Search Results (37)

Traditional rock mass evaluation methods have three main limitations: (1) their application depends largely on specialist judgment; (2) their discrete classification approach, such as RMR (Rock Mass Rating) and SMR (Slope Mass Rating), leads to abrupt transitions between categories; and (3) the interaction among geomechancial parameters is limited; these aspects reduce their ability to represent slope behavior in a gradual manner. The main objective of this research was to develop a model capable of representing gradual transitions between geomechanical conditions and the interaction among parameters related to susceptibility to instability. The model uses a hierarchical modular framework based on the Mamdani fuzzy inference mechanism, allowing the incorporation of expert knowledge through linguistic rules. It is implemented in a graphical environment that allows users to directly use geomechanical parameters obtained through conventional characterization or from three-dimensional digital models derived from UAV (Unmanned Aerial Vehicle) photogrammetry. Model consistency was evaluated through a sensitivity analysis, which verified the model’s response coherence across variations in input parameters. The graphical evaluation tool was then applied to three real case studies with different geomechanical configurations, and the results were compared with those from traditional methods (RMR and SMR). The results showed differences between traditional and fuzzy approaches, as our proposal links recommendations to specific geomechanical conditions across different evaluation levels, identifying conditions for potential intervention measures. In addition, the model enables the zonification of instability susceptibility, facilitating its use in future risk analyses. Our model is intended for application under normal slope conditions, without accounting for extreme events or external dynamic loads, such as seismic activity, groundwater level variations, infiltration, or high-mountain conditions. Full article

Volcanic caves, commonly referred to as lava tubes, are typically shallow subsurface cavities formed by the cooling of a generally basaltic lava flow under a roof or crust that cools faster and acts as a thermal insulator. These cavities can serve as tourist attractions, in which case their stability must be analyzed and ensured. Empirical rock mass classification systems, in this case we have applied the Q-index have been employed to evaluate the stability of underground excavations: mines and tunnels, including natural caves. We have identified that these approaches have limitations, particularly incorporating key geometric parameters such as roof thickness and cave length. In this study we have analyzed applicability of the Scaled Span Method (SSM) to volcanics caves. This method was originally developed for the stability assessment of crown pillar stability in shallow mines. We have developed a dataset of lava tubes (caves) located in the Canary Islands (Spain), the Galápagos Islands (Ecuador), and Jordan. In this research we have conducted geomechanical characterization using the Q-system, and also the Scaled Span to evaluate stability based on cave geometry and rock mass properties. The results indicate that, in general, the SSM yields more conservative stability estimates compared to the Q-system, particularly for shallow caves with limited roof thickness. Nevertheless, discrepancies between the two approaches are observed in several cases, highlighting the limitations of directly transferring empirical methods developed for mining excavations to natural cave systems. These differences underscore the need for careful interpretation and, where appropriate, complementary stability analyses. The Scaled Span Method is useful for preliminary assessment of volcanic cave stability, especially in scenarios where potential interaction with the ground surface is expected: buildings or roads on top. However, its application requires adaptation and critical evaluation due to the fundamental differences between engineered mining excavations and natural subsurface cavities. Full article

Rockburst intensity prediction is commonly formulated as a hard-label classification problem, although rockburst grades are ordered, transitional, and often ambiguous under sparse geomechanical indicators. This study integrates three publicly available datasets to construct a 761-sample heterogeneous rockburst database using three common predictors: Stress Coefficient (SC), Brittleness Coefficient (BC), and Elastic Energy Index (EEI). Diagnostic analysis shows substantial adjacent-grade overlap and source-dependent feature shifts, indicating that conventional one-hot labels and random validation may be insufficient for robust intensity assessment. To address these issues, a Source-Invariant Ordinal Label Distribution Learning (SI-OLDL) framework is proposed. The framework generates neighborhood-adaptive ordinal soft labels to represent local grade ambiguity and introduces a source-confusion branch to reduce source-specific bias during training. Under repeated stratified random validation, SI-OLDL achieved an accuracy of 0.821 and a Macro-F1 of 0.824, showing performance comparable to XGBoost, which achieved 0.819 and 0.823, respectively. Under leave-one-source-out validation, SI-OLDL showed more favorable average cross-source ordinal performance within the tested benchmark, with a Macro-F1 of 0.856 and a severe misclassification rate of 0.020. These results suggest that modeling rockburst intensity as an ordinal risk distribution is a useful representation strategy for heterogeneous small-sample rockburst databases, while independent external validation remains necessary before broader engineering deployment. Full article

The Cerchar Abrasivity Index (CAI) is essential for predicting tool wear in mechanized tunneling and mining, but direct measurement requires time-consuming laboratory procedures. We developed a data-driven framework to estimate CAI from standard geomechanical and mineralogical properties using 193 rock samples covering igneous, metamorphic, and sedimentary lithologies. After evaluating 278 feature combinations with multicollinearity constraints (VIF < 10.0), we identified an optimal four-variable subset: brittleness index B₁, density, Equivalent Quartz Content (EQC), and Uniaxial Compressive Strength (UCS), with rock type indicators. CatBoost achieved the best performance (Test R² = 0.907, RMSE = 0.420), and SHAP analysis confirmed that density and EQC are primary drivers of abrasivity. Additionally, symbolic regression derived an explicit formula using only three variables (density, EQC, B₁) without rock type classification (Test R² = 0.720). The proposed framework offers a practical approach for assessing rock abrasivity at early project stages. Full article

The stability of underground excavations is a critical factor in the safety and efficiency of mining operations, particularly in structurally complex and geomechanically variable rock mass. This study presents a comparative evaluation of empirical and numerical methods for the design of tunnel support systems in the Gilar underground mine, located in the Gedabek Contract Area of Azerbaijan. To validate and optimize the empirical Q-system-based support designs, Finite Element Method (FEM) simulations were conducted using RS2 software. These simulations enabled the modeling of stress distribution, deformation, and support–rock interaction under in situ conditions. Critical sections along the main ramp were analyzed in detail to determine safety factors during excavation and post-support installation. The study reveals that, although the Q-system provides a practical and time-efficient method for support selection, it may underestimate the reinforcement required in highly fractured or low-strength zones. Numerical modeling proved to be essential in identifying zones with low strength factors and in optimizing support configurations by adjusting rockbolt spacing and shotcrete thickness. The hybrid approach adopted in this study—empirical classification followed by numerical verification and optimization—demonstrated significant improvements in long-term tunnel stability. This research highlights the importance of integrating empirical and numerical approaches for robust ground support design in underground mining. The proposed methodology not only enhances the accuracy of support recommendations but also provides a more reliable basis for decision-making in complex geological settings. The results are particularly relevant for deep and geologically active mines requiring long-term stability of access tunnels. Full article

The development of a geomechanical model is aimed at enhancing the safety of mining operations through the determination of optimal slope angles and the probabilistic assessment of pit wall stability. For the conditions of open-pit mining, three-dimensional geomechanical models were constructed based on the calculation of the slope stability factor using the Rocscience Slide2/Slide3 (v.9.027, 2023) software package. The stress–strain state of the rock mass at the final stage of extraction was evaluated using the finite element method. Strength reduction factors (SRF) were determined considering the physico-mechanical properties of the rocks forming the near-contour zone of the massif. The stability of the pit slopes was assessed along individual geological cross-sections in accordance with the design contours of the Northern Katpar open pit. Calculations performed using several methods confirmed the overall stability of the pit walls. The final design parameters of the projected open pit were determined. For the first time, it was established that in the southern and southwestern sectors of the Northern Katpar pit, within the elevation range of +700 to +400 m, a reduction in the SFR (from 1.18 to 1.41) occurs due to the predominance of siltstones and the presence of tectonic disturbances. The generalized results of numerical slope stability analyses for the design pit contour, together with the developed geological–structural model of the deposit, provide a basis for ensuring the safe conduct of mining operations at the site. Full article

This study presents an expert system for assessing the stability of underground mine workings and automatically selecting rock bolt support schemes. The system integrates physically based calculations of roof compressive strength (Rc) and expected maximum displacement (Um) with rule-based decision logic grounded in engineering practice. Unlike empirical classifications and black-box AI models, the proposed approach ensures interpretable, reproducible, and context-aware engineering decisions. The architecture includes a numerical solver that computes Rc and Um based on excavation geometry, geomechanical properties, and mining conditions. The support scheme is selected using a knowledge base of formalized rules, while the specific support parameters are calculated within the solver. The system was validated across 51 underground excavations, with approximately 85% of its recommendations matching field-proven support solutions, and 12% suggesting reinforced schemes that could have prevented failures. The expert system is suitable for integration into digital mine management platforms and offers a foundation for developing digital twin solutions in geomechanically variable environments. Full article

This paper presents a geomechanical characterization for unwelded volcanic bimrock materials. Bimrocks are geological materials consisting of blocks of rock of different sizes embedded in a finer matrix. Many volcanic deposits and outcrops can be classified as bimrocks, and some of them correspond to unwelded bimrocks, i.e., with the absence of strong bonds between blocks of rock and matrix. The geomechanical characterization proposed is oriented towards bimrocks slopes, their stability and landslide hazard occurrence. It consists of five steps which includes the material description, the volcanic deposit classification, the definition of block size range, the computation of the volumetric block percentage, the geotechnical characterization of the blocks of rock, and the geological and geotechnical analysis of the matrix that surrounds the blocks. The geomechanical characterization proposed is applied to four slopes at the Western Cordillera of Ecuador, where slopes instabilities are common. Results show that the geomechanical characterization sets a reliable framework for geotechnically describing bimrocks materials, explaining the actual stability state of the slopes. It also enables taking appropriate and optimum decisions in the design and management of volcanic slopes, thus contributing to a sustainable approach of landslide mitigation. Full article

With the in-depth application of digital transformation in the oil industry, data-driven methods provide a new technical path for drilling engineering safety evaluation. In this paper, a data-driven drilling safety level evaluation system is proposed. By integrating the three-dimensional visualization technology of wellbore trajectory and the prediction model of friction torque, a dynamic and intelligent drilling risk evaluation framework is constructed. The Python platform is used to integrate geomechanical parameters, real-time drilling data, and historical working condition records, and the machine learning algorithm is used to train the friction torque prediction model to improve prediction accuracy. Based on the K-means clustering evaluation method, a three-tier drilling safety classification standard is established: Grade I (low risk) for friction (0–100 kN) and torque (0–10 kN·m), Grade II (medium risk) for friction (100–200 kN) and torque (10–20 kN·m), and Grade III (high risk) for friction (>200 kN) and torque (>20 kN·m). This enables intelligent quantitative evaluation of drilling difficulty. The system not only dynamically optimizes bottom-hole assembly (BHA) and drilling parameters but also continuously refines the evaluation model’s accuracy through a data backtracking mechanism. This provides a reliable theoretical foundation and technical support for risk early warning, parameter optimization, and intelligent decision-making in drilling engineering. Full article

Empirical and numerical methodologies for the geomechanical assessment of underground excavations have evolved in recent years to adapt to the geotechnical and structural conditions of natural caves, enabling stability evaluation and ensuring safe conditions for speleological exploration. This study analyzes the evolution of the state of the art of these techniques worldwide, assessing their reliability and application context, and identifying the most suitable methodologies for determining the stability of the Al-Badia lava tube. The research was conducted through bibliographic analysis and rock mass characterization using empirical geomechanical classifications. Subsequently, the numerical boundary element method (BEM) was applied to compare the obtained results and model the stress–strain behavior of the cavity. The results allowed the classification of the Al-Badia lava tube into stable, transition, and unstable zones, using empirical support charts and determining the safety factors of the surrounding rock mass. The study site highlights that empirical methods are rather conservative, and numerical results align better with observed conditions. Full article

This study presents a digital twin–based framework for assessing rockfall hazards at the immediate vicinity of the Rumkale Archaeological Site, a geologically sensitive and culturally significant location in southeastern Türkiye. Historically associated with early Christianity and strategically located along the Euphrates, Rumkale is a protected heritage site that attracts increasing numbers of visitors. Here, high-resolution photogrammetric models were generated using imagery acquired from a remotely piloted aircraft system and post-processed with ground control points to produce a spatially accurate 3D digital twin. Field-based geomechanical measurements including discontinuity orientations, joint classifications, and strength parameters were integrated with digital analyses to identify and evaluate hazardous rock blocks. Kinematic assessments conducted in the study revealed susceptibility to planar, wedge, and toppling failures. The results showed the role of lithological structure, active tectonics, and environmental factors in driving slope instability. The proposed methodology demonstrates effective use of digital twin technologies in conjunction with traditional geotechnical techniques, offering a replicable and non-invasive approach for site-scale hazard evaluation and conservation planning in heritage contexts. This work contributes to the advancement of interdisciplinary methods for geohazard-informed management of cultural landscapes. Full article

Micro-fracturing technology is a key approach to enhancing the flow capacity of oil sands reservoirs and improving Steam-Assisted Gravity Drainage (SAGD) performance, whereas heterogeneity in reservoir physical properties significantly impacts stimulation effectiveness. This study systematically investigates the coupling mechanisms of asphaltene content, clay content, and heavy oil viscosity on micro-fracturing stimulation effectiveness, based on the oil sands reservoir in Block Zhong-18 of the Fengcheng Oilfield. By establishing an extended Drucker–Prager constitutive model, Kozeny–Poiseuille permeability model, and hydro-mechanical coupling numerical simulation, this study quantitatively reveals the controlling effects of reservoir properties on key rock parameters (e.g., elastic modulus, Poisson’s ratio, and permeability), integrating experimental data with literature review. The results demonstrate that increasing clay content significantly reduces reservoir permeability and stimulated volume, whereas elevated asphaltene content inhibits stimulation efficiency by weakening rock strength. Additionally, the thermal sensitivity of heavy oil viscosity indirectly affects geomechanical responses, with low-viscosity fluids under high-temperature conditions being more conducive to effective stimulation. Based on the quantitative relationship between cumulative injection volume and stimulation parameters, a classification-based optimization model for oil sands reservoir operations was developed, predicting over 70% reduction in preheating duration. This study provides both theoretical foundations and practical guidelines for micro-fracturing parameter design in complex oil sands reservoirs. Full article

Natural caves have a great heritage and natural value, which has made them a tourist attraction that contributes positively to the diversification of tourist offerings in Spain. Volcanic caves are a particular type of natural cave, exclusive to the Canary Islands. The tourist management of these caves entails certain peculiarities that do not occur in other types of tourist establishments. The caves are exposed to certain natural hazards that are important to recognize, evaluate, and where appropriate, plan and adopt the necessary measures to guarantee the safety of visitors and workers. The main natural hazard is the structural stability of the cavity, which can affect workers and visitors. Volcanic caves present structural, lithological, and geomechanical singularities that require a specific methodology to study their stability. This study proposes a specific protocol for the early detection and management of instabilities in tourist volcanic caves, in order to help with the proper management of this ecotourism resource. To this end, tools are provided for the recognition, characterization, and geological and geomechanical analysis, classification of the types of instability in volcanic tubes, and geospatial techniques to control the structural stability. 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

An understanding of the textures (grain size, grain shape, porosity, etc.), composition (mineralogy), and distribution of constituent components of geological materials such as carbonate and siliciclastic sedimentary rocks is essential in their classification, interpretation, and significance in terms of their geomechanical strength and liquid/gas storage potential. In terms of scanning electron microscopy (SEM), this is limited to relatively flat areas of selected rough surfaces, or the analysis of polished thin sections. Here, we illustrate a new technique that can image large areas of the external surface of mini-cores (approximately 10 mm or smaller in diameter) drilled from carbonate and siliciclastic rock samples. The technique utilises a specially developed horizontal rotation stage within an SEM and allows the collection of high-resolution images that can be reconstructed into realistic surface representations of the mini-core surfaces. Elemental data (representative of mineralogy) can also be added using a combined backscattered electron and X-ray (BEX) detector. Currently, these reconstructions can be used as a useful tool for the analysis of both carbonate and siliciclastic geological materials. Further work may allow such reconstructions to aid in the improvement of resolution in micro-CT scans and the direct identification of mineral phases within such scans. Full article