Search Results (250)

The alkali–silica reaction (ASR) is an important mechanism of concrete deterioration, whereby reactive silica in aggregate interacts with cement alkalis to form expanding gel, which compromises the structural integrity of the concrete. Although the Republic of Korea has historically been classified as a low-risk region for ASR due to its geological stability, documented examples of concrete damage since the late 1990s have necessitated a rigorous reassessment of local aggregates. This study evaluated the ASR potential of 84 aggregate samples sourced from diverse Korean geological regions using standardized protocols, including ASTM C 1260 for mortar bar expansion and ASTM C 289 for chemical reactivity, supplemented by soundness, acid drainage, and weathering index analyses. The results indicate expansion within the range of 0.1–0.2%, classified as potentially deleterious, for some rock types. In addition to ASR reactivity, isolated high anomalies (e.g., high soundness, acid producing, and weathering) suggest the existence of other durability risks. Consequently, while Korean aggregates predominantly have a low ASR reactivity, the adoption of various validated ASR tests as a routine test and the integration of supplementary cementitious materials are recommended to ensure long-term concrete durability, highlighting the need for sustained monitoring and further investigation into mitigation strategies. Full article

Southwest China, with typical karst, is one of the 36 biodiversity hotspots in the world, facing extreme ecological fragility due to thin soils, limited water retention, and high bedrock exposure. This fragility intensifies under climate change and human pressures, threatening regional sustainable development. Ecological strategic areas (ESAs) are critical safeguards for ecosystem resilience, yet their spatiotemporal dynamics and driving mechanisms remain poorly quantified. To address this gap, this study constructed a multidimensional ecological health assessment framework (pattern integrity–process efficiency–function diversity). By integrating Sen’s slope, a correlated Mann–Kendall (CMK) test, the Hurst index, and fuzzy C-means clustering, we systematically evaluated ecological health trends and identified ESA differentiation patterns for 2000–2024. Orthogonal partial least squares structural equation modeling (OPLS-SEM) quantified driving factor intensities and pathways. The results revealed that ecological health improved overall but exhibited significant spatial disparity: persistently high in southern Guangdong and most of Yunnan, and persistently low in the Sichuan Basin and eastern Hubei, with 41.47% of counties showing declining/slightly declining trends. ESAs were concentrated in the southwest/southeast, whereas high-EHI ESAs increased while low-EHI ESAs declined. Additionally, the natural environmental and human interference impacts decreased, while unique geographic factors (notably the rock exposure rate, with persistently significant negative effects) increased. This long-term, multidimensional assessment provides a scientific foundation for targeted conservation and sustainable development strategies in fragile karst ecosystems. Full article

Gravity and gravity gradient data serve as fundamental inputs for geophysical resource exploration and geological structure analysis. However, traditional denoising methods—including wavelet transforms, moving averages, and low-pass filtering—exhibit signal loss and limited adaptability under complex, non-stationary noise conditions. To address these challenges, this study proposes an improved U-Net deep learning framework that integrates multi-scale feature extraction and attention mechanisms. Furthermore, a Laplace consistency constraint is introduced into the loss function to enhance denoising performance and physical interpretability. Notably, the datasets used in this study are generated by the authors, involving simulations of subsurface prism distributions with realistic density perturbations (±20% of typical rock densities) and the addition of controlled Gaussian noise (5%, 10%, 15%, and 30%) to simulate field-like conditions, ensuring the diversity and physical relevance of training samples. Experimental validation on these synthetic datasets and real field datasets demonstrates the superiority of the proposed method over conventional techniques. For noise levels of 5%, 10%, 15%, and 30% in test sets, the improved U-Net achieves Peak Signal-to-Noise Ratios (PSNR) of 59.13 dB, 52.03 dB, 48.62 dB, and 48.81 dB, respectively, outperforming wavelet transforms, moving averages, and low-pass filtering by 10–30 dB. In multi-component gravity gradient denoising, our method excels in detail preservation and noise suppression, improving Structural Similarity Index (SSIM) by 15–25%. Field data tests further confirm enhanced identification of key geological anomalies and overall data quality improvement. In summary, the improved U-Net not only delivers quantitative advancements in gravity data denoising but also provides a novel approach for high-precision geophysical data preprocessing. Full article

It is vital to stabilize pillar dams in underground reservoirs in coal mine goafs to protect groundwater resources and quarry safety, practice green mining, and protect the ecological environment. Considering the actual occurrence of coal pillar dams in underground reservoirs, acoustic emission (AE) mechanical tests were performed on dry, naturally absorbed, and soaked coal samples. According to the mechanical analysis, Quantitative analysis revealed that dry samples exhibited the highest mechanical parameters (peak strength: 12.3 ± 0.8 MPa; elastic modulus: 1.45 ± 0.12 GPa), followed by natural absorption (peak strength: 9.7 ± 0.6 MPa; elastic modulus: 1.02 ± 0.09 GPa), and soaked absorption showed the lowest values (peak strength: 7.2 ± 0.5 MPa; elastic modulus: 0.78 ± 0.07 GPa). The rate of mechanical deterioration increased by ~25% per 1% increase in moisture content. It was identified that the internal crack development presented a macrofracture surface initiating at the sample center and expanding radially outward, and gradually expanding to the edges by adopting AE seismic source localization and the K-means clustering algorithm. Soaked absorption was easier to produce shear cracks than natural absorption, and a higher water content increased the likelihood. The b-value of the AE damage evaluation index based on crack development was negatively correlated with the rock damage state, and the S-value was positively correlated, and both effectively characterized it. The research results can offer reference and guidance for the support design, monitoring, and warning of coal pillar dams in underground reservoirs. (The samples were tested under two moisture conditions: (1) ‘Soaked absorption’—samples fully saturated by immersion in water for 24 h, and (2) ‘Natural absorption’—samples equilibrated at 50% relative humidity and 25 °C for 7 days). Full article

As open-pit mining extends to greater depths, slope stability is becoming a critical factor in ensuring safe production. This issue is particularly pronounced in geological settings with weak interlayers, where sudden slope failures are more likely to occur, demanding precise and reliable stability assessment methods. In this study, a typical open-pit slope with weak interlayers was investigated. Acoustic testing and ground-penetrating radar were employed to identify rock mass structural features and delineate loose zones, enabling detailed rock mass zoning and the development of numerical simulation models for stability analysis. The results indicate that (1) the slope exhibits poor overall integrity, dominated by blocky to fragmented structures with well-developed joints and significant weak interlayers, posing a severe threat to stability; (2) in the absence of support, the slope’s dissipated energy, displacement, and plastic zone volume all exceeded the failure threshold (Δ < 0), and the safety factor was only 0.962, indicating a near-failure state; after implementing support measures, the safety factor increased to 1.31, demonstrating a significant improvement in stability; (3) prior to excavation, the energy damage index (ds) in the 1195–1240 m platform zone reached 0.82, which dropped to 0.48 after reinforcement, confirming the effectiveness of support in reducing energy damage and enhancing slope stability; (4) field monitoring data of displacement and anchor rod forces further validated the stabilizing effect of the support system, providing strong assurance for safe mine operation. By integrating cusp catastrophe theory with energy-based analysis, this study establishes a comprehensive evaluation framework for slope stability under complex geological conditions, offering substantial practical value for deep open-pit mining projects. Full article

Rainfall can easily cause local sliding and collapse of carbonaceous mudstone deep road cut slopes. In order to study the strength characteristics of carbonaceous mudstone under different water environments, large-scale horizontal push shear tests were conducted on carbonaceous mudstone rock masses in their natural state and after immersion in saturated water. The push shear force–displacement relationship curve and fracture surface shape characteristics of carbonaceous mudstone samples were analyzed, and the shear strength index of carbonaceous mudstone was obtained, and numerical simulations on the stability and support effect of carbonaceous mudstone slopes were conducted. The research results indicate that carbonaceous mudstone can exhibit good structural properties and typical strain softening characteristics under natural conditions. The fracture surface, shear strength, and shear deformation process of carbonaceous mudstone samples will undergo significant changes after being soaked in saturated water. The average cohesion decreases by 33% compared to the natural state, and the internal friction angle decreases by 15%. The numerical simulation results also fully verify the attenuation of mechanical properties of carbonaceous mudstone after immersion, as well as the effectiveness of prestressed anchor cables and frame beams in supporting carbonaceous mudstone slopes. The research results provide an effective method for understanding the shear performance of carbonaceous mudstone and practical guidance for evaluating the stability and reinforcement design of carbonaceous mudstone slopes. Full article

The Jurassic aquifer in Northwest China is the key aquifer for mine water filling, which is significant due to its loose structure, large porosity, strong rock permeability, and fracture development characteristics. In addition, the water richness in space is extremely uneven, and many coal mine roof water inrush events are closely related to it. A case of evaluation of water-richness of the roof sandstone in the 3-1 coal seam of the Bayangaole minefield was analyzed in depth, and the evaluation index system is established based on lithology and structural characteristics. Specifically, the evaluation indexes are under the influence of the influencing factors of lithology, the density of fault intersection endpoints, and the density of fault scale and the strength of folds as the influencing factors of structure. On this basis, the set pair analysis-variable fuzzy set coupling evaluation method is introduced to form a targeted water-rich evaluation model of a roof sandstone aquifer. By using the coupling method of set pair analysis and variable fuzzy set, a targeted evaluation model is formed to realize the organic integration of indicators. Through the comprehensive analysis of the relative zoning of water abundance and the data from the borehole pumping (drainage) test, the distribution of water abundance grade in the study area is clarified. Full article

Aiming to mitigate engineering risks such as tunnel face collapse and equipment jamming caused by poor geological conditions during the construction of tunnel boring machines (TBMs), this study proposes a TBM surrounding rock grade prediction method based on multi-source feature fusion. Firstly, a multi-source dataset is established by systematically integrating TBM tunnelling parameters, horizontal acoustic profile (HSP) detection data and three-dimensional geological spatial information. In the data preprocessing stage, the TBM data is cleaned and divided according to the mileage section, the statistical characteristics of key tunnelling parameters (thrust, torque, penetration, etc.) are extracted, and the rock fragmentation index (TPI, FPI, WR) is fused to construct a composite feature vector. The Direct-LiNGAM causal discovery algorithm is innovatively introduced to analyse the nonlinear correlation mechanism between multi-source features, and then a hybrid model, TRNet, which combines the local feature extraction ability of convolutional neural networks and the nonlinear approximation advantages of Kolmogorov–Arnold networks, is constructed. Verified by a real tunnel project in western Sichuan, China, the prediction accuracy of TRNet for surrounding rock grade on the test set reaches an average of 92.15%, which is higher than other data-driven methods. The results show that the prediction method proposed in this paper can effectively predict the surrounding rock grade of the tunnel face during TBM tunnelling, and provide decision support for the dynamic regulation of tunnelling parameters. Full article

The implementation of lean drilling and completion design techniques is a pivotal strategy for the petroleum and natural gas industry to achieve green, low-carbon, and intelligent transformation and innovation. These techniques significantly enhance oil and gas recovery rates. In shale gas development, the shale brittleness index plays a crucial role in evaluating fracturing ability during hydraulic fracturing. Indoor experiments on Gulong shale oil were conducted under a confining pressure of 30 MPa. Based on Rickman’s brittleness evaluation method, this study performed numerical simulations of triaxial compression tests on shale using the finite discrete element method. The fractal dimensions of the fractures formed during shale fragmentation were calculated using the box-counting method. Utilizing the obtained data, a multiple linear regression equation was established with elastic modulus and Poisson’s ratio as the primary variables, and the coefficients were normalized to propose a new brittleness evaluation method. The research findings indicate that the finite discrete element method can effectively simulate the rock fragmentation process, and the established multiple linear regression equation demonstrates high reliability. The weights reassigned for brittleness evaluation based on Rickman’s method are as follows: the coefficient for elastic modulus is 0.43, and the coefficient for Poisson’s ratio is 0.57. Furthermore, the new brittleness evaluation method exhibits a stronger correlation with the brittleness mineral index. The fractal characteristics of crack networks and the relationship between symmetry response and mechanical parameters offer a new theoretical foundation for brittle weight distribution. Additionally, the scale symmetry characteristics inherent in fractal dimensions can serve as a significant indicator for assessing complex crack morphology. Full article

The Central Depression of the Junggar Basin relies heavily on Permian lacustrine mudstone for deep-seated hydrocarbon sealing. This research investigated how the fluorescence parameters of caprock samples responded to the leakage of palaeo-oil zones based on measurements from SEM, Rock-Eval, and X-ray diffraction analysis. First, two sets of control experiments were conducted to establish the proper grain-size range of 100–140 mesh for testing caprock samples in the research area using quantitative fluorescence technology. Subsequently, based on the examination of the rock pyrolysis parameters and the fluorescence parameters against TOC values, the conjecture was formed that the quantitative fluorescence technology test results were mostly unaffected by the primary hydrocarbons. Lastly, four fluorescence parameters were used to assess seal integrity: quantitative grain fluorescence intensity of the extract (QGF E intensity, the meaning of QGF is the same in this study), QGF spectral peaks (QGF λmax), the ratio of QGF intensity to fluorescence intensity at 300 nm on the QGF spectrum (QGF index), and total scanning fluorescence spectral ratio R₁ (TSF R₁). The Permian caprock can effectively seal hydrocarbons as evidenced by the decrease of QGF E intensity and QGF index values with depth. When hydraulic fracturing causes caprock failure, it can lead to complete leakage of hydrocarbons from the palaeo-oil zones. As the depth becomes shallower, the QGF E intensity value increases, the QGF index value decreases. Due to the differences in the migration pathways of hydrocarbons in the caprock, those leaked from the Permian palaeo-oil zone into the well PD1 caprock are mainly condensate and light–normal crude oil, while the hydrocarbons from the Carboniferous palaeo-oil zone into the well MS1 caprock consist predominantly of light–normal crude oil and medium–heavy crude oil. Full article

The selection of mining cutting tools used on the cutting heads of roadheaders and shearers in hard coal mines is primarily based on the uniaxial compressive strength (UCS) of the rock. However, selecting cutting tools solely on the basis of a single parameter characterizing the rock has proven to be insufficient. Therefore, the aim of the presented study was to develop guidelines for the selection of cutting tools with appropriate protective coatings on the working parts, based not only on the mechanical strength properties of rocks, but also on their abrasivity. For the study, twelve rock samples were collected from five different Polish hard coal mines. For each rock type, the UCS (uniaxial compressive strength), BTS (Brazilian tensile strength), and chemical composition (determined using wavelength-dispersive X-ray fluorescence, WD-XRF) were measured, along with the rock abrasivity index Wz, determined using a proprietary method developed at the AGH University of Krakow. The test results were compared with the calculated specific pick wear, defined as the number of picks consumed (replaced) per 1000 m³ of excavated material. As a result, a classification of rocks based on their UCS and abrasivity was developed, along with recommendations for selecting conical picks with suitable protective coatings on the working parts. Full article

Subsurface characterisation is a fundamental aspect of the planning and design of hydroelectric projects, as it enables the assessment of the technical and geotechnical feasibility of the proposed infrastructure, ensuring its stability and functionality. This study focuses on the characterisation of rock masses from boreholes in the “Santa Rosa” and “El Rosario” areas, located in Morona Santiago, Ecuador, to determine key parameters for the design of hydroelectric projects. Field and laboratory tests were conducted, including uniaxial compression tests, indirect tensile–Brazilian tests, point load tests, tilt tests, and geomechanical classifications using the RMR and Q systems. The results show that igneous rocks, such as basalt and andesite, exhibit mechanical properties ranging from moderate to high, with uniaxial compressive strengths exceeding 120 MPa in the case of basalt, classifying it as a strong rock. In contrast, metamorphic rocks, such as chert, exhibit lower strength, with values ranging between 69.69 MPa and 90.63 MPa, classifying them as moderately strong. The RMR and Q index values indicate a variable rock mass quality, ranging from excellent in diorite and granite sectors to low in areas with significant discontinuities and alterations. Additionally, variations in basic friction angles were identified, ranging from 18° to 38°, which directly influence the stability of the proposed structures. In conclusion, this study highlights the importance of geomechanical characterisation in ensuring the technical feasibility of hydroelectric projects, providing key information for the design and development of safe and sustainable infrastructure in the region. Full article

This study investigated the technical feasibility and antimicrobial potential of incorporating ornamental rock, limestone, and ginger waste into coating mortars with the aim of developing an innovative and sustainable solution for civil construction. This study evaluated the synergistic action of these materials on the microbiological and mechanical resistance of mortar, contributing to the greater durability and efficiency of the coatings. Scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and X-ray diffraction (XRD) analyses were performed to characterize the morphology, chemical composition, and crystalline structure of the added materials, confirming their suitability for the cement matrix. Tests in the fresh state evaluated parameters such as density, consistency index, and entrained air content, demonstrating the viability of the formulations, whereas flexural and compressive strength tests indicated significant improvements in the mechanical performance of the modified mortar. Microbiological tests demonstrated a significant reduction in microbial colonization, indicating the action of ginger’s bioactive compounds, such as gingerol and shogaol, which have antimicrobial properties and are effective in inhibiting the growth of pathogenic microorganisms, as confirmed by the reduction in the bacterial colony count from 4 × 10² to 1 × 10² CFU mL⁻¹. Comparisons with conventional compositions indicate that the proposed approach outperformed traditional formulations in terms of both mechanical resistance and microbiological control. Thus, the results validate this research as a promising strategy for improving the durability and performance of coating mortars, reducing maintenance costs, and promoting the sustainable use of alternative materials in civil construction. Full article

Novel geothermal power generation systems are being developed that use supercritical CO₂ as the heat transfer medium. In this technology, some CO₂ injected into the underground reacts with surrounding water and rocks to form secondary minerals, such as carbonate minerals and clay minerals; however, the reaction mechanism in the vicinity of the injection well, the subject of this study, has not been clarified. As the first laboratory test, Rishiri Island basalt was reacted with distilled water at 250 °C for 15 days at four different CO₂ concentrations to investigate the difference in reaction depending on the CO₂ concentration. Na, K and Ca increased rapidly until 5 days of the reaction, with higher values at higher CO₂ concentrations; Mg showed characteristic behavior with higher values in the test without CO₂ (using Ar gas). The saturation index of each secondary mineral was calculated, and it was found that carbonate minerals were unsaturated and clay minerals, such as smectite, were supersaturated under all test conditions, which was in agreement with the experimental results. It is concluded that a small amount of clay minerals was formed in this test due to the low pH of the reaction solution caused by the high CO₂ concentration, indicating that dissolution was the main reaction for the rocks in the vicinity of the injection well. Full article

Accurate joint roughness coefficient (JRC) estimation is crucial for understanding rock mass mechanical behavior, yet existing predictive models show limitations in capturing complex morphological characteristics of geological surfaces. This study developed an advanced hybrid ensemble learning methodology (HELIOS-Stack) to enhance JRC prediction accuracy by integrating multiple machine learning models and statistical analysis techniques. The research implemented a hybrid ensemble approach combining random forest regression, XGBoost, LightGBM, support vector regression, multilayer perceptron models, and meta-learner using LightGBM as the final estimator. The study analyzed 112 rock samples using eight statistical parameters. Model performance was evaluated against 12 empirical regression models using comprehensive statistical metrics. HELIOS-Stack achieved exceptional accuracy with R² values of 0.9884 (training) and 0.9769 (testing), significantly outperforming traditional empirical models and alternative machine learning models. Also, the HELIOS-Stack statistical evaluation demonstrated superior performance across multiple metrics, including mean absolute error (training: 1.0165, testing: 1.4097) and concordance index (training: 0.99, testing: 0.987). The analysis identified three distinct roughness clusters: high (JRC 16–20), moderate (JRC 8–15), and smooth (JRC 0.4–7). The HELIOS-Stack methodology significantly advances rock discontinuity characterization, establishing a new benchmark for geological surface analysis. This innovative approach offers transformative applications in geotechnical engineering, rock mass stability assessment, and geological modeling through its unprecedented precision in JRC prediction. Full article