Search Results (1,298)

Fatigue crack propagation is a critical failure mechanism in engineering structures, requiring meticulous monitoring for timely maintenance. This research introduces a deep learning framework for estimating fatigue fracture length in metallic plates through acoustic emission (AE) signals. AE waveforms recorded during crack growth are transformed into time-frequency images using the Choi–Williams distribution. First, a clustering system is developed to analyze the distribution of the AE image-based dataset. This system employs a CNN-based model to extract features from the input images. The AE dataset is then divided into three categories according to fatigue lengths using the K-means algorithm. Principal Component Analysis (PCA) is used to reduce the feature vectors to two dimensions for display. The results show how close together the data points are in the clusters. Second, convolutional neural network (CNN) models are trained using the AE dataset to categorize fracture lengths into three separate ranges. Using the pre-trained models ResNet50V2 and VGG16, we compare the performance of a bespoke CNN using transfer learning. It is clear from the data that transfer learning models outperform the custom CNN by a wide margin, with an accuracy of approximately 99% compared to 93%. This research confirms that convolutional neural networks (CNNs), particularly when trained with transfer learning, are highly successful at understanding AE data for data-driven structural health monitoring. Full article

This critical review explores the transformative impact of artificial intelligence (AI), particularly machine learning (ML) and computer vision (CV), on scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) for metallic microstructure analysis, spanning research from 2010 to 2025. It critically evaluates how AI techniques balance automation, accuracy, and scalability, analysing why certain methods (e.g., Vision Transformers for complex microstructures) excel in specific contexts and how trade-offs in data availability, computational resources, and interpretability shape their adoption. The review examines AI-driven techniques, including semantic segmentation, object detection, and instance segmentation, which automate the identification and characterisation of microstructural features, defects, and inclusions, achieving enhanced accuracy, efficiency, and reproducibility compared to traditional manual methods. It introduces the Microstructure Analysis Spectrum, a novel framework categorising techniques by task complexity and scalability, providing a new lens to understand AI’s role in materials science. The paper also evaluates AI’s role in chemical composition analysis and predictive modelling, facilitating rapid forecasts of mechanical properties such as hardness and fracture strain. Practical applications in steelmaking (e.g., automated inclusion characterisation) and case studies on high-entropy alloys and additively manufactured metals underscore AI’s benefits, including reduced analysis time and improved quality control. Extending prior reviews, this work incorporates recent advancements like Vision Transformers, 3D Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs). Key challenges—data scarcity, model interpretability, and computational demands—are critically analysed, with representative trade-offs from the literature highlighted (e.g., GANs can substantially augment effective dataset size through synthetic data generation, typically at the cost of significantly increased training time). Full article

This study investigates the reuse of mechanically recycled polyester–glass thermoset scraps (PS) as fillers in LDPE and HDPE matrices at 10–50 wt.% loading. Composites were produced through mechanical size reduction, single-screw extrusion, and compression molding without compatibilizers, and their mechanical and microstructural properties were systematically evaluated. LDPE composites exhibited a notable stiffness increase, with tensile modulus rising from 318.8 MPa (neat) to 1245.6 MPA (+291%) and tensile strength improving from 9.50 to 11.45 MPa (+20.5%). Flexural performance showed even stronger reinforcement: flexural modulus increased from 0.40 to 3.00 GPa (+650%) and flexural strength from 14.5 to 35.6 MPa (+145%). HDPE composites displayed similar behavior, with flexural modulus increasing from 1.2 to 3.1 GPa (+158%) and strength from 34.1 to 45.5 MPa (+33%). Surface-treated fillers provided additional stiffness gains (+36% in sPL4; +33% in sPH3). Impact strength decreased with loading (LDPE: −51%, HDPE: −61%), though surface treatment partially mitigated this (+14–19% in LDPE; +13% in HDPE). Density increased proportionally (PL: 0.95 → 1.20 g/cm³, PH: 0.99 → 1.23 g/cm³), while moisture uptake remained low (≤0.25%). Optical and SEM analyses indicated increasingly interconnected fiber networks at high loadings, driving stiffness and fracture behavior. Overall, PS-filled polyolefins offer a scalable route for converting thermoset waste into functional semi-structural materials. Full article

This study aims to compare the surface roughness and fracture resistance of implant-supported permanent crowns additively manufactured using composite resins (Crowntec, VarseoSmile) versus subtractively manufactured polymer-infiltrated hybrid ceramic (VITA Enamic) at various wall thicknesses using an experimental setup as close to clinical as possible. 180 crowns were fabricated in three thicknesses (1.0, 1.5, and 2.0 mm) and cemented onto titanium abutments. Experimental groups underwent thermal aging (10,000 cycles) to simulate one year of clinical service. Surface roughness was measured via profilometry, and fracture resistance was assessed using a universal testing machine. Composite resin crowns exhibited lower surface roughness and lower fracture resistance than subtractively manufactured crowns. No significant difference in fracture resistance was found between materials at 1.0 mm (p > 0.05). However, at 1.5 and 2.0 mm, hybrid ceramic network crowns showed significantly higher resistance (p < 0.01). It was concluded that, within the limitations of this 1-year simulated study, both material-method combinations met the biological threshold for surface roughness. Regarding fracture resistance, composite resins and hybrid ceramics satisfied clinical requirements for molar bite forces only at thicknesses of 1.5 mm and above. 1.0 mm thickness may pose a risk under high occlusal loads. Full article

Aiming to predict the evolution of fracture structures under stress conditions and the Permeability process of the fracture network, a damage evolution model reflecting the coupling mechanism between topological characteristics and mechanical responses of fracture networks is established based on yield criteria and complex network theory, realizing a prediction for permeability processes. Firstly, key parameters such as degree centrality, betweenness centrality, and clustering coefficient of fracture nodes are extracted through complex network topological analysis. Combined with the finite element method to calculate the node shear stress transfer coefficient, a topology–mechanics coupling model of the fracture network is constructed. Secondly, the Coulomb–Mohr yield criterion is improved to establish a damage evolution equation considering normal stress and shear stiffness degradation. Based on the above theory, a fracture network permeability iterative algorithm was developed to simultaneously update the network topology and the stress distribution of the fracture network. The evolution process of the network was analyzed based on the adjacency matrix and the changes in the number of connected clusters. The results show that the average degree of the largest cluster directly reflects the connectivity of the fracture network; a higher average degree corresponds to greater damage to the fracture network under stress. The average clustering coefficient indicates the extent of local connectivity; a higher clustering coefficient signifies denser local connections, which enhances the fracture network connectivity. Compared with traditional static methods, the dynamic damage evolution model has a permeability prediction error within 7%, indicating the effectiveness of this method. Full article

Lost circulation in oil-based drilling fluids (OBDFs) remains difficult to mitigate because particulate lost circulation materials depend on bridging/packing and gel systems for aqueous media often lack OBDF compatibility and controllable in situ sealing. A dual-precursor oil–water biphasic metal–organic supramolecular gel enables rapid in situ sealing in OBDF loss zones. The optimized formulation uses an oil-phase to aqueous gelling-solution volume ratio of 10:3, with 2.0 wt% Span 85, 12.5 wt% TXP-4, and 5.0 wt% NaAlO₂. Apparent-viscosity measurements and ATR–FTIR analysis were used to evaluate the effects of temperature, time, pH, and shear on MOSG gelation. Furthermore, the structural characteristics and performances of MOSGs were systematically investigated by combining microstructural characterization, thermogravimetric analysis, rheological tests, simulated fracture-plugging experiments, and anti-shear evaluations. The results indicate that elevated temperatures (30–70 °C) and mildly alkaline conditions in the aqueous gelling solution (pH ≈ 8.10–8.30) promote P–O–Al coordination and strengthen hydrogen bonding, thereby facilitating the formation of a three-dimensional network. In contrast, strong shear disrupts the nascent network and delays gelation. The optimized MOSGs rapidly exhibit pronounced viscoelasticity and thermal resistance (~193 °C); under high shear (380 rpm), the viscosity retention exceeds 60% and the viscosity recovery exceeds 70%. In plugging tests, MOSG forms a dense sealing layer, achieving a pressure-bearing gradient of 2.27 MPa/m in simulated permeable formations and markedly improving the fracture pressure-bearing capacity in simulated fractured formations. Full article

Background/Objectives: Abaloparatide is an osteoanabolic therapy used in patients at high risk of fracture; however, the breadth of evidence across routes, comparators, and sequential strategies has not yet been comprehensively summarized. This study aimed to evaluate the efficacy and safety of abaloparatide for reducing fractures and improving bone mineral density (BMD) in adults with osteoporosis. Methods: Following PRISMA 2020, we searched PubMed, Embase, CENTRAL, and Web of Science (2016–2024) for randomized controlled trials and comparative real-world studies. Additional meta-analyses and network meta-analyses were included as contextual evidence but not pooled to avoid double-counting. Primary outcomes were vertebral, non-vertebral, and hip fractures; secondary outcomes included percentage change in BMD and safety endpoints. Random-effects models were used; heterogeneity, influence analyses, and prediction intervals were examined. Risk of bias was assessed using RoB 2 and AMSTAR 2. Results: Nine quantitative evidence sources met the criteria. Abaloparatide reduced vertebral fractures (RR 0.13–0.21) and showed moderate reductions in non-vertebral fractures. Lumbar spine BMD increased substantially, while hip and femoral neck gains were smaller and heterogeneous. Hypercalcemia risk was consistently lower compared to teriparatide. Transdermal delivery was less effective, and sequential abaloparatide → antiresorptive therapy further reduced fractures. Serious adverse events were not increased. Conclusions: Abaloparatide provides strong vertebral protection, significant BMD improvement, and shows a favorable calcemic profile, with moderate certainty for non-vertebral effects. Evidence in men and long-term safety remains limited. Full article

Supercritical CO₂ modifies deep coal reservoirs through the coupled effects of adsorption-induced deformation and geochemical dissolution. CO₂ adsorption causes coal matrix swelling and facilitates micro-fracture propagation, while CO₂–water reactions generate weakly acidic fluids that dissolve minerals such as calcite and kaolinite. These synergistic processes remove pore fillings, enlarge flow channels, and generate new dissolution pores, thereby increasing the total pore volume while making the pore–fracture network more heterogeneous and structurally complex. Such reservoir restructuring provides the intrinsic basis for CO₂ injectivity and subsequent CH₄ displacement. Both adsorption capacity and volumetric strain exhibit Langmuir-type growth characteristics, and permeability evolution follows a three-stage pattern—rapid decline, slow attenuation, and gradual rebound. A negative exponential relationship between permeability and volumetric strain reveals the competing roles of adsorption swelling, mineral dissolution, and stress redistribution. Swelling dominates early permeability reduction at low pressures, whereas fracture reactivation and dissolution progressively alleviate flow blockage at higher pressures, enabling partial permeability recovery. Injection pressure is identified as the key parameter governing CO₂ migration, permeability evolution, sweep efficiency, and the CO₂-ECBM enhancement effect. Higher pressures accelerate CO₂ adsorption, diffusion, and sweep expansion, strengthening competitive adsorption and improving methane recovery and CO₂ storage. However, excessively high pressures enlarge the permeability-reduction zone and may induce formation instability, while insufficient pressures restrict the effective sweep volume. An optimal injection-pressure window is therefore essential to balance injectivity, sweep performance, and long-term storage integrity. Importantly, the enhanced methane production and permanent CO₂ storage achieved in this study contribute directly to greenhouse gas reduction and improved sustainability of subsurface energy systems. The multi-field coupling insights also support the development of low-carbon, environmentally responsible CO₂-ECBM strategies aligned with global sustainable energy and climate-mitigation goals. The integrated experimental–numerical framework provides quantitative insight into the coupled adsorption–deformation–flow–geochemistry processes in deep coal seams. These findings form a scientific basis for designing safe and efficient CO₂-ECBM injection strategies and support future demonstration projects in heterogeneous deep coal reservoirs. Full article

Supercritical CO₂ (SC-CO₂) fracturing has emerged as a promising technology for the effective stimulation of unconventional tight reservoirs due to its low viscosity, high diffusivity, and environmental advantages. However, existing fracturability evaluation models often oversimplify key parameters and lack validation under realistic dual-well conditions. To address these gaps, we developed a multi-factor coupled evaluation model incorporating well spacing, stress anisotropy, and fluid viscosity and proposed a fracturability index (FI) to quantify the potential for complex fracture development. True triaxial SC-CO₂ fracturing experiments using both single- and dual-well setups were conducted, and 3D fracture networks were analyzed via CT imaging and U-Net segmentation. Results show strong agreement between FI and fracture complexity. Optimal fracturing conditions were identified, providing a practical framework for the design and optimization of SC-CO₂ fracturing in tight reservoirs. Full article

Background: Osteoporosis and osteopenia are prevalent bone diseases characterized by reduced bone mineral density (BMD) and an increased risk of fractures, particularly in postmenopausal women. While dual-energy X-ray absorptiometry (DXA) remains the gold standard for diagnosis, it has limitations regarding accessibility, cost, and predictive capacity for fracture risk. Machine learning (ML) approaches offer an opportunity to develop automated and more accurate diagnostic models by incorporating both BMD values and clinical variables. Method: This study retrospectively analyzed BMD data from 142 postmenopausal women, classified into 3 diagnostic groups: normal, osteopenia, and osteoporosis. Various supervised ML algorithms—including Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Decision Trees (DT), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and Artificial Neural Networks (ANN)—were applied. Feature selection techniques such as ANOVA, CHI2, MRMR, and Kruskal–Wallis were used to enhance model performance, reduce dimensionality, and improve interpretability. Model performance was evaluated using 10-fold cross-validation based on accuracy, true positive rate (TPR), false negative rate (FNR), and AUC values. Results: Among all models and feature selection combinations, SVM with ANOVA-selected features achieved the highest classification accuracy (94.30%) and 100% TPR for the normal class. Feature sets based on traditional diagnostic regions (L1–L4, femoral neck, total femur) also showed high accuracy (up to 90.70%) but were generally outperformed by statistically selected features. CHI2 and MRMR methods also yielded robust results, particularly when paired with SVM and k-NN classifiers. The results highlight the effectiveness of combining statistical feature selection with ML to enhance diagnostic precision for osteoporosis and osteopenia. Conclusions: Machine learning algorithms, when integrated with data-driven feature selection strategies, provide a promising framework for automated classification of osteoporosis and osteopenia based on BMD data. ANOVA emerged as the most effective feature selection method, yielding superior accuracy across all classifiers. These findings support the integration of ML-based decision support tools into clinical workflows to facilitate early diagnosis and personalized treatment planning. Future studies should explore more diverse and larger datasets, incorporating genetic, lifestyle, and hormonal factors for further model enhancement. Full article

In the mining industry, particle size reduction is the most energy-demanding activity. Blasting represents the first stage of comminution. Experimental and field observations have demonstrated that blasting produces two main effects on rock: (i) macroscopic fracturing and fragmentation, and (ii) microscopic fracturing, consisting of a network of microfractures that weaken the rock, reduce the specific Work Index, and make the material less resistant to crushing and milling. The present work represents an initial investigation into the relationship between blast-induced microfracturing, fragment size, and mechanical resistance. Blasted rock was analyzed using three approaches: macroscopic testing via point load tests, laboratory grinding tests using a Bond ball mill to determine the blasted Work Index, and microscopic optical observation of microfractures. The results show that macroscopic testing is unable to detect microscopic weakening, as no correlation was observed between point load strength and particle size. In contrast, laboratory ball mill tests and microscopic optical observations indicate a preliminary relationship between particle size and the internal weakening of particles. These results allow the formulation of a new hypothesis: that the Work Index may not be constant within a given volume of blasted rock and could depend on the particle size distribution. Full article

Many fluid-injection sequences display burst-like seismicity with approximate power-law event-size distributions whose exponents drift between catalogs. Classical percolation models instead predict fixed, dimension-dependent exponents and do not specify which geometric mechanisms could underlie such b-value variability. We address this gap using two loopless invasion percolation variants—the constrained Leath invasion percolation (CLIP) and avalanche invasion percolation (AIP) models—to generate synthetic burst catalogs and quantify how burst geometry modifies size–frequency statistics. For each model we measure burst-size distributions and an interference fraction, defined as the proportion of attempted growth steps that terminate on previously activated bonds. Single-burst clusters recover the Fisher exponent of classical percolation, whereas multi-burst sequences show systematic, dimension-dependent drift of the effective exponent with a burst number that is strongly correlated with the interference fraction. CLIP and AIP are indistinguishable under these diagnostics, indicating that interference-driven exponent drift is a generic feature of burst growth rather than a model-specific artifact. Mapping the size-distribution exponent to an equivalent Gutenberg–Richter b-value shows that increasing interference suppresses large bursts and produces b value ranges comparable to those reported for injection-induced seismicity, supporting the interpretation of interference as a geometric proxy for mechanical inhibition that limits the growth of large events in real fracture networks. Full article

To enhance production efficiency, shale gas development often employs tighter well spacing and aggressive fracturing strategies. However, these approaches can result in well interference, where overlapping fracture networks between adjacent wells adversely affect gas production. This study introduces a comprehensive evaluation method for assessing fracture interference, with a specific focus on the role of Repeatedly Stimulated Volume (RSV). By integrating fracture network analysis with fracturing fluid migration modeling, we propose a combined static and dynamic risk assessment framework. The results demonstrate that RSV is a critical indicator of fracture interference—larger RSV values signify greater fracture overlap and intensified fluid migration between wells. Key engineering parameters influencing RSV are identified, including well spacing, fluid volume, and fracture design. Supported by real-time monitoring techniques such as microseismic events and pressure data, our dynamic assessment approach enables proactive management of interference risks. This work offers practical insights for optimizing shale gas development, allowing for improved production efficiency while mitigating interference-related drawbacks. Full article

Coalbed methane development is constrained by reservoir characteristics including high gas adsorption, high salinity, and high closure pressure, which impose significant limitations on conventional polymer fracturing fluids regarding viscosity enhancement, proppant transport, and fracture maintenance. In this study, a novel polymer fracturing fluid system, Z-H-PAM, was designed and synthesized to achieve strong salt tolerance, low adsorption affinity, and high elasticity to withstand closure pressure. This was accomplished through the molecular integration of a zwitterionic monomer ZM-1 and a hydrophobic associative monomer HM-2, forming a unified structure that combines rigid hydrated segments with a hydrophobic elastic network. The results indicate that ZM-1 provides a stable hydration layer and low adsorption tendency under high-salinity conditions, while HM-2 contributes to a high-storage-modulus, three-dimensional physically cross-linked network via reversible hydrophobic association. Their synergistic interaction enables Z-H-PAM to retain viscoelasticity that is significantly superior to conventional HPAM and to achieve rapid structural recovery in high-mineralization environments. Systematic evaluation shows that this system achieves a static sand-suspension rate exceeding 95% in simulated flowback fluid, produces broken gel residues below 90 mg/L, and results in a core damage rate of only 10.5%. Moreover, it maintains 88.8% of its fracture conductivity under 30 MPa closure pressure. Notably, Z-H-PAM can be prepared directly using high-salinity flowback water, maintaining high elasticity and sand-carrying capacity while enabling fluid recycling and reducing reservoir damage. This work clarifies the multi-scale mechanisms of strongly hydrated and highly elastic polymers in coalbed methane reservoirs, offering a theoretical and technical pathway for developing efficient and low-damage fracturing materials. Full article

This study investigates the permeability evolution in floor fault damage zones under stress–seepage–damage coupling, with a focus on water inrush risks caused by confined water upward conduction during deep mining. A stochastic fracture geometry model of the fault damage zone was developed using the discrete fracture network (DFN) model and the Monte Carlo method. Based on geological data from a mining area in Shandong, a multiphysics-coupled numerical model under mining-induced conditions was established with COMSOL Multiphysics. The simulations visually reveal the dynamic evolution of damage propagation patterns in the floor strata during working face advancement. Results indicate that the damage zone stabilizes after the working face advances to 80 m, with its morphology exhibiting strong spatial correlation to regions of high seepage velocity. Moreover, increasing confined water pressure plays a critical role in driving flow field evolution. Full article