabilistic reliability zones (90–95% survival) to the deterministic optimization peak. This integration establishes a unified framework for designing natural fiber composites by embedding reliability into multi-property optimization. Full article

Repurposing end-of-life hydrocarbon wells for geothermal energy generation offers a cost-effective and sustainable strategy to expand low-carbon energy deployment while utilizing existing infrastructure. Fracture-connected horizontal oil and gas well pairs present a promising configuration for enhancing heat transfer in low-permeability reservoirs. Existing modeling approaches, however, lack the ability to simulate transient heat conduction from rock to fluid in such complex fracture pathways. This work develops a mathematical model that couples time-dependent heat conduction in the reservoir rock with convective heat transport within the fractures. This model enables prediction of heat energy productivity of converted well pairs by accounting for realistic boundary conditions and operational parameters. In applying the model to a representative shale gas field in Louisiana, key factors affecting fluid temperature and thermal power output, including fracture geometry, fluid flow rate, and wellbore insulation, were considered. The results demonstrate the feasibility and sensitivity of converting hydrocarbon wells into geothermal energy production, providing critical insight for optimizing such conversions to support the increased demand for clean, sustainable energy. Full article

Short-range forces enable peridynamics to simulate impact, yet it demands a computationally expensive contact search and includes no intrinsic damping. A significantly more efficient solution is the coupled dual-horizon peridynamics–discrete element method approach, which provides a robust framework for modeling fracture. The peridynamics component handles the nonlocal continuum mechanics capabilities to predict material damage and fracture, while the discrete element method captures discrete particle behavior. Whereas existing peridynamics–discrete element method approaches assign discrete element method particles to many or all surface peridynamics points, the proposed method integrates dual-horizon peridynamics with a single discrete element particle representing each object. Contact forces are computed once per discrete element pair and mapped to overlapping peridynamics points in proportion to shared volume, conserving linear momentum. Benchmark sphere-on-plate impact demonstrates prediction of peak contact force, rebound velocity, and plate deflection within 5% of theoretical results found in the literature, while decreasing neighbour-search cost by more than an order of magnitude. This validated force-transfer mechanism lays the groundwork for future extension to fully resolved fracture and fragmentation. Full article

The Ordos Basin is a key district for sandstone-hosted uranium, yet host-rock controls and uranium sources remain debated. We integrate measured sections, whole-rock geochemistry, and detrital zircon U-Pb-Lu-Hf data from the Cretaceous Huanhe Formation (Yihewusu, northern Ordos) to resolve provenance, transport, and enrichment pathways. Uranium enrichment is concentrated in feldspathic-lithic sandstones deposited in proximal fluvial-lacustrine settings. Detrital zircon ages define three clusters—Phanerozoic (500–200 Ma), Paleoproterozoic (2000–1700 Ma), and Neoarchean (2600–2300 Ma)—with Proterozoic grains >60%, indicating derivation from Archean–Paleoproterozoic TTG gneisses, granulites, and khondalites of the Yinshan Block and the northern Central Orogenic Belt. Zircon εHf(t) values (−10.84 to +7.76) and crustal model ages (3.2–2.1 Ga) record substantial Meso- to Neoarchean crustal growth in the source terranes. Critically, Permian-Cretaceous intermediate-felsic igneous rocks along the northern margin of the Western North China Block—marked by elevated U, Th/U > 5 (indicative of U loss), pervasive feldspar micro-fractures, and proximity to basin-margin uranium belts—are identified as the principal uranium reservoirs. We propose a dual uranium supply: soluble uranium mobilized from leached igneous rocks during weathering and fluid-rock interaction, and U-enriched detritus delivered to the basin. Uranium concentrated in redox-sensitive, feldspathic-lithic sandstones of the Huanhe Formation, which effectively trapped advected uranium at proximal facies transitions. These findings establish a direct genetic link between basin-margin uranium sources and in-basin mineralization, providing a predictive framework for regional uranium exploration in North China. Full article

This study investigates the mechanisms of hazardous gas outbursts in geologically complex non-coal tunnels. This is a critical safety concern during excavation, particularly at specific locations and during time-sensitive periods. To address this, a gas–solid coupled numerical model is established to simulate gas seepage processes under such conditions. The simulations systematically reveal the spatiotemporal evolutionary patterns of the velocity and direction of the gas seepage and elucidate the migration mechanism driven by excavation-induced pressure gradients. The model specifically analyzes how geological structures, such as rock joints and fractures, control the seepage pathways. The model also demonstrates the dynamic variations in and enrichment behavior of the gas escape velocities near these discontinuities. Field measurements obtained from the Hongdoushan Tunnel validated the simulated emission patterns along jointed fissures. The findings clarify the intrinsic relationships between the outburst dynamics and key factors that include pressure differentials, geological structures, and temporal effects. This work provides a crucial theoretical foundation and practical strategy for the prediction and prevention of hazardous gas disasters in analogous tunnel engineering projects, thereby enhancing overall construction safety. Full article

Background/Objectives: Screw loosening and vertebral fractures remain common after vertebral body tethering (VBT). Because tightening torque sets screw preload, its biomechanical effect warrants explicit modeling. In this paper, a Finite Element (FE) model, supported by ex vivo porcine vertebral tests, was developed and validated that incorporates torque-induced pre-tension to quantify vertebral stress, aiming toward customizable VBT planning. Methods: An FE model with pre-tension and axial extraction failure was parameterized using ex vivo tests on five porcine vertebrae. A laterally inserted surgical screw in each specimen was tightened to $5.9\pm 0.80$ Nm. Axial extraction produced failure loads of $2.1\pm 0.31$ kN. This is also considered in the FE model to validate the failure scenario. Results: Torque alone generated peak von Mises stresses of $16.1\pm 0.86$ MPa (cortical bone 1) and $2.1\pm 0.13$ MPa (trabecular), lower than prior reports. With added axial load, peaks rose to $141.1\pm 0.70$ MPa and $19.7\pm 0.23$ MPa, exceeding typical ranges. However, predicted failure agreed with experiments, showing $0.58$ mm displacement and a conical displacement distribution around the washer. Conclusions: Modeling torque-induced pre-tension is essential to reproduce realistic stress states and anchor failure in VBT. The framework enables patient-specific assessment (bone geometry/density) to recommend safe tightening torques, potentially reducing screw loosening and early fractures. Full article

The estimation of the area susceptible to rock failure and the prediction of its movement process are pivotal for hazard mitigation, yet they are also challenging. In this study, we proposed a novel integrated method combining field investigation, remote sensing, and three-dimensional discrete element method (DEM) simulation to achieve our goal. The field investigation and remote sensing analysis are used for the purpose of ascertaining the deformation phenomenon and the structure of the rock slope, identifying the potential failure position and area of the slope. Subsequently, a three-dimensional DEM simulation is employed to quantitatively assess the potential rock failure-affected area and movement process, based on the above potential failure information. The simulation results demonstrate that potential rock failure persists for approximately 30 s, and its movement process can be categorized into two distinct stages: acceleration and deceleration. The initial acceleration stage is characterized by a duration of 10 s, culminating in a peak average velocity of 13 m/s. The subsequent deceleration stage extends for a duration of 20 s. Notably, the maximum attainable velocity for the segment of rock mass under consideration is estimated to be 50 m/s. Furthermore, the model demonstrates the variation in fracture energy, friction energy, and kinetic energy over time. The potential affected area is 140,000 m², and approximately 8000 m² of residential construction will be destroyed if a rock failure occurs. It is imperative to implement measures aimed at the prevention of rock failure in order to mitigate the risk of such an occurrence. Full article

The strong heterogeneity in and complex engineering conditions of deep shale gas reservoirs make productivity prediction challenging, especially in nascent blocks where data is scarce. This scarcity constitutes a critical research gap for the application of data-driven methods. To bridge this gap, we develop an interpretable framework by combining grey relational analysis (GRA) with three machine learning algorithms: Random Forest (RF), Support Vector Machine (SVR), and eXtreme Gradient Boosting (XGBoost). Utilizing small-sample data from 87 shale gas wells in the study area, eight key controlling factors were identified, namely, total fracturing fluid volume, proppant intensity, average tubing head pressure, pipeline transfer pressure, casing head pressure, ceramic proppant fraction, fluid placement intensity, and flowback recovery ratio. These factors were used to train, optimize, and validate a productivity prediction model tailored for deep shale gas horizontal wells. The results demonstrate that XGBoost delivers the highest predictive accuracy and generalization capability, achieving an R² of 0.907 for productivity prediction—surpassing RF and SVR by 12.11% and 131.38%, respectively. Integrating SHapley Additive exPlanations (SHAP) interpretability analysis further enabled immediate post-fracturing productivity assessment and engineering parameter optimization. This research provides a reliable, data-driven strategy for predicting productivity and optimizing operations within the studied block, offering a valuable template for development in geologically similar areas. Full article

Glass fiber reinforced polymer (GFRP) composite profiles produced by pultrusion method are widely used as an alternative to traditional building materials due to their lightness and corrosion resistance. However, these materials are susceptible to crushing type fractures known as “web crippling” especially under local loading due to their anisotropic structure and limited mechanical strength. Understanding web-crippling behavior is crucial for the safe and efficient structural application of pultruded GFRP profiles. This study report narrated the review of experimental, numerical, and analytical investigations of web-crippling behavior of pultruded GFRP profiles. Highlights of the major findings include profile geometry and detailing of the flange–web joint, loading types (end-two-flange (ETF), interior-two-flange (ITF), end bearing with ground (EG), interior bearing with ground (IG)), bearing plate dimensions, presence of web openings, and elevated temperatures. It also considers the limitations of current standards, along with new modeling techniques that incorporate finite element analysis as well as artificial intelligence. Damage types such as web–flange joint fractures, crushing, and buckling were comparatively analyzed; design approaches based on finite element modeling and artificial intelligence-supported prediction models were also included. These insights provide guidance for optimizing profile design and improving predictive models for structural engineering applications. Gaps in current design standards and modeling approaches are highlighted to guide future research. Full article

To evaluate the creep and fatigue fracture lives of structural acrylic spliced components fabricated via bulk polymerization, and to elucidate the associated fracture mechanisms, this study conducted creep and fatigue tests on spliced coupons annealed at 85 °C and 65 °C, as well as base material coupons. The experimental life data were fitted using log-log linear regression models. Based on statistical analysis, a simple yet robust statistical framework was established for life prediction, featuring three design curves: 97.7% survival curves, improved 95% confidence interval lower bounds, and one-sided tolerance curves. Fractographic examination using scanning electron microscopy (SEM) was performed to characterize macroscopic failure modes. The results indicate distinct threshold behavior between stress levels and both creep and fatigue life. The creep threshold stresses are 25 MPa for the base material, 29 MPa for the spliced coupons annealed at 85 °C, and 17 MPa for the spliced coupons annealed at 65 °C. Corresponding fatigue threshold stress amplitudes are 21 MPa, 22 MPa, and 31 MPa, respectively. Failure in the base material is primarily initiated by randomly distributed internal defects, whereas failure in the spliced coupons is mainly caused by defects within the seam or interfacial tearing. Full article

Hydrogen-assisted fatigue cracking presents a critical challenge to the structural integrity of legacy carbon steel natural gas pipelines being repurposed for hydrogen transport, posing a major barrier to the deployment of hydrogen infrastructure. This study systematically evaluates the fatigue crack growth (FCG) behavior of API 5L X60 pipeline steel under varying hydrogen–natural gas (H₂–NG) blending conditions to assess its suitability for long-term hydrogen service. Experiments are conducted using a custom-designed autoclave to replicate field-relevant environmental conditions. Gas mixtures range from 0% to 100% hydrogen by volume, with tests performed at a constant pressure of 6.9 MPa and a temperature of 25 °C. A fixed loading frequency of 8.8 Hz and load ratio (R) of 0.60 ± 0.1 are applied to simulate operational fatigue loading. The test matrix is designed to capture FCG behavior across a broad range of stress intensity factor values (ΔK), spanning from near-threshold to moderate levels consistent with real-world pipeline pressure fluctuations. The results demonstrate a clear correlation between increasing hydrogen concentration and elevated FCG rates. Notably, at 100% hydrogen, API X60 specimens exhibit crack propagation rates up to two orders of magnitude higher than those in 0% hydrogen (natural gas) conditions, particularly within the Paris regime. In the lower threshold region (ΔK ≈ 10 MPa·√m), the FCG rate (da/dN) increased nonlinearly with hydrogen concentration, indicating early crack activation and reduced crack initiation resistance. In the upper Paris regime (ΔK ≈ 20 MPa·√m), da/dNs remained significantly elevated but exhibited signs of saturation, suggesting a potential limiting effect of hydrogen concentration on crack propagation kinetics. Fatigue life declined substantially with hydrogen addition, decreasing by ~33% at 50% H₂ and more than 55% in pure hydrogen. To complement the experimental investigation and enable predictive capability, a modular machine learning (ML) framework was developed and validated. The framework integrates sequential models for predicting hydrogen-induced reduction of area (RA), fracture toughness (FT), and FCG rate (da/dN), using CatBoost regression algorithms. This approach allows upstream degradation effects to be propagated through nested model layers, enhancing predictive accuracy. The ML models accurately captured nonlinear trends in fatigue behavior across varying hydrogen concentrations and environmental conditions, offering a transferable tool for integrity assessment of hydrogen-compatible pipeline steels. These findings confirm that even low-to-moderate hydrogen blends significantly reduce fatigue resistance, underscoring the importance of data-driven approaches in guiding material selection and infrastructure retrofitting for future hydrogen energy systems. Full article

Paulownia wood, as a fast-growing natural material, exhibits inherently low axial compressive strength. To improve the axial structural performance of Paulownia wood, wood-cored glass fiber-reinforced polymer (GFRP) sandwich Paulownia wood columns were developed in this study. Nevertheless, the behavior of such columns remained largely unexplored—particularly under elevated temperatures and upon subsequent cooling. Consequently, an experimental program was conducted to characterize the influences of GFRP wrapping layers, steel hoop end confinement, high temperature, post-cooling strength recovery, and chamfer radius on the axial compressive performance of the columns. End crushing occurred in the absence of steel hoops, whereas mid-height fracture dominated when end confinement was provided. As the temperature rose from room temperature to 100 °C and 200 °C, the load-bearing capacity of the columns decreased by 38.26% and 54.05%, respectively, due to the softening of the GFRP composites. After cooling back to room temperature, the post-high-temperature specimens recovered approximately 95% of their original capacity, confirming that no significant thermal decomposition had been initiated. The load-bearing capacity also increased significantly with the number of GFRP layers, as the additional thickness provided both higher axial load capacity and enhanced lateral confinement of the wood core. Relative to a 4.76 mm chamfer, a 9.52 mm radius increased axial capacity by 14.07% by mitigating stress concentration. A theoretical model accounting for lateral confinement was successfully developed to predict the axial load-bearing capacity of the wood-cored GFRP sandwich columns. Full article

As an important technology to enhance nutrient use efficiency and reduce agricultural non-point source pollution, controlled-release fertilizers (CRFs) have been widely applied in modern agriculture. However, during packaging, transportation, and field application, CRF particles are prone to mechanical impacts, which can lead to particle fragmentation and damage to the controlled-release coating. This compromises the release kinetics, increases nutrient loss risk, and ultimately exacerbates environmental issues such as eutrophication. Currently, studies on the impact-induced fragmentation behavior of CRF particles remain limited, and there is an urgent need to investigate their fragmentation susceptibility mechanisms from the perspective of internal stress evolution. In this study, the mechanical properties of CRF particles were first experimentally determined to obtain essential parameters. A two-layer finite element model representing the coating and core structure of the particles was then constructed, and a fragmentation susceptibility index was proposed as the key evaluation criterion. The index, defined as the ratio of fractured volume to peak impact energy, reflects the efficiency of energy conversion at the critical moment of particle rupture (1–5). An explicit dynamic simulation framework incorporating multiple influencing factors—equivalent diameter, sphericity, impact material, velocity, and angle—was developed to analyze fragmentation behavior from the perspective of energy transformation. Based on the observed effects of these variables on fragmentation susceptibility, three regression models were developed using response surface methodology to quantitatively predict fragmentation susceptibility. Comparative analysis between the simulation and experimental results showed a fragmentation rate error range of 0–11.47%. The findings reveal the relationships between particle fragmentation modes and energy responses under various impact conditions. This research provides theoretical insights and technical guidance for optimizing the mechanical stability of CRFs and developing environmentally friendly fertilization strategies. Full article

Bone fragility represents a significant global health burden, characterized by the deterioration of bone strength, increased brittleness, and heightened fracture susceptibility. Osteoporosis substantially elevates the risk of fragility fractures, the principal clinical manifestation of the disease. Current diagnostic approaches, including biomedical imaging, bone strength assessment, and bone mineral density measurement, are closely linked to identifying bone fragility through various predictive models and tools. Although numerous studies have employed predictors to characterize fragility fractures, few have comprehensively examined the morpho-structural features of bone across multiple hierarchical scales, limiting the ability to fully elucidate the underlying mechanisms of bone fragility. This review summarizes recent advancements in predictive modeling and novel diagnostic tools, focusing on multiscale approaches for assessing bone fragility. We critically evaluate the translational potential of these tools for the early detection of fragility fractures and their clinical application in mitigating fracture risk. Moreover, this study discusses the integration of multiscale predictive methodologies, which promise to enhance early-stage bone fragility detection and potentially prevent severe fractures through timely intervention. Finally, the study reflects on current research limitations, addressing the challenges associated with multiscale predictive modeling of bone fragility, and proposes future directions to refine these tools to improve the accuracy and utility of fragility fracture prediction and prevention strategies. Full article

Nano- and micro-indentation have become essential tools for quantifying the micromechanical behavior of rocks beyond traditional macroscopic tests. This review summarizes the historical evolution, experimental methodologies, and interpretation models (e.g., Oliver–Pharr, Doerner–Nix, energy-based methods, Hertz/ECM/Lawn), with a particular focus on rock-specific challenges such as heterogeneity, anisotropy, and surface roughness. A structured literature survey (1980–August 2025) covers representative studies on shale, limestone, marble, sandstone, claystone, and granite. The transition from classical hardness measurements to advanced instrumented indentation has enabled more reliable determination of localized properties, including hardness, elastic modulus, fracture toughness, and creep. Special attention is given to the applicability and limitations of different interpretation models when applied to heterogeneous and anisotropic rocks. Current challenges include high sensitivity to surface conditions and difficulties in capturing the full complexity of natural rock behavior. Looking forward, promising directions involve intelligent systems that integrate AI-driven data analytics, robotic automation, and multiscale modeling (from molecular dynamics to continuum FEM) to enable predictive material design. This review aims to provide geoscientists and engineers with a comprehensive foundation for the effective application and further development of indentation-based testing in rock mechanics and geotechnical engineering. Full article