Search Results (82)

The spatial variability in rock mass mechanical parameters critically affects slope stability assessments. This study investigated the southern slope of the Bayan Obo open-pit mine. A representative elementary volume (REV) with a side length of 14 m was determined through discrete fracture network (DFN) simulations. Based on the rock quality designation (RQD) data from 40 boreholes, a three-dimensional spatial distribution model of the RQD was constructed using Ordinary Kriging interpolation. The RQD values were converted into geological strength index (GSI) values through an empirical correlation, and the generalized Hoek–Brown criterion was applied to develop a spatially heterogeneous equivalent mechanical parameter field. Numerical simulations were performed using FLAC3D, with the slope stability evaluated using the point safety factor (PSF) method. For comparison, three homogeneous benchmark models based on the 5th, 25th, and 50th percentiles produced profile-scale safety factors of 0.96–1.92 and failed to replicate the observed failure geometry. By contrast, the heterogeneous model yielded safety factors of approximately 1.03–1.08 and accurately reproduced the mapped sliding surface. These findings demonstrate that incorporating spatial heterogeneity significantly improves the accuracy of slope stability assessments, providing a robust theoretical basis for targeted monitoring and reinforcement design. Full article

This paper provides research that shows that the scale and quantification of the degree of fracturing in a rock mass should and can be considered when estimating geological strength index (GSI) ratings for rock mass strength and deformability estimates. In support of this notion, a brief review is provided to demonstrate why it is imperative that scale is considered when using GSI in engineering design. The impact of scale and scale effects on the engineering response of a rock mass typically requires a definition of fracture intensity relative to the volume or size of rock mass under consideration and the relative scale of the project being built. In this research three volume scales are considered: the volume of a structural domain, a representative elemental REV, and unit volume. A theoretical framework is established that links these three volume scales together, how they are estimated, and how they relate to parameters used to estimate engineering behaviour. Analysis of data from several examples and case histories for real rock masses is presented that compares and validates the use of a new and innovative but practical method (a sphere of unit volume) to estimate fracture intensity parameters VFC or P30 (fractures/m³) and P32 (fracture area—m²/m³) that is included on the vertical axis of the volumetric V-GSI chart. The research demonstrates that the unit volume approach to calculating VFC and P32 used in the V-GSI system compares well with other methods of estimating these two parameters (e.g., discrete fracture network (DFN) modelling). The research also demonstrates the reliability of the VFC-correlated rating scale included on the vertical axis of the V-GSI chart for use in estimating first-order strength and deformability estimates for rock masses. This quantification does not negate or detract from geological logic implicit in the original graphical GSI chart. Full article

The efficiency of coalbed methane (CBM) extraction is closely related to the drilling response of coal seams, which is significantly influenced by natural fracture development of coal seams. This work investigated 11 coal samples from the Baode, Xinyuan, and Huolinhe mines, employing quantitative fracture characterization, acoustic wave testing, drilling experiments, and cuttings analysis to systematically reveal the relationships and mechanisms between fracture parameters and coal drilling response characteristics. The result found that acoustic parameters (average wave velocity v and drilling surface wave velocity v₀) exhibit significant negative correlations with fracture line density (ρ₁) and area ratio (ρ₂) (|r| > 0.7), while the geological strength index (GSI) positively correlates with acoustic parameters, confirming their utility as indirect indicators of fracture development. Fracture area ratio (ρ₂) strongly correlates with drilling cuttings rate q (r = 0.82), whereas GSI negatively correlates with drilling rate w, indicating that highly fractured coal is more friable but structural stability constrains drilling efficiency, while fracture parameters show limited influence on drill cuttings quantity Q. Cuttings characteristics vary with fracture types and density. Type I coal (low-density coexisting exogenous fractures and cleats) produces cuttings dominated by fine particles with concentrated size distribution (average particle size d ≈ 0.52 mm, crushability index n = 0.46–0.61). Type II coal (exogenous-fracture-dominant) exhibits coarser particle sizes in cuttings (d ≈ 0.8 mm, n = 0.43–0.53). Type III coal (dense-cleat-dominant) drill cuttings are mainly coarse particles and are concentrated in distribution (d ≈ 1.53 mm, n = 0.72–0.98). Additionally, drilling response differences are governed by the coupling effects of vitrinite reflectance (R_o), density, and firmness coefficient (f), with Huolinhe coal being easier to drill due to its lower R_o, f, and density. This study elucidates the mechanism by which fracture development affects coal drilling response through multi-parameter correlation analysis, while also providing novel insights into the optimization of fracturing sweet spot selection for CBM development. Full article

Landslide prevention is crucial, particularly for protecting roads and infrastructure in rock landslide-prone areas. This global issue has garnered significant attention from researchers worldwide. This study addresses landslide prevention by modeling the factor of safety (FoS) for slope stability through the Geological Strength Index (GSI), limit equilibrium method (LEM), and finite element method (FEM). A GSI analysis was conducted using RocLab software version 1.0, and slope modeling was performed using RocScience SLIDE version 6.0 and RS2 version 11. The results revealed various cohesion and friction angles across six slopes, with Slope 5 exhibiting the highest FoS values (up to 3.27 with the FEM) and Slope 1 exhibiting the lowest (1.59 with the FEM). All slopes, designed with a uniform geometry, remained stable, exhibiting FoS values greater than 1.1. This study further provides an optimal slope design for the open pit in the andesite mining plan at Anugerah Berkah Sejahtera. These findings highlight the important role of accurate modeling in the assessment of slope stability. With a suggested safe slope height of 10 m and an angle of 80° (FoS = 1.62), slope stability analysis based on the factor of safety (FoS) showed that single slopes made of andesite maintain stability at steep angles. Claystone slopes, however, have a maximum slope height of 30 m at 20° (FoS = 1.27) and 27 m at 50° (FoS = 1.34), requiring more conservative geometries to maintain their stability. For an overall slope that comprises both rock types, a height of 30 m with a slope angle of 60° is recommended (FoS = 1.23) to ensure stability. The critical design condition for a claystone slope occurs at a height of 30 m with a slope angle of 50°, yielding a factor of safety (FoS) of 0.92, which indicates instability (FoS < 1.1). Similarly, a 35 m-high slope with a slope angle of 20° produced an FoS of 1.04, and a 35 m-high slope with a slope angle of 50° produced an FoS of 0.89, further confirming instability. For the overall slope configuration, instability occurs at a height of 30 m with a slope angle of 65° that produces an FoS of 1.09. Full article

The disturbance factor (D) in the Hoek–Brown criterion quantifies excavation-induced rock mass disturbance. Although D is conceptually defined as a continuous parameter ranging from 0 to 1, the most recent Hoek–Brown guidelines provide descriptions only for boundary conditions related to slopes and tunnels. In slope excavations, the degree of disturbance is governed not only by the excavation method but also by the thickness of the removed overburden, with its influence becoming particularly significant in deep excavations. In recent years, the concept of a transitional disturbance factor, varying with depth from the excavation surface, has gained increasing attention. To address this need, the EXCASS system, an empirical method for selecting appropriate excavation techniques based on the Geological Strength Index (GSI) and point load strength (I_s50) values, was integrated into the transitional disturbance factor framework in this study. EXCASS allows for the selection of stronger or weaker excavation methods, offering flexibility to control the degree of disturbance induced in the rock mass. Moreover, the disturbance factor at the excavation surface was determined by incorporating both the operational excavation power index and the thickness of the removed overburden. This integrated approach enables a more realistic evaluation of excavation-induced damage in slope stability analyses. Full article

This study was conducted to characterize and classify soils and rocks and to produce an engineering geological map that is beneficial for overall urban planning. The soils’ moisture content and specific gravity values range from 23.47% to 44.21% and 2.68 to 2.81, respectively. The activity of soils varies from 0.34 to 0.78 (inactive to normal). The shrinkage limit and shrinkage index values of soils range from 5% to 11.43% and 14.29% to 26.9%, respectively. Free swell value varies from 5 to 23% (low expansive). The unconfined compressive strength of soils ranges from 215.8 to 333.5 kPa (very stiff). According to USCS (Unified Soil Classification System), soils are classified into lean clay, lean clay with sand, fat clay with sand, and clayey silt with slight plasticity. According to BSCS (British Soil Classification SystemS), soils are classified into clay of intermediate plasticity, clay of high plasticity, and silt of intermediate plasticity. Rocks were classified into four categories based on their mass strength: very low mass strength, low mass strength, medium mass strength, and high mass strength. The RQD Rock Quality Designatione) value ranges from 47.48% to 98.25%, indicating a quality range from poor to excellent. The RMR Rock Mass Ratinge) values range from 44 to 90%, indicating that the rocks of the study area fall into three major classes: Class I (very good), Class II (good), and Class III (fair). Full article

Subsea tunnels cross complex geological structures, such as weathered troughs with fractured rock masses and high permeability, and are prone to water and mud inrush. To minimize the risk of subsea tunnelling, a novel method consisting of a multi-index evaluation system and a computational model using the interval number TOPSIS method was established. The multi-index evaluation system was formed by eight evaluation indices that can potentially affect water and mud inrush: sea depth, subsea tunnel burial depth, scale of weathering trough, interface angle, strength of surrounding rock, permeability of weathering trough, cyclical footage, and grouting reinforced region. The risk levels of water and mud inrush were divided into four grades. Considering the uncertainty of the evaluation indices, an evaluation vector of interval numbers was adopted. The triangular fuzzy number membership function was used to determine the membership degree, and the 1–9 scales method was used to construct the judgment matrices, which can obtain the weight of evaluation indices. Furthermore, the weight values of the evaluation indices combined with the membership degree were used to obtain the result vector, which can be analyzed using the interval number TOPSIS method. This novel assessment method was applied to the FWK15+350 of the Haicang tunnel successfully. The risk level fell into IV, which represents a high-risk section. The results showed a high degree of congruence with the prevailing circumstances, thereby validating the credibility of the proposed methodology. Full article

Concrete materials exposed to sulfate-rich geological environments are prone to structural durability degradation due to chemical erosion. Silane-based protective materials can enhance the durability of concrete structures under harsh environmental conditions. This study investigates the evolution of acoustic emission (AE) precursor characteristics in silane-protected, sulfate-eroded concrete specimens during uniaxial compression failure. Unlike existing research focused primarily on protective material properties, this work establishes a novel framework linking “silane treatment–AE parameters–failure precursor identification”, thereby bridging the research gap in damage evolution analysis of sulfate-eroded concrete under silane protection. Uniaxial compressive strength tests and AE monitoring were conducted on both silane-protected and unprotected sulfate-eroded concrete specimens. A diagnostic system integrating dynamic analysis of the acoustic emission b-value, mutation detection of energy concentration index ρ, and multifractal detrended fluctuation analysis (MF-DFA) was developed. The results demonstrate that silane-protected specimens exhibited a distinct b-value escalation followed by an abrupt decline prior to peak load, whereas unprotected specimens showed disordered fluctuations. The mutation point of energy concentration ρ for silane-protected specimens occurred at 0.83 σc, representing a 9.2% threshold elevation compared to 0.76 σc for unprotected specimens, confirming delayed damage accumulation in protected specimens. MF-DFA revealed narrowing spectrum width ($∆\alpha$) in unprotected specimens, indicating reduced heterogeneity in AE signals, while protected specimens maintained significant multifractal divergence. $∆f\left(\alpha \right)$ peak localization revealed that weak AE signals dominated during early loading stages in both groups, with crack evolution primarily involving sliding and friction. During the mid-late elastic phase, crack propagation became the predominant failure mode. Experimental evidence confirms the engineering significance of silane protection in extending service life of concrete structures in sulfate environments. The proposed multi-parameter AE diagnostic methodology provides quantitative criteria for the safety monitoring of protected concrete structures in sulfate-rich conditions. Full article

Loess is a distinctly structured soil. Undisturbed loess is prone to geological hazards, such as liquefaction and landslides under dynamic loads. There are also problems such as the inhomogeneity, anisotropy, and disturbance of in situ sampling. An artificial structural loess is prepared to accurately display the dynamic characteristics of undisturbed loess. This study took artificial structural loess as the study object, through dynamic triaxial tests, analyzed the effects of the confining pressure (σ₃), dry density (ρ_d), and cement content (D) on its dynamic strength. Then, a dynamic strength index model of artificial structural loess was established. Our results show that the dynamic strength of artificial structural loess rises with enhanced σ₃, ρ_d, and D. The dynamic cohesion (c_d) and dynamic friction angle (φ_d) increased with the rise of ρ_d, and D. The dynamic strength of artificial structured loess is closer to that of undisturbed loess when the ρ_d is 1.60 g/cm³ and D is 2%. The R² values of the φ_d and the c_d model were 0.97 and 0.98, respectively, fitting the dynamic strength index of artificial structural loess with different D, ρ_d, and σ₃. Our study outcomes can serve as references and guides for engineering construction in loess areas. Full article

This study presents an application of Boruta-SHapley Additive ExPlanations (Boruta-SHAP) for geotechnical characterization using Measure-While-Drilling (MWD) data, enabling a more interpretable and statistically rigorous assessment of feature importance. Measure-While-Drilling data collected at the scale of an open-pit mine was used to characterize geotechnical properties using regression-based machine learning models. In contrast to previous studies using MWD data to recognize rock type using Principal Component Analysis (PCA), which only identifies the directions of maximum variance, the Boruta-SHAP method quantifies the individual contribution of each Measure-While-Drilling variable. This method ensures interpretable and reliable geotechnical characterization as well as robust feature selection by comparing predictors against randomized ‘shadow’ features. The Boruta-SHAP analysis revealed that bit air pressure and torque-to-penetration ratio were the most significant predictors of rock strength, contradicting previous assumptions that rate of penetration was the dominant factor. Moreover, feature importance was conducted for fracture frequency and Geological Strength Index (GSI), a rock mass classification system. A comparative analysis of prediction performance was also performed using a range of different machine learning algorithms that resulted in strong coefficient of determinations of actual field or laboratory results versus predicted values. The results are plausible, confirming that MWD data could provide a high-resolution description of geotechnical conditions prior to mining, leading to a more confident prediction of subsurface geotechnical properties. Therefore, the fragmentation from blasting as well as downstream operational phases, such as digging, hauling, and crushing, could be improved effectively. Full article

Used for soil and weathered rocks, soil nails are rigid reinforcements positioned at certain angles on the ground to provide slope stability. A rigid reinforcement element placed in a well filled with cement grout mix after completing drilling will generate adherence stress between the grout-mixed nail bar and soil. Due to this stress, load is transferred to the soil along the soil–grout interaction surface. In the case discussed herein, the slope at the parcel border needed to be made steeper in order to accommodate the construction of a facility in the Taşkısığı region of Sakarya province. Soil-nailed walls, which are inexpensive and suitable for weathered rocks, were needed as a support system because the slope was too steep to support itself. Support system performance was measured using two inclinometers and two soil nail pull-out tests conducted on different sections observed during and after construction. Contrary to the design-phase prediction, it was determined that the stresses started to dampen in the region closer to the slope-facing zone. Field measurement data and numerical analysis revealed that higher parameters than necessary were selected. In this context, sensitivity and parameter analyses were carried out using the Hoek–Brown constitutive model. The GSI value was re-evaluated and found to be compatible with the observation results obtained from the field performance. Since the retaining wall performance observed was higher than expected, geometric parametric analysis of the structural elements was performed; high safety coefficients were found across variations. The effects of the inclination of the slope, nail length, nail spacing, and nail slope design parameters on the safety coefficient and horizontal displacement were examined. The optimal design suggested nail lengths of 4.00 m, a spacing of 1.60 m, and slopes of 20°. It was discovered that the effect of the inclination degree of the slope on the safety coefficient was lower than expected. The results revealed that a more economical design with a similar safety factor can be obtained by shortening the lengths of the nails. Full article

Bench-scale geotechnical characterization often suffers from high uncertainty, reducing confidence in geotechnical analysis on account of expensive resource development drilling and mapping. The Measure-While-Drilling (MWD) system uses sensors to collect the drilling data from open-pit blast hole drill rigs. Historically, the focus of MWD studies was on penetration rates to identify rock formations during drilling. This study explores the effectiveness of Artificial Intelligence (AI) classification models using MWD data to predict geotechnical categories, including stratigraphic unit, rock/soil strength, rock type, Geological Strength Index, and weathering properties. Feature importance algorithms, Minimum Redundancy Maximum Relevance and ReliefF, identified all MWD responses as influential, leading to their inclusion in Machine Learning (ML) models. ML algorithms tested included Decision Trees, Support Vector Machines (SVMs), Naive Bayes, Random Forests (RFs), K-Nearest Neighbors (KNNs), Linear Discriminant Analysis. KNN, SVMs, and RFs achieved up to 97% accuracy, outperforming other models. Prediction performance varied with class distribution, with balanced datasets showing wider accuracy ranges and skewed datasets achieving higher accuracies. The findings demonstrate a robust framework for applying AI to real-time orebody characterization, offering valuable insights for geotechnical engineers and geologists in improving orebody prediction and analysis Full article

The accurate analysis of rock cores is of primary importance for designing in and on the rock mass environment. There are several methods for analyzing boreholes, but the most accepted and widely used method is the rock quality designation (RQD) value, which has been a core rating metric for six decades. The RQD value serves as: (1) an important input parameter for rock mass classifications such as RMR and Q; (2) a basis for calculating the Geological Strength Index (GSI) of boreholes; and (3) a key indicator in assessing rock mass quality, particularly in highly fractured or weak rock masses. The original RQD method has several drawbacks and shortcomings, which have led to numerous proposed amendments. This review paper aims to: (1) summarize alternative methods of calculating the RQD value; (2) analyze the sensitivity of different rock mass classifications to the accuracy of this value; and (3) present a systematic analysis of the practical implications of modified RQD methods, emphasizing advancements such as DFN modeling, seismic RQD techniques, and machine learning-based approaches. The findings provide a comprehensive framework for more robust and versatile assessments of rock mass quality. Full article

Identifying suitable sites for urban, industrial, and tourist development is important, especially in areas with increasing population and limited land availability. Kharga Oasis, Egypt, stands out as a promising area for such development, which can help reduce overcrowding in the Nile Valley and Delta. However, soil and various environmental factors can affect the suitability of civil engineering projects. This study used Geographic Information Systems (GISs) and a multi-criteria decision-making approach to assess the suitability of Kharga Oasis for construction activities. Geotechnical parameters were obtained from seismic velocity data, including Poisson’s ratio, stress ratio, concentration index, material index, N-value, and foundation-bearing capacity. A comprehensive analysis of in situ and laboratory-based geological and geotechnical data from 24 boreholes examined soil plasticity, water content, unconfined compressive strength, and consolidation parameters. By integrating geotechnical, geomorphological, geological, environmental, and field data, a detailed site suitability map was created using the analytic hierarchy process to develop a weighted GIS model that accounts for the numerous elements influencing civil project design and construction. The results highlight suitable sites within the study area, with high and very high suitability classes covering 56.87% of the land, moderate areas representing 27.61%, and unsuitable areas covering 15.53%. It should be noted that many settlements exist in highly vulnerable areas, emphasizing the importance of this study. This model identifies areas vulnerable to geotechnical and geoenvironmental hazards, allowing for early decision-making at the beginning of the planning process and reducing the waste of effort. The applied model does not only highlight suitable sites in the Kharga Oasis, Egypt, but, additionally, it provides a reproducible method for efficiently assessing land use suitability in other regions with similar geological and environmental conditions around the world. Full article

The rockburst hazard is a primary geological disaster endangering the environment in underground engineering. Due to the complexity of the rockburst mechanism, traditional methods are insufficient to predict the rockburst hazard objectively, especially when dealing with an imbalanced dataset. To address this issue, the hybrid models of PSO-BPNN-AdaBoost and PSO-BPNN-XGBoost were developed to predict rockburst hazards in this study. First, a rockburst dataset with 266 cases was constructed, containing six indicators: the maximum tangential stress, uniaxial compressive strength, uniaxial tensile strength, elastic deformation energy index, tangential stress index, and brittleness coefficient of strength. Then, the original dataset was oversampled using the synthetic minority oversampling technique (SMOTE) for dataset balancing. Subsequently, the PSO-BPNN-AdaBoost and PSO-BPNN-XGBoost models were constructed and evaluated to have the best accuracies of 0.901 and 0.851, respectively. Finally, the developed models were applied to predict the rockburst hazard in the Daxaingling Tunnel, the Cangling Tunnel, and the Zhongnanshan Tunnel shaft. The results indicate that the obtained rockburst hazard levels are consistent with engineering records, and the developed PSO-BPNN-AdaBoost and PSO-BPNN-XGBoost models are reliable for rockburst prediction. Full article