Search Results (518)

Assessing soil deformation leading to collapse is often conducted through a suction-controlled method, which can be time-intensive. In this study, the collapse deformation of compacted clay was investigated by conducting time-saving and convenient water content-controlled tests. The compacted clay specimens, each with a unique initial void ratio, were subjected to water retention experiments. The water content-controlled oedometer apparatus performed tests involving compression, wetting, and subsequent recompression. Observed experimental results indicate that water content has an inverse relationship with suction, with suction increasing as water content decreases, suggesting an inverse relationship between the two variables. In compression tests performed at a constant water content, water saturation increases and suction decreases as the void ratio decreases. Wetting leads to a decrease in void ratio as the saturation level rises, gradually declining along the wetting path until it aligns with the compression line of fully saturated soil. The compression lines at varying suction levels are established through theoretical analysis of water retention and water content-controlled compression test results. In addition, the collapse deformation is well predicted with a concise formula related to pore gas saturation. In this way, this study provides a quick and effective method for evaluating the hydro-mechanical properties of unsaturated soils. Full article

The behavior of gas emissions at the heading face of the coal mine is a key indicator of potentially harmful gas disaster risk, necessitating in-depth study via analytical and statistical methods. However, conventional prediction and evaluation methods depend on long-interval statistical data, which are too coarse for and lack the immediacy required for real-time applications. Based on the physical laws of gas storage and flow, a refined computational model has been developed to compute dynamic gas emission rates that vary with geology and excavating process. Furthermore, by comparing the computed outputs with actual monitoring data, it becomes possible to assess whether abnormal gas emissions are occurring. Methodologically, this model first applies the finite difference method to compute the dynamic gas flux and the dynamic residual gas content. It then determines the exposure duration of each segment of the roadway wall at any given moment, as well as the mass of newly dislodged coal. The total gas emission rate at a specific sensor location is obtained by aggregating the contributions from all of the exposed wall and the freshly dislodged coal. Owing to some simplifications, the model’s applicability is currently restricted to single, medium-thick coal seams. The model was preliminarily implemented in Python (3.13.2) and validated against a case study of an active heading face. The results demonstrate a strong concordance between model predictions and field measurements. The model notably captures the significant variance in emission rates resulting from different mining activities, the characteristic emission surges from dislodged coal and newly exposed coal walls, and the influence of sensor placement on monitoring outcomes. Full article

The gas desorption behavior of coal is a key basis for guiding gas parameter determination, optimizing gas extraction, and preventing gas-related disasters. Coal in mine working faces typically exhibits a mixed particle size distribution. However, research on the gas desorption behavior of mixed-size coal samples and comparative studies with single-sized samples remains insufficient. This study employed a self-developed experimental system for the multi-field coupled seepage desorption of gas-bearing coal to conduct comparative experiments on gas desorption behavior between single-sized and mixed-size coal samples. Systematic analysis revealed significant differences in their desorption and diffusion patterns: smaller particle sizes and higher proportions of small particles correlate with greater total gas desorption amounts and higher desorption rates. The desorption process exhibits distinct stages: the initial desorption amount is primarily influenced by the particle size, while the later stage is affected by the proportion of coal samples with different particle sizes. The desorption intensity for both single-sized and mixed-size samples decays exponentially over time, with the decay rate weakening as the proportion of small particles decreases. The gas diffusion coefficient decays over time during desorption, eventually approaching zero, and increases as the proportion of small particles rises. Conversely, the gas desorption attenuation coefficient increases with a higher proportion of fine particles. Based on the desorption laws of coal samples with single and mixed particle sizes, this study can be applied to coalbed gas content measurements, emission prediction, and extraction design, thereby providing a theoretical foundation and technical support for coal mine operations. Full article

Using wheat flour milling (WFM) co-products in pig diets may reduce feed cost. Still, energy digestibility is lower for WFM co-products than for feed grains. Inadequate information exists about their fermentation characteristics and the relationship between digestible energy (DE) value and chemical characteristics or in vitro energy digestibility. The objectives were to (1) determine the chemical characteristics, in vitro and in vivo DE values, and energy digestibility of WFM co-products in growing pigs; (2) determine their in vitro microbial fermentation characteristics, and (3) establish relationships between in vivo DE value of WFM co-products and their chemical composition, fermentation characteristics, or in vitro digestibility values. Across Canada, 94 WFM co-products were sampled and characterized for their chemical composition and in vitro dry matter (DM) and energy digestibility using pepsin, pancreatin, and a multi-enzyme complex containing arabinase, β-glucanase, hemicellulase, xylanase, and cellulase. The in vivo energy, DM digestibility and DE value of 9 WFM co-products (2 shorts, 5 millrun, 1 middling, and 1 bran) were determined using a corn-based diet and 40 growing pigs in two periods to obtain 8 observations per diet. After in vitro digestion, the 9 WFM co-product samples were subjected to microbial fermentation using fresh fecal inoculum in a cumulative gas-production technique. The WFM co-products had a high content of crude fiber (up to 7.9% in shorts, 9.9% in millrun, 7.1% in middlings, and 12.0% in bran) and crude protein (CP; up to 27.8% in shorts, 20.0% in millrun, 22.1% in middlings, 15.9% in bran). The DE values ranged from 2.84 to 3.74 Mcal/kg DM among WFM co-products. Among chemical characteristics, neutral detergent fiber was the best predictor (R² = 0.81) for in vivo DE value, followed by crude fiber (R² = 0.78), and acid detergent fiber (R² = 0.72). The in vitro DE values predicted (R² = 0.80) in vivo DE values of 9 WFM co-products. Based on principal component analysis, total gas and short-chain fatty acid production varied among WFM co-products and was associated with the CP content of WFM co-products. In conclusion, WFM co-products contain high crude protein and have a high DE value for growing pigs but vary substantially in nutritional value. Full article

This work presents an integrated experimental and machine learning study on the fire performance of sisal fiber-reinforced polyester composites treated with magnesium hydroxide as a flame retardant. A total of 43 small-scale fire resistance tests were conducted in a custom-built gas-fired furnace following ISO 834 and NCh935/2 standards. Key parameters—including fiber content, flame retardant proportion, catalyst, and accelerator—were correlated with burn time and mass loss. Linear regression revealed negligible to weak correlations, while nonlinear models (Random Forest, Support Vector Regression, and Deep Neural Network) showed improved predictive capacity. The Deep Neural Network achieved the best performance (MSE = 0.061, R² = 0.334). Experimental results confirm that magnesium hydroxide substantially increases burn time, whereas sisal fiber content alone has a minimal effect on fire resistance. This study highlights an affordable strategy for enhancing the fire safety of bio-based composites and demonstrates the potential of machine learning to optimize material formulations. Future research should expand the dataset and validate the models through standardized large-scale fire tests. However, the findings are limited to small-scale fire resistance tests under controlled laboratory conditions and should not be generalized to full-scale structural applications without further validation. Full article

In the construction sector, cement and concrete are among the most widely utilized manufactured materials, yet their environmental impact remains a significant concern. The concrete industry is a major contributor to carbon dioxide emissions, accounting for over 8% of global greenhouse gas emissions annually. Several reports have estimated that between 1930 and 2013, a total of 4.5 gigatons of carbon was sequestered through the carbonation of cement-based materials. This process offset approximately 43% of the carbon dioxide (CO₂) emissions resulting from cement production during the same period, excluding emissions related to fossil fuel consumption in the manufacturing process. It is well established that producing one ton of cement results in approximately 0.60–0.98 tons of CO₂ emissions, coupled with substantial energy consumption. To mitigate these environmental effects, developing low-carbon or cement-free binders has become crucial. Alkali-activated binders (AABs), derived from industrial by-products or agricultural waste materials and activated with a low-molarity or one-part activator, are increasingly recommended as sustainable alternatives to reduce greenhouse gas emissions in the cement industry and minimize the consumption of natural resources. The production of alkali-activated concrete (AAC) involves several critical factors that significantly influence its mix design, fresh properties, and compressive strength (CS) performance. This study aims to provide a comprehensive review of the key factors affecting AAC’s mix design, workability, and CS characteristics. Firstly, the study discusses various methods employed for AAC mix design and the factors influencing these designs. Secondly, it examines the impact of binder type, source, chemical, mineralogical, and physical properties, as well as alkaline activator solutions, water content, and fillers on AAC’s workability, setting times, and strength development. Additionally, the study explores the correlation matrix and predictive performance models for fresh and strength properties. Lastly, the relationship between workability and CS is extensively analyzed. The review concludes by highlighting the existing challenges and prospects of AACs as sustainable construction materials to replace traditional cement and reduce carbon emissions. Full article

Onboard the MetOp satellite series, Infrared Atmospheric Sounding Interferometer (IASI) is a Fourier Transform spectrometer based on the Michelson interferometer. IASI acquires interferograms, which are processed to provide high-resolution atmospheric emission spectra. These spectra enable the derivation of temperature and humidity profiles, among other parameters, with exceptional spectral resolution. In this study, we evaluate a novel, rapid retrieval approach in the interferogram domain, aiming for near-real-time (NRT) analysis of large spectral datasets anticipated from next-generation tropospheric sounders, such as MTG-IRS. The Partially Sampled Interferogram (PSI) method, applied to trace gas retrievals from IASI, has been sparsely explored. However, previous studies suggest its potential for high-accuracy retrievals of specific gases, including CO, CO₂, CH₄, and N₂O at the resolution of a single IASI footprint. This article presents the results of a study based on retrieval in the interferogram domain. Furthermore, the optical pathway differences sensitive to the parameters of interest are studied. Interferograms are generated using a fast Fourier transform on synthetic IASI spectra. Finally, the relationship to the total column of carbon monoxide is explored using three different algorithms—from the most intuitive to a complex neural network approach. These algorithms serve as a proof of concept for interferogram classification and rapid predictions of surface temperature, as well as the abundances of H₂O and CO. IASI spectra simulations were performed using the LATMOS Atmospheric Retrieval Algorithm (LARA), a robust and validated radiative transfer model based on least squares estimation. The climatological library TIGR was employed to generate IASI interferograms from LARA spectra. TIGR includes 2311 atmospheric scenarios, each characterized by temperature, water vapor, and ozone concentration profiles across a pressure grid from the surface to the top of the atmosphere. Our study focuses on CO, a critical trace gas for understanding air quality and climate forcing, which displays a characteristic absorption pattern in the 2050–2350 ${\mathrm{cm}}^{-1}$ wavenumber range. Additionally, the study explores the potential of correlating interferogram characteristics with surface temperature and H₂O content, aiming to enhance the accuracy of CO column retrievals. Starting with intuitive retrieval algorithms, we progressively increased complexity, culminating in a neural network-based algorithm. The results of the NN study demonstrate the feasibility of fast interferogram-domain retrievals, paving the way for operational applications. Full article

This study addresses gas drainage challenges in the Pingdingshan NO.10 mine JI_15-16 coal seam through coupled COMSOL-FLAC^3D numerical simulations. The research evaluates the effectiveness of a cross-measure borehole drainage system. It analyzes the failure mechanisms of the surrounding rock in both the machine roadway and floor roadway of the 24130 working face under the influence of boreholes. The results demonstrate that extended drainage duration progressively reduces both gas content and pressure within the borehole-affected zone of the coal seam while enhancing the effective permeability of the JI_15-16 coal stratum. The operational system extracted 1,527,357 m³ of methane, achieving a pre-drainage efficiency of 59.18% through cross-measure boreholes. The measured gas content aligns with simulated predictions, though field-recorded gas pressure registered slightly higher than modeled values. This validated drainage design complies with the Pingmei Group’s regulations for coal and gas outburst prevention. Critically, cross-measure boreholes alter stress distribution around both coal and floor roadways, promoting plastic zone expansion. Consequently, during the development of the 24130 working face’s machine roadway, intensified ground pressure monitoring is essential near borehole locations in the roof, floor, and rib strata. Supplementary support reinforcement should be implemented when required to prevent rib spalling and roof collapse incidents. Full article

This study presents the first integrated, basin-scale analysis of helium distribution and its geological controls within the Ordos Basin, one of China’s most prospective cratonic gas provinces. Through comprehensive sampling and experimental analysis of the helium content in natural gas, combined with high-resolution gravity and magnetic data processed using the normalized vertical derivative of the total horizontal derivative (NVDR-THDR) method, we reveal significant spatial heterogeneity in helium enrichment. The results show that helium concentrations are generally higher along the basin margins and structurally complex zones, while central areas are relatively depleted. Helium primarily originates from the radioactive decay of uranium (U) and thorium (Th) within metamorphic and magmatic basement rocks. Fault systems act as efficient vertical migration pathways, enabling deep-sourced helium to accumulate in structurally and stratigraphically favorable traps. This study proposes a new enrichment mode, “basement-sourced helium generation, fault-mediated migration, and caprock-controlled preservation”, which highlights the synergistic roles of basement lithology, deep-seated faults, and sealing capacity in controlling helium distribution. This model is supported by the observed alignment of high helium concentrations with zones of strong basement magnetism and major fault intersections. These findings advance our understanding of helium accumulation mechanisms in stable cratonic settings and provide a predictive framework for helium exploration in similar geological contexts worldwide. Full article

To promote the large-scale utilization of construction and industrial solid waste in engineering, this study focuses on developing accurate prediction and optimization methods for the unconfined compressive strength (UCS) of composite recycled mortar. Innovatively incorporating three types of recycled powder (RP)—recycled clay brick powder (RCBS), recycled concrete powder (RCBP), and recycled gypsum powder (RCGP)—we systematically investigated the effects of RP type, replacement rate, and curing period on mortar UCS. The core objective and novelty lie in establishing and comparing three artificial intelligence models for high-precision UCS prediction. Furthermore, leveraging GA-BP’s functional extremum optimization theory, we determined the optimal UCS alongside its corresponding mix proportion and curing scheme, with experimental validation of the solution reliability. Key findings include the following: (1) Increasing total RP content significantly reduces mortar UCS; the maximum UCS is achieved with a 1:1 blend ratio of RCBP:RCGP, while a 20% RCBS replacement rate and extended curing periods markedly enhance strength. (2) Among the prediction models, GA-BP demonstrates superior performance, significantly outperforming BP models with both single and double hidden layer. (3) The functional extremum optimization results exhibit high consistency with experimental validation, showing a relative error below 10%, confirming the method’s effectiveness and engineering applicability. Full article

With the rapid increase in global lithium demand, the exploration of newly discovered lithium in the bauxite of the Wenshan area in southeastern Yunnan has become increasingly important. However, the current research on clay-type lithium in the Wenshan area has primarily focused on local exploration, and large-scale predictive metallogenic studies remain limited. To address this, this study utilized multi-source remote sensing data from ZY1-02D and ASTER, combined with ALOS 12.5 m DEM and Sentinel-2 imagery, to carry out remote sensing mineral identification, structural interpretation, and prospectivity mapping for clay-type lithium in the Wenshan area. This study indicates that clay-type lithium in the Wenshan area is controlled by NW, EW, and NE linear structures and are mainly distributed in the region from north of the Wenshan–Malipo fault to south of the Guangnan–Funing fault. High-value areas of iron-rich silicates and iron–magnesium minerals revealed by ASTER data indicate lithium enrichment, while montmorillonite and cookeite identification by ZY1-02D have strong indicative significance for lithium. Field verification samples show the highest Li₂O content reaching 11,150 μg/g, with six samples meeting the comprehensive utilization criteria for lithium in bauxite (Li₂O ≥ 500 μg/g) and also showing an enrichment of rare earth elements (REEs) and gallium (Ga). By integrating stratigraphic, structural, mineral identification, geochemical characteristics, and field verification data, ten mineral exploration target areas were delineated. This study validates the effectiveness of remote sensing technology in the exploration of clay-type lithium and provides an applicable workflow for similar environments worldwide. Full article

The envelopes of Red Supergiants (RSGs) have a unique chemical environment not seen in other types of stars. They foster an oxygen-rich synthesis but are tempered by sporadic and chaotic mass loss, which distorts the envelope and creates complex outflow sub-structures consisting of knots, clumps, and arcs. Near the stellar photosphere, molecules and grains form under approximate LTE conditions, as predicted by chemical models. However, the complicated outflows appear to have distinct chemistries generated by shocks and dust destruction. Various RSG envelopes have been probed for their molecular content, mostly by radio and millimeter observations; however, VY Canis Majoris (VY CMa) and NML Cygni (NML Cyg) display the highest chemical complexity, and also the most complicated envelope structure. Thus far, over 29 different molecules have been identified in the envelopes of RSGs. Some molecules are common for circumstellar gas, including CO, SiO, HCN and H₂O, which have abundances of ∼10⁻⁶–10⁻⁴, relative to H₂. More exotic oxides have additionally been discovered, such as AlO, AlOH, PO, TiO₂, and VO, with abundances of ∼10⁻⁹–10⁻⁷. RSG shells support intricate maser emission in OH, H₂O and SiO, as well. Studies of isotope ratios in molecules suggest dredge-up at least into the H-burning shell, but further exploration is needed. Full article

Calorific value and moisture content are the key indices to evaluate Caragana pellet fuel’s quality and combustion characteristics. Calorific value is the key index to measure the energy released by energy plants during combustion, which determines energy utilization efficiency. But at present, the determination of solid fuel is still carried out in the laboratory by oxygen bomb calorimetry. This has seriously hindered the ability of large-scale, rapid detection of fuel particles in industrial production lines. In response to this technical challenge, this study proposes using hyperspectral imaging technology combined with various chemometric methods to establish quantitative models for determining moisture content and calorific value in Caragana korshinskii fuel. A hyperspectral imaging system was used to capture the spectral data in the 935–1720 nm range of 152 samples from multiple regions in Inner Mongolia Autonomous Region. For water content and calorific value, three quantitative detection models, partial least squares regression (PLSR), random forest regression (RFR), and extreme learning machine (ELM), respectively, were established, and Monte Carlo cross-validation (MCCV) was chosen to remove outliers from the raw spectral data to improve the model accuracy. Four preprocessing methods were used to preprocess the spectral data, with standard normal variate (SNV) preprocessing performing best on the quantitative moisture content detection model and Savitzky–Golay (SG) preprocessing performing best on the calorific value detection method. Meanwhile, to improve the prediction accuracy of the model to reduce the redundant wavelength data, we chose four feature extraction methods, competitive adaptive reweighted sampling (CARS), successive pojections algorithm (SPA), genetic algorithm (GA), iteratively retains informative variables (IRIV), and combined the three models to build a quantitative detection model for the characteristic wavelengths of moisture content and calorific value of Caragana korshinskii fuel. Finally, a comprehensive comparison of the modeling effectiveness of all methods was carried out, and the SNV-IRIV-PLSR modeling combination was the best for water content prediction, with its prediction set determination coefficient $\left(R_P^2\right)$, root mean square error of prediction (RMSEP), and relative percentage deviation (RPD) of 0.9693, 0.2358, and 5.6792, respectively. At the same time, the moisture content distribution map of Caragana fuel particles is established by using this model. The SG-CARS-RFR modeling combination was the best for calorific value prediction, with its $R_P^2$, RMSEP, and RPD of 0.8037, 0.3219, and 2.2864, respectively. This study provides an innovative technical solution for Caragana fuel particles’ value and quality assessment. Full article

The gas-production potential of shale gas is a comprehensive evaluation metric that assesses the reservoir quality, gas-content properties, and gas-production capacity. Currently, the evaluation of gas-production potential is generally conducted through qualitative comparisons of relevant parameters, which can lead to multiple solutions and make it difficult to establish a comprehensive evaluation index. This paper introduces a gas-production potential evaluation method based on the Analytic Hierarchy Process (AHP). It uses judgment matrices to analyze key parameters such as gas content, brittleness index, total organic carbon content, the length of high-quality gas-layer horizontal sections, porosity, gas saturation, formation pressure, and formation density. By integrating fuzzy mathematics, a mathematical model for gas-production potential is established, and corresponding gas-production levels are defined. The model categorizes gas-production potential into four levels: when the gas-production index exceeds 0.65, it is classified as a super-high-production well; when the gas-production index is between 0.45 and 0.65, it is classified as a high-production well; when the gas-production index is between 0.35 and 0.45, it is classified as a medium-production well; and when the gas-production index is below 0.35, it is classified as a low-production well. Field applications have shown that this model can accurately predict the gas-production potential of shale gas wells, showing a strong correlation with the unobstructed flow rate of gas wells, and demonstrating broad applicability. Full article

The strength parameters of fault gouge are critical factors that influence sealing capacity and fault reactivation in underground gas storage reservoirs. This study investigates the shear characteristics of remolded fault gouge under varying hydro-mechanical conditions, focusing on the coupled influence of water content and cementation. Sixty fault gouge samples are prepared using a mineral mixture of quartz, montmorillonite, and kaolinite, with five levels of water content (10–30%) and three cementation degrees (0%, 1%, 3%). Direct shear tests are conducted under four normal stress levels (100–400 kPa), and microstructural characteristics are examined using SEM. The results show that shear strength and cohesion exhibit a non-monotonic trend with water content, increasing initially and then decreasing, while the internal friction angle decreases continuously. Higher cementation degrees not only enhance shear strength and reduce the softening effect caused by water but also shift the failure mode from ductile sliding to brittle, cliff-type rupture. Moreover, clay content is found to modulate the degree—but not the trend—of strength parameter responses to water and cementation variations. Based on the observed mechanical behavior, a semi-empirical shear strength prediction model is developed by extending the classical Mohr–Coulomb criterion with water–cementation coupling terms. The model accurately predicts cohesion and internal friction angle as functions of water content and cementation degree, achieving strong agreement with experimental results (R² = 0.8309 for training and R² = 0.8172 for testing). These findings provide a practical and interpretable framework for predicting the mechanical response of fault gouge under complex geological conditions. Full article