Search Results (679)

Chinese coalbed methane (CBM) reservoirs exhibit characteristically low recovery rates due to adsorbed gas dominance and “three-low” properties (low permeability, low pressure, and low saturation). CO₂ thermal drive (CTD) technology addresses this challenge by leveraging dual mechanisms—thermal desorption and displacement to enhance production; however, its effectiveness necessitates uniform fracture networks for temperature field homogeneity—a requirement unmet by conventional long-fracture fracturing. To bridge this gap, a coupled seepage–heat–stress–fracture model was developed, and the temperature field evolution during CTD in coal under non-uniform fracture networks was determined. Integrating multi-cluster fracture propagation with stress barrier and intra-stage stress differential characteristics, a stress-barrier-responsive diverting fracturing technology meeting CTD requirements was established. Results demonstrate that high in situ stress and significant stress differentials induce asymmetric fracture propagation, generating detrimental CO₂ channeling pathways and localized temperature cold islands that drastically reduce CTD efficiency. Further examination of multi-cluster fracture dynamics identifies stress shadow effects and intra-stage stress differentials as primary controlling factors. To overcome these constraints, an innovative fracture network uniformity control technique is proposed, leveraging synergistic interactions between diverting parameters and stress barriers through precise particle size gradation (16–18 mm targeting toe obstruction versus 19–21 mm sealing heel), optimized pumping displacements modulation (6 m³/min enhancing heel efficiency contrasted with 10 m³/min improving toe coverage), and calibrated diverting concentrations (34.6–46.2% ensuring uniform cluster intake). This methodology incorporates dynamic intra-stage adjustments where large-particle/low-rate combinations suppress toe flow in heel-dominant high-stress zones, small-particle/high-rate approaches control heel migration in toe-dominant high-stress zones, and elevated concentrations (57.7–69.2%) activate mid-cluster fractures in central high-stress zones—collectively establishing a tailored framework that facilitates precise flow regulation, enhances thermal conformance, and achieves dual thermal conduction and adsorption displacement objectives for CTD applications. Full article

To facilitate the high-value utilization of reclaimed asphalt pavement (RAP), this study investigated the efficacy of fine separation technology as a pre-treatment method. This technology significantly reduced the variability of RAP, controlling the coefficients of variation for asphalt content and aggregate gradation within 5% and 10%, respectively, and minimized false particle content (agglomerates of fines and aged asphalt). Response Surface Methodology (RSM) was employed to optimize the mix design for ultra-high-RAP- content mixtures (50–70%). A predictive regression model was developed to determine the Optimal Binder Content (OBC) based on RAP and rejuvenator dosage. The road performance of the resulting mixtures was comprehensively evaluated. Results showed that the technology markedly enhanced the overall performance of recycled asphalt mixtures. While high-temperature rutting resistance improved with increasing RAP content, low-temperature performance declined. The mixture with 70% RAP failed to meet low-temperature cracking requirements. Consequently, an optimal RAP content of 60% is recommended. Furthermore, the generalized sigmoidal model effectively constructed dynamic modulus master curves, accurately predicting the viscoelastic behavior of these ultra-high-RAP mixtures. This study demonstrates that fine separation is a critical pre-processing step for reliably producing high-quality, sustainable asphalt mixtures with RAP content far exceeding conventional limits. Full article

Local scour around bridge piers is one of the primary causes of structural failure in bridges. Therefore, this study focuses on addressing the estimation of maximum scour depth (d_sm), which is essential for safe and resilient bridge design. Many studies in the last eight decades have included metadata collection and developed around 80 empirical formulas using various scour-affecting parameters of different ranges. To date, a total of 33 formulas have been comparatively analyzed and ranked based on their predictive accuracy. In this study, novel formulas using semi-empirical methods and gene expression programming (GEP) have been developed alongside an artificial neural network (ANN) model to accurately estimate d_sm using 768 observed data points collected from published work, along with eight newly conducted experimental data points in the laboratory. These new formulas/models are systematically compared with 74 empirical literature formulas for their predictive capability. The influential parameters for predicting d_sm are flow intensity, flow shallowness, sediment gradation, sediment coarseness, time, constriction ratio, and Froude number. Performances of the formulas are compared using different statistical metrics such as the coefficient of determination, Nash–Sutcliffe efficiency, mean bias error, and root-mean-squared error. The Gauss–Newton method is employed to solve the nonlinear least-squares problem to develop the semi-empirical formula that outperforms the literature formulas, except the formula from GEP, in terms of statistical performance metrics. However, the feed-forward ANN model outperformed the semi-empirical model during testing and validation phases, respectively, with higher CD (0.790 vs. 0.756), NSE (0.783 vs. 0.750), lower RMSE (0.289 vs. 0.301), and greater prediction accuracy (64.655% vs. 61.935%), providing approximately 15–18% greater accuracy with minimal errors and narrower uncertainty bands. Using user-friendly tools and a strong semi-empirical model, which requires no coding skills, can assist designers and engineers in making accurate predictions in practical bridge design and safety planning. Full article

Ecological concrete designed by empirical method does not consider the mesoscopic influence of aggregates, resulting in problems such as low strength, excessive porosity, and poor stability with different gradations, which severely restricts the development and application of ecological concrete. To achieve the refined design of ecological concrete, a mesoscopic specific surface area design method based on statistical analysis is proposed. First, the meso-aggregate model with sub-millimeter precision was established using a high-precision 3D scanner, and CloudCompare was used to calculate the specific surface area of the mesoscopic aggregate model, laying the foundation for the statistical analysis of specific surface area. Second, statistical analysis methods verified that the mean specific surface area of 20 aggregates from a single random sampling reliably estimates the mean of the overall aggregate population. Third, the optimal water–cement ratio was calculated considering the water absorption characteristics and the mortar-wrapping capacity of aggregates; standard cubic specimens were prepared using this optimal water–cement ratio, with aggregates evenly coated with mortar and no obvious mortar settlement. Fourth, the cubic compressive strength of specimens naturally cured for 7 days was tested; experimental results showed that the cubic compressive strength of specimens formed by this project’s design method increased by more than 30% compared to the empirical design method. The results indicate that using the average volume-specific surface area of 20 aggregates to assess the overall average volume-specific surface area of aggregates is both reliable and relatively efficient. Based on the reliable estimation of the overall average volume-specific surface area of aggregates derived from this method, measurements were taken of the thickness of water films adsorbed on dry aggregates and the thickness of mortar coatings on surface-dry aggregates. Further, the optimal water–cement ratio for eco-concrete was deduced, and a comprehensive set of feasible refined methods for eco-concrete mix proportion design was proposed. In contrast to the empirical method, concrete designed via the subject’s methodology exhibits a marked enhancement in compressive strength while retaining favorable pore characteristics—rendering it well-suited for deployment in the slope protection of reservoirs and ponds and thereby facilitating the realization of ecological slope protection functionality. Full article

The temperature rise in mass concrete structures, caused by the exothermic process of cement hydration and concurrent heat exchange with the environment, results in thermal gradients between the core and outer layers of the structure. These gradients generate tensile stresses that may exceed the early age tensile strength of concrete, leading to cracking. Therefore, reliable prediction of the temperature rise and associated thermal gradients is essential for assessing the risk of early age thermal cracking. Traditional methods for predicting temperature development rely on numerical simulations and simplified analytical approaches, which are often time-consuming and impractical for rapid engineering assessments. This paper proposes a machine learning-based (ML) approach to predict temperature rise and thermal gradients in mass concrete. The dataset was generated using the analytical CIRIA C766 method, enabling systematic selection and gradation of key factors, which is nearly impossible using measurements collected from full-scale structures and is essential for identifying an effective ML model. Three regression models, linear regression, decision tree, and XGBoost were trained and evaluated on simulated datasets that included concrete mix parameters and environmental conditions. Among these, the XGBoost model achieved the highest accuracy in predicting the maximum temperature rise and the temperature differential between the core and surface of the analysed element. The results confirm the suitability of ML models for reliable thermal response prediction. Furthermore, ML models can provide a usable alternative to conventional methods, offering both tools to thermal control strategies and insight into the influence of input factors on temperature in early age mass concrete. Full article

In off-road scenes, segmentation targets exhibit significant scale progression due to perspective depth effects from oblique viewing angles, meaning that the size of the same target undergoes continuous, boundary-less progressive changes along a specific direction. This asymmetric variation disrupts the geometric symmetry of targets, causing traditional segmentation networks to face three key challenges: (1) inefficientcapture of continuous-scale features, where pyramid structures and multi-scale kernels struggle to balance computational efficiency with sufficient coverage of progressive scales; (2) degraded intra-class feature consistency, where local scale differences within targets induce semantic ambiguity; and (3) loss of high-frequency boundary information, where feature sampling operations exacerbate the blurring of progressive boundaries. To address these issues, this paper proposes the ProCo-NET framework for systematic optimization. Firstly, a Progressive Strip Convolution Group (PSCG) is designed to construct multi-level receptive field expansion through orthogonally oriented strip convolution cascading (employing symmetric processing in horizontal/vertical directions) integrated with self-attention mechanisms, enhancing perception capability for asymmetric continuous-scale variations. Secondly, an Offset-Frequency Cooperative Module (OFCM) is developed wherein a learnable offset generator dynamically adjusts sampling point distributions to enhance intra-class consistency, while a dual-channel frequency domain filter performs adaptive high-pass filtering to sharpen target boundaries. These components synergistically solve feature consistency degradation and boundary ambiguity under asymmetric changes. Experiments show that this framework significantly improves the segmentation accuracy and boundary clarity of multi-scale targets in off-road scene segmentation tasks: it achieves 71.22% MIoU on the standard RUGD dataset (0.84% higher than the existing optimal method) and 83.05% MIoU on the Freiburg_Forest dataset. Among them, the segmentation accuracy of key obstacle categories is significantly improved to 52.04% (2.7% higher than the sub-optimal model). This framework effectively compensates for the impact of asymmetric deformation through a symmetric computing mechanism. Full article

Rapid expansion of global transportation infrastructure leads to the accumulation of vast quantities of reclaimed asphalt pavement (RAP). Recycling RAP is essential for reducing environmental impacts. However, current recycling practices typically limit RAP content to below 30%. Increasing RAP content in asphalt mixtures—especially beyond 50%—offers a more sustainable approach, but also introduces challenges in performance, design, and implementation. This study presents a case in which the hot central plant recycling asphalt mixture containing 50% RAP, enhanced with an active rejuvenating agent, was used for the lower layer of the Jianghe Expressway. The aggregate gradation was designed using the Marshall method, with the optimal asphalt–aggregate ratio determined to be 3%. The average compaction degree was 98.9%, the infiltration coefficient ranged from 6.43 mL/min to 23.77 mL/min, and the standard deviation of flatness did not exceed 1.0. However, material shoving was observed during paving, suggesting that, with appropriate adjustments, the technique can be optimized for large-scale implementation. The gradation of the RAP material showed minimal deviation from the design gradation, remaining within ±5%. The compaction scheme with eight roller passes and a loose paving coefficient of 1.35 yielded superior compaction performance. Full article

This study addresses engineering challenges associated with sandy residual deposits in the coastal zone of Quanzhou, China, characterized by high void ratios (e > 0.8), low cohesion (c < 10 kPa), and strong liquefaction tendencies induced by marine dynamic forces. Focusing on the beach sands of Shenhu Bay and Qingshan Bay, 123 in situ dynamic penetration tests and 12 laboratory physical–mechanical tests (including water content, particle gradation, relative density, and triaxial shear strength) were conducted. The correlations between the physical and mechanical properties of these coastal sandy soils and their foundation bearing capacity were systematically analyzed. Results reveal that the sands, predominantly medium-to-fine grains with 8–15% biogenic debris, are generally in a loose-to-medium dense state (relative density ~34%), with negligible cohesion. Shear strength depends primarily on the internal friction angle (28.89–37.43°). Correlation analyses show that water content (17.8–31.92%) and particle gradation parameters (uniformity coefficient C_u and curvature coefficient C_c) significantly influence bearing capacity, with bearing capacity increasing by 12.15% per 14.12% rise in water content and 35% per 0.518 increase in C_c. An improved foundation bearing capacity model based on the Prandtl–Reissner theory is proposed by integrating particle gradation and water content, tailored for beach foundations in Quanzhou. Model validation demonstrates an average error of approximately 15%, outperforming traditional models. These findings provide valuable theoretical support for assessing foundation stability in building construction projects in Quanzhou and similar coastal regions. Full article

Based on mechanical experiments conducted on bulk raw coal and coal of different types in order to explore the correlations between the fractal dimension and the grain size gradation and strength parameters of coal samples, the fractal statistics method was used to statistically analyze the grain size distribution characteristics of tectonic soft coal, while fractal theory was applied to study the grain size fractal characteristics of tectonic soft coals of categories III–V. The results of this study show that coal types III–V have increasing fractal dimension numbers, and the content of coarse particles decreases with an increasing fractal dimension number. Within this sampling range, the Class V coal is better graded, and the fractal dimension number decreases as the distance of the sampling point from the fault zone increases. In the direct shear experiments, the internal friction angle of the bulk raw coal decreased linearly with an increasing fractal dimension number, and the regularity of the cohesive force and the fractal dimension number was not strong, but the adhesion cohesion of the types of coal exhibited a positive exponential relationship with the fractal dimension, and the relationship between the internal friction angle and the fractal dimension was not strong. There was a positive exponential relationship, and the internal friction angle was relatively stable. The uniaxial compressive strength of the types of coal exhibited a good correlation with the coefficient of firmness of the coal samples and the fractal dimension, and the coefficient of firmness of the coal samples was the main factor influencing the uniaxial compressive strength of the types of coal compared with the particle size gradation. Full article

Seepage erosion around sheet pile walls represents a critical failure mechanism in geotechnical engineering, yet the underlying mechanisms governing the onset of erosion remain poorly understood. This study presents a comprehensive multi-scale investigation employing a coupled computational fluid dynamics (CFD)-discrete element method (DEM) to elucidate the onset mechanisms of seepage erosion in gap-graded soils with varying the fines content under different hydraulic gradients. The results demonstrate that increasing the fines content enhances the overall erosion resistance, as evidenced by reduced particle mobilization and eroded mass ratio. Particle tracking analysis reveals that the fines content fundamentally influences the spatial distribution of the erosion. Specimens with low fines content exhibit distributed erosion throughout the domain, while specimens with higher fines content show concentrated erosion around the sheet pile wall and downstream regions. Micromechanical analysis of local contact fabric and contact forces indicates that this spatial heterogeneity stems from the mechanical coordination number and mechanical redundancy, characterized by the reduced magnitudes of these parameters for the region with lower erosion resistance. These findings establish that the fines content governs both global erosion resistance and spatial erosion patterns, providing essential insights for optimizing soil gradation design and advancing fundamental understanding of seepage erosion mechanisms. Full article

With the continuous depletion of natural river sand resources and the escalating ecological degradation caused by excessive sand mining, manufactured sand has emerged as a sustainable and environmentally favorable alternative aggregate, playing an increasingly important role in the advancement of green construction materials. Nevertheless, the shrinkage behavior of manufactured sand concrete (MSC) exhibits significant deviations from that of conventional natural sand concrete due to differences in the material characteristics. Existing shrinkage prediction models—such as ACI 209, CEB-FIP 2010, B3, and GL 2000—fail to adequately incorporate the specific properties and substitution effects of manufactured sand, thereby limiting their predictive accuracy and applicability. To bridge this gap, the present study conducted a systematic evaluation of the four aforementioned classical shrinkage prediction models based on experimental data from MSC specimens incorporating varying replacement rates of manufactured sand. The findings revealed that models such as B3 and CEB-FIP 2010 neglected the influence of critical characteristics of manufactured sand—namely, particle morphology, gradation, and stone powder content—on the cementitious matrix and interfacial transition zone, which led to substantial prediction discrepancies. Accordingly, a nonlinear regression-based correction function was developed, introducing the manufactured sand content as a key influencing variable to recalibrate and enhance the ACI 209 and GL 2000 models for a more accurate application to MSC. The modified models exhibited markedly improved fitting performance and predictive robustness across the full range of manufactured sand replacement ratios (0–100%), thereby offering a more reliable framework for modeling the shrinkage development of MSC. Full article

Fully graded concrete exhibits significantly enhanced mechanical properties due to optimized aggregate gradation. However, the effects of coarse aggregate morphological characteristics at the microscale—such as axial ratio (Ar) and angularity coefficient (Ac)—on fracture behavior remain insufficiently understood and require further investigation. This study investigates the fracture behavior of fully graded concrete using a peridynamic approach. First, a multilinear constitutive model of concrete considering nonlocal effects and damage evolution is constructed. Second, aggregate modeling via the Tensile-Concave Synthesis Method in peridynamics, and different property bonds and random bond parameters are introduced to characterize the heterogeneity and initial defects of concrete. Finally, the effects of loading rate, aggregate randomness, and the parameters Ar and Ac on the uniaxial tensile properties of concrete are systematically studied. The results demonstrate that peridynamics accurately captures the entire process of crack initiation and propagation in fully graded concrete. Uniaxial tensile behavior exhibits strong rate dependence. Different random aggregate models result in variations in the peak tensile strength of concrete. With the increase in Ar, the peak tensile stress gradually decreased by 4.15%, whereas the elastic modulus increased by 14.31%. As Ac increased, the peak stress exhibited an overall trend of first increasing by 5.14%, followed by a decrease of 3.8%. Therefore, in numerical simulations, the influences of loading rate and aggregate randomness should not be overlooked. Moreover, to enhance the strength of fully graded concrete, the proportion of aggregates with large Ar and Ac values should be minimized. Full article

In recent years, in situ blasting–leaching, in the stope has emerged as an economically viable and environmentally sustainable mining technique for low-grade ore deposits. While the leaching efficiency is influenced by factors such as ore type, solution composition, and spraying speed, the most significant factor is the effect of post-blasting crushed-stone particle size and gradation on the pore structure, which subsequently influences seepage and leaching performance. To investigate how particle size and gradation affect the pore structure of granular media, physical models of ore particles with varying sizes and gradations were constructed. These models were scanned and three-dimensionally reconstructed using CT scanning technology and Avizo software (Avizo, Version 2023.1; Thermo Fisher Scientific: Waltham, MA, USA, 2023) enabling quantitative analysis of pore structure parameters. The results indicate that the coefficient of uniformity (Cu) is approximately negatively correlated with porosity, while the vertical absolute permeability (kz) follows an attenuated exponential trend. When the fine-particle content (L8 > L3 > L1) increases by 1.5-fold and 9-fold, the number of pore throats increases by 8.71% and 30.91%, respectively, the average pore size decreases by 75.1% and 64.4%, the average throat size decreases by 66.3% and 60%, and the connectivity rate decreases by 92% and 77.8%. This study further evaluates permeability based on the aforementioned pore structure parameters. Multiple regression analysis reveals that the connectivity rate and throat size have the most significant influence on permeability. Accordingly, permeability analysis and prediction are conducted using the improved Purcell formula, which demonstrates a strong correlation with the experimentally measured results. Full article

Remote sensing targets typically exhibit characteristics of gradual scale changes and diverse orientations. Most existing remote sensing detectors adapt to these differences by adding multi-level structures for feature fusion. However, this approach leads to incomplete coverage of the overall target by the extracted local features, resulting in the loss of critical directional information and an increase in computational complexity which affect the detector’s performance. To address this issue, this paper proposes a Rotation Target Detection Network based on Multi-kernel Interaction and Hierarchical Expansion (MIHE-Net) as a systematic solution. Specifically, we first refine scale modeling through the Multi-kernel Context Interaction (MCI) module and Hierarchical Expansion Attention (HEA) mechanism, achieving sufficient extraction of local features and global information for targets of different scales. Additionally, the Midpoint Offset Loss Function is employed to mitigate the impact of gradual scale changes on target direction perception, enabling precise regression for targets across various scales. We conducted comparative experiments on three commonly used remote sensing target datasets (DOTA, HRSC2016, and UCAS-AOD), with mean average precision (mAP) as the core evaluation metric. The mAP values of the method in this paper on the three datasets reached 81.72%, 92.43%, and 91.86% respectively, which were 0.65%, 1.93%, and 1.87% higher than those of the optimal method, significantly outperforming existing one-stage and two-stage detectors. Through multi-scale feature interaction and direction-aware optimization, MIHE-Net effectively addresses the challenges posed by scale gradation and direction diversity in remote sensing target detection, providing an efficient and feasible solution for high-precision remote sensing target detection. Full article

Rockfill particle gradation significantly influences mechanical performance in earth–rockfill dam construction, yet on-site screening is often time-consuming, labor-intensive, and structurally invasive. This study proposes a rapid and non-destructive detection method using mobile-based photography and an end-to-end image segmentation approach. An enhanced YOLOv8-seg model with an integrated dual-attention mechanism was pre-trained on laboratory images to accurately segment densely stacked particles. Transfer learning was then employed to retrain the model using a limited number of on-site images, achieving high segmentation accuracy. The proposed model attains a mAP50 of 97.8% (base dataset) and 96.1% (on-site dataset), enabling precise segmentation of adhered and overlapped particles with various sizes. A Minimum Area Rectangle algorithm was introduced to compute the gradation, closely matching the results from manual screening. This method significantly contributes to the automation of construction workflows, cutting labor costs, minimizing structural disruption, and ensuring reliable measurement quality in earth–rockfill dam projects. Full article