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The geological condition monitoring and identification based on TBM-equipped sensors is of great significance for efficient and safe tunnel construction. Full-scale rotary cutting experiments are carried out using tunnel-boring machine disc cutters. Thrust, torque and vibration sensors are equipped on the rotary cutting machine (RCM). A stacking ensemble-learning model for real-time prediction of rock mass classification using features of mathematical statistics is proposed. Three signals, thrust, torque and a novel vibration spectrogram-based local amplification feature, are fed into the model and trained separately. The results show that the stacked ensemble-learning model has better accuracy and stability than any single model, showing a good application prospect in the rock mass classification. [ABSTRACT FROM AUTHOR]

Rock mass classification (RMC) systems in the realm of rock slope engineering have gathered a lot of attention. The present review article delves into the major RMC systems, elucidating their fundamental principles, key parameters and practical applications. Among the various RMC systems for rock slope designing, Romana's Slope mass rating (SMR) was found to be the most comprehensive; therefore, the focus of the discussion centres around the SMR method. The article provides a crisp overview of the major advancements, evolution and potential challenges of SMR. The radical concept of wedge failure in SMR and the use of continuous functions are discussed in detail. A thorough discussion is provided on the efforts made by different researchers, encompassing the inclusion of novel factors such as slope height, heterogeneity in rock mass or lithology, weighted consideration of existing discontinuities, fuzzy sets and overburden thickness. Various automated calculation techniques and empirical correlations of SMR with other classification systems and rock engineering parameters are also outlined. Moreover, a critical examination of the variations among major extensions of SMR and their geotechnical relevance have also been discussed. Based on a meticulous assessment of the susceptibility to toppling failure in SMR, two sub‐factors to include block shape and interlayer slip between discontinuities have been suggested. These sub‐factors were validated by Goodman's tests for toppling failure. The scope of future projections or possible aspects for revisiting and refinement of the SMR method are suggested to enhance the applicability of the method under a diverse initial set of geotechnical conditions. [ABSTRACT FROM AUTHOR]

In this study, by conducting the in situ seismic tests on limestone rock mass, the P-wave velocity and the maximum vibration of particles and anisotropy index were determined in different nine directions. Then, the rock mass was classified according DRMR classification system and the correlation between the DRMR with P- wave velocity and peak particle velocity was investigated and new experimental relationships with high correlation were presented. The seismic source is the impact caused by a weight falling. Recording the first arrival time of seismic waves and peak particle velocity in the rock mass was done by a three-component geophone. The recorded data were analyzed using the statistical method of least squares, and linear relationships with the maximum correlation coefficient between the quality of the anisotropic rock mass, P-wave velocity and peak particle velocity were obtained. The results showed that the joint sets cause anisotropy in the rock mass quality, P-wave velocity and the peak particle velocity. The rose and radar diagrams showed that the rock mass quality, P-wave velocity and peak particle velocity are higher in the direction parallel with the strike of joint sets and lower in bisector direction of the angle between the strikes of joint sets. [ABSTRACT FROM AUTHOR]

Swift and precise classification of rock masses significantly enhances the prudent and efficient advancement of rock mass engineering. To expeditiously and accurately determine the engineering classification of slope rock masses, this paper introduces a hybrid Data Comprehensive Weight Analysis (DCWA)-Equilibrium Optimizer (EO)-AdaBoost ensemble prediction model. This model is formulated to prognosticate revised Basic Quality value ([BQ]) and the engineering classification of slope rock masses, utilizing the Basic Quality (BQ) classification method. Six elements have been chosen to formulate the prognostic index, and a dataset comprising information on the slope rock mass of 266 groups has been assembled for the purpose of training and evaluating the predictive accuracy of the established model. Two slope sections in DEZIWA open pit mine is chosen to validate the correctness of established DCWA-EO-AdaBoost model in the field through numerical simulation. In comparison to AdaBoost, DCWA-AdaBoost, DCWA-EO-AdaBoost, K-Nearest Neighbor (KNN), Back Propagation (BP), Decision Tree (DT), and Random Forest (RF) models, it is discerned that the DCWA-EO-AdaBoost hybrid model exhibits elevated coefficient of determination (R2, 0.986), variance accounted for (VA, 98.64%), prediction accuracy (A, 92.31%), and kappa coefficient (KC, 88.87%). Conversely, the mean absolute error (E, 5.97%) and root mean square error (ER, 38.37) are diminished, affirming its reliability and superiority. Field validation reveals that, combine the DCWA-EO-AdaBoost model and BQ classification method, mechanical parameters used for slope stability analysis can be obtained accurately, and can obtains slope stability analysis results basically consistent with the engineering practice through numerical simulation, which can prove the correctness of the established prediction model and clarify the use of the established model in the process of slope stability analysis. This attests that the established predictive model holds substantial merit, offering a noteworthy reference for the expeditious and precise acquisition of [BQ] and the classification of slope rock mass based on the BQ method, and can be adopted in the process of slope stability analysis. [ABSTRACT FROM AUTHOR]

Accurate rock mass quality classification is crucial for the design and construction of underground projects. Traditional methods often rely on expert experience, introducing subjectivity, and struggle with complex geological conditions. Machine learning algorithms have improved this issue, but obtaining complete rock mass quality datasets is often difficult due to high cost and complex procedures. This study proposed a hybrid XGBoost model for predicting rock mass quality using incomplete datasets. The zebra optimization algorithm (ZOA) and Bayesian optimization (BO) were used to optimize the hyperparameters of the model. Data from various regions and types of underground engineering projects were utilized. Adaptive synthetic (ADASYN) oversampling addressed class imbalance. The model was evaluated using metrics including accuracy, Kappa, precision, recall, and F1-score. The ZOA-XGBoost model achieved an accuracy of 0.923 on the test set, demonstrating the best overall performance. Feature importance analysis and individual conditional expectation (ICE) plots highlighted the roles of RQD and UCS in predicting rock mass quality. The model's robustness with incomplete data was verified by comparing its performance with other machine learning models on a dataset with missing values. The ZOA-XGBoost model outperformed other models, proving its reliability and effectiveness. This study provides an efficient and objective method for rock mass quality classification, offering significant value for engineering applications. [ABSTRACT FROM AUTHOR]

Abstract The accurate prediction of uneven rock mass classes is crucial for intelligent operation in tunnel-boring machine (TBM) tunneling. However, the classification of rock masses presents significant challenges due to the variability and complexity of geological conditions. To address these challenges, this study introduces an innovative predictive model combining the improved EWOA (IEWOA) and the light gradient boosting machine (LightGBM). The proposed IEWOA algorithm incorporates a novel parameter l for more effective position updates during the exploration stage and utilizes sine functions during the exploitation stage to optimize the search process. Additionally, the model integrates a minority class technique enhanced with a random walk strategy (MCT-RW) to extend the boundaries of minority classes, such as Classes II, IV, and V. This approach significantly improves the recall and F1-score for these rock mass classes. The proposed methodology was rigorously evaluated against other predictive algorithms, demonstrating superior performance with an accuracy of 94.74%. This innovative model not only enhances the accuracy of rock mass classification but also contributes significantly to the intelligent and efficient construction of TBM tunnels, providing a robust solution to one of the key challenges in underground engineering.

Slope instability is a complex geological phenomenon triggered by heavy rainfall, earthquakes, tectonic forces and anthropogenic activities. Unplanned excavation of rock slopes for the development and maintenance of infrastructure such as highways, railways, and buildings in hilly regions plays a significant role in causing slope instability. The present work conducts a comprehensive stability assessment of rock slopes along the national highway (NH-44) section of the Ramban district of Jammu and Kashmir. The kinematic investigation was carried out to determine the different modes of failure of road-cut slopes, and two main modes were found: planar and wedge. Various empirical classifications have been applied for the assessment of slope stability, like geological strength index (GSI), slope mass rating (SMR), Chinese slope mass rating (CSMR), and continuous slope mass rating (CoSMR). Numerical analysis through universal distinct element code (UDEC) has been conducted to evaluate the stability of four critical rock slopes under static and dynamic loading conditions. Modelling results have provided insights into the failure mechanism, and based on these findings, remedial measures have been proposed. Three (L-1, L-3, and L-13) out of four slopes were determined to be unstable under static conditions, with factors of safety (FoS) < 1.2 and under dynamic conditions, two slopes were unstable with FoS < 1.0. However, one (L-8) slope was found to be stable under both static and dynamic conditions, with FoS 1.29 and 1.12, respectively. Installing rock bolts reduced displacement for slopes L-1 and L-13, with reductions of 11.86% and 21.05% under static conditions and 4.09% and 18.75% under dynamic conditions, respectively, while L-3 does not stabilise even after installation of rock bolts. [ABSTRACT FROM AUTHOR]

This study presents an engineering geological investigation aimed at assessing the bearing capacity of the proposed site for the Meli gold processing plant (GPP) located in the northwestern region of Tigray, Ethiopia. The geological composition of the site predominantly comprises low-grade metamorphic rocks, with intermediate metavolcanic rocks being the most prevalent. This research utilized an innovative combination of empirical methodologies, including the Hoek–Brown and Mohr–Coulomb criteria, to evaluate the strength and elasticity characteristics of the rock mass. Additionally, the rock mass foundation for the GPP was rigorously classified using renowned systems such as the rock mass rating (RMR), quality index (Q), and geological strength index (GSI). Employing five different empirical equations to estimate bearing capacity, this study significantly advances our understanding by comparing these diverse methodologies, which is a novel approach in this geological context where engineering property data are scarce or non-existent. The bearing capacities determined using the Hoek–Brown and Mohr–Coulomb criteria ranged from 11.6 to 46.2 MPa and 7.9 to 10.5 MPa, respectively. These findings not only offer valuable insights into the assessment of bearing capacity in metamorphic rock formations but also underscore the effectiveness of combining multiple empirical approaches to enhance the reliability of geological assessments. This research contributes to the advancement of construction practices and enhances project planning strategies in comparable geological environments, particularly highlighting the utility of robust empirical data in the absence of extensive drilling data. By integrating comprehensive empirical analyses, the study provides a methodological framework that significantly aids in informed decision-making for future projects located in similar geological settings.

The study for the design of tunnels in similar geological settings, providing insights into potential challenges that may arise during excavation and offering strategies for mitigating risks in District Kalam on the Ushu River, Khyber Pakhtunkhwa, Pakistan. The methodology involved geological mapping, rock sampling, discontinuity surveys, and laboratory testing for empirical analysis of tunnel parameters at the Weir House, Powerhouse, and tunnel alignment locations. Empirical analysis of tunnel parameters using three rock classification systems, rock mass rating (RMR), rock quality tunneling index, and rock mass index (RMi). Based on the classification, the rock quality was found to be fair, indicating favorable rock properties. The Q-system rated the rock as poor to fair, suggesting low discontinuity intensity, medium rock strength, or medium deformation modulus. According to the RMi, rock was rated as medium to strong, indicating low discontinuity intensity, high rock strength, or low deformability. The support design for the tunnel is based on empirical analysis, it recommends support design for the tunnel reinforcement elements such as rock bolts, wire mesh, and shotcrete lining. Overall, the tunnel is stable and does not have complex structure and weak zones. HIGHLIGHTS The study has novel significant implications for the design of tunnels in similar geological settings, providing insights into potential challenges that may arise during excavation and offering strategies for mitigating risks.; Design of a tunnel for a hydropower in a high-tectonic stress regime, for tunnel engineering due to the potential for rock mass instabilities.;

Abstract The present study introduces “rock slope instability score (RSIS)” a novel classification system for assessing rock slope stability. It takes into account geological and geotechnical parameters, as well as the impact of human activities and triggering parameters, which have become more frequent due to climate change and few of them have been ignored in existing classifications. The study focuses on rock slopes of various lithologies from the Indian Himalayas. The development of this new classification system is based on the examination of 81 different rock slopes from various states of the Indian Himalayas. Extensive field surveys, rock sampling, geotechnical laboratory tests, and ground measurements have been conducted at the various slope sites to establish a comprehensive scoring system for the stability assessment. The distributions of weightage to each parameter have been considered, corresponding to its degree of impact in causing slope instability. Sensitivity analysis of all defined parameters of RSIS system has revealed that the majority of the parameters exhibit a strong positive correlation, with Pearson correlation coefficients ranging from 0.74 to 0.61. However, two parameters, namely discontinuity dip and the relationship between slope & discontinuity direction, gives moderate relationship with correlation coefficient values of 0.48 and 0.41, respectively. To avoid any designer biasness in the system, several individuals gathered data set at different times. The proposed classification system has demonstrated a strong correlation with the actual slope condition, and it is quite promising. The outcome of RSIS classification for studied 81 slopes classified 2 slopes under stable condition, 21 slopes as partially stable, 44 as unstable, and 14 as completely unstable.

This study presents an engineering geological investigation aimed at assessing the bearing capacity of the proposed site for the Meli gold processing plant (GPP) located in the northwestern region of Tigray, Ethiopia. The geological composition of the site predominantly comprises low-grade metamorphic rocks, with intermediate metavolcanic rocks being the most prevalent. This research utilized an innovative combination of empirical methodologies, including the Hoek–Brown and Mohr–Coulomb criteria, to evaluate the strength and elasticity characteristics of the rock mass. Additionally, the rock mass foundation for the GPP was rigorously classified using renowned systems such as the rock mass rating (RMR), quality index (Q), and geological strength index (GSI). Employing five different empirical equations to estimate bearing capacity, this study significantly advances our understanding by comparing these diverse methodologies, which is a novel approach in this geological context where engineering property data are scarce or non-existent. The bearing capacities determined using the Hoek–Brown and Mohr–Coulomb criteria ranged from 11.6 to 46.2 MPa and 7.9 to 10.5 MPa, respectively. These findings not only offer valuable insights into the assessment of bearing capacity in metamorphic rock formations but also underscore the effectiveness of combining multiple empirical approaches to enhance the reliability of geological assessments. This research contributes to the advancement of construction practices and enhances project planning strategies in comparable geological environments, particularly highlighting the utility of robust empirical data in the absence of extensive drilling data. By integrating comprehensive empirical analyses, the study provides a methodological framework that significantly aids in informed decision-making for future projects located in similar geological settings. [ABSTRACT FROM AUTHOR]

The present study introduces "rock slope instability score (RSIS)" a novel classification system for assessing rock slope stability. It takes into account geological and geotechnical parameters, as well as the impact of human activities and triggering parameters, which have become more frequent due to climate change and few of them have been ignored in existing classifications. The study focuses on rock slopes of various lithologies from the Indian Himalayas. The development of this new classification system is based on the examination of 81 different rock slopes from various states of the Indian Himalayas. Extensive field surveys, rock sampling, geotechnical laboratory tests, and ground measurements have been conducted at the various slope sites to establish a comprehensive scoring system for the stability assessment. The distributions of weightage to each parameter have been considered, corresponding to its degree of impact in causing slope instability. Sensitivity analysis of all defined parameters of RSIS system has revealed that the majority of the parameters exhibit a strong positive correlation, with Pearson correlation coefficients ranging from 0.74 to 0.61. However, two parameters, namely discontinuity dip and the relationship between slope & discontinuity direction, gives moderate relationship with correlation coefficient values of 0.48 and 0.41, respectively. To avoid any designer biasness in the system, several individuals gathered data set at different times. The proposed classification system has demonstrated a strong correlation with the actual slope condition, and it is quite promising. The outcome of RSIS classification for studied 81 slopes classified 2 slopes under stable condition, 21 slopes as partially stable, 44 as unstable, and 14 as completely unstable. [ABSTRACT FROM AUTHOR]

Current rock engineering design in drill and blast tunnelling primarily relies on engineers' observational assessments. Measure While Drilling (MWD) data, a high-resolution sensor dataset collected during tunnel excavation, is underutilised, mainly serving for geological visualisation. This study aims to automate the translation of MWD data into actionable metrics for rock engineering. It seeks to link data to specific engineering actions, thus providing critical decision support for geological challenges ahead of the tunnel face. Leveraging a large and geologically diverse dataset of ∼500,000 drillholes from 15 tunnels, the research introduces models for accurate rock mass quality classification in a real-world tunnelling context. Both conventional machine learning and image-based deep learning are explored to classify MWD data into Q-classes and Q-values—examples of metrics describing the stability of the rock mass—using both tabular- and image data. The results indicate that the K-nearest neighbours algorithm in an ensemble with tree-based models using tabular data effectively classifies rock mass quality. It achieves a cross-validated balanced accuracy of 0.86 in classifying rock mass into the Q-classes A, B, C, D, E1, E2, and 0.95 for a binary classification with E versus the rest. Classification using a CNN with MWD-images for each blasting round resulted in a balanced accuracy of 0.82 for binary classification. Regressing the Q-value from tabular MWD-data achieved cross-validated R2 and MSE scores of 0.80 and 0.18 for a similar ensemble model as in classification. High performance in regression and classification boosts confidence in automated rock mass assessment. Applying advanced modelling on a unique dataset demonstrates MWD data's value in improving rock mass classification accuracy and advancing data-driven rock engineering design, reducing manual intervention.

In the last few decades, several rock mass classification systems have been proposed to identify rock slope stability conditions, having high probability of failure and accordingly prioritize preventive measures. This paper reviews the various classification systems, highlighting their differences and similarities with regard to the factors involved and the mode of their failures. The advantages and limitations of each classification have also been compared. However, many of these existing systems fail to classify slope cuttings according to their actual vulnerability of failure, as they ignore important triggering factors such as earthquakes or precipitation. For example groundwater is considered as an instability causing factor with limited effect, rather than a triggering factor for failure. It is observed that rock slope should be classified according to their potential of failure, taking into account both their condition and the influence of triggering factors upon stability. It is also observed that it is important to analyse each type of failure separately, since each one is influenced by unique factors of instability. Finally, it provides suggestion for the improvement of existing classifications through incorporation of all the critical factors like slope aspect, mode of excavation, earthquake, and rainfall that would have caused slope instability. [ABSTRACT FROM AUTHOR]

The differences in geomechanical properties and the uncertainty in the spatial distribution of Bimrock pose significant challenges to the construction and disaster prediction of geotechnical engineering. To clarify the geomechanical characteristics of Bimrock, this paper summarizes the basic concepts and classification methods of Bimrock at home and abroad. It discusses the methods and characteristics of determining the geometric features of Bimrock blocks and explores the influencing factors and laws of failure modes and strength under different stress states of Bimrock. The study finds that the failure mode of Bimrock is mainly influenced by factors such as block proportion, degree of welding between blocks and matrix, strength ratio between blocks and matrix, and geometric properties of blocks. Among these factors, block proportion is the most significant, and the degree of welding is a controlling factor. However, due to the complexity of Bimrock structures, there is a lack of applicable methods and mechanical models for the evaluation of geomechanical characteristics of Bimrock in engineering practice. This article also explores the influence and research methods of the geological characteristics of Bimrock in slope and tunnel engineering and, finally, provides prospects for the future research trends relating to Bimrock. [ABSTRACT FROM AUTHOR]

Technological advancements have made rock engineering more data-driven, leading to increased use of machine learning (ML). While the use of ML in rock engineering has the potential to transform the industry, several methodological issues should first be addressed: (i) rock engineering's use of biased (poor quality) data, resulting in biased ML models and (ii) limited rock mass classification and characterization data. If these issues are not addressed, rock engineering risks using unreliable ML models that can have potential real-life adverse impacts. This paper aims to provide an overview of these methodological issues and demonstrate their impact on the reliability of ML models using surrogate models. To take full advantage of the benefits of ML, rock engineers should make sure that their ML models are reliable by ensuring that there are sufficient unbiased data to develop reliable ML models. In the context of this paper, the term sufficient retains a relative meaning since the amount of data that is sufficient to develop reliable a ML models depends on the problem under consideration and the application of the ML model (e.g., pre-feasibility, feasibility, design stage). [ABSTRACT FROM AUTHOR]

The network of Himalayan roadways and highways connects some remote regions of valleys or hill slopes, which is vital for India's socio-economic growth. Due to natural and artificial factors, frequency of slope instabilities along the networks has been increasing over last few decades. Assessment of stability of natural and artificial slopes due to construction of these connecting road networks is significant in safely executing these roads throughout the year. Several rock mass classification methods are generally used to assess the strength and deformability of rock mass. This study assesses slope stability along the NH-1A of Ramban district of North Western Himalayas. Various structurally and non-structurally controlled rock mass classification systems have been applied to assess the stability conditions of 14 slopes. For evaluating the stability of these slopes, kinematic analysis was performed along with geological strength index (GSI), rock mass rating (RMR), continuous slope mass rating (CoSMR), slope mass rating (SMR), and Q-slope in the present study. The SMR gives three slopes as completely unstable while CoSMR suggests four slopes as completely unstable. The stability of all slopes was also analyzed using a design chart under dynamic and static conditions by slope stability rating (SSR) for the factor of safety (FoS) of 1.2 and 1 respectively. Q-slope with probability of failure (PoF) 1% gives two slopes as stable slopes. Stable slope angle has been determined based on the Q-slope safe angle equation and SSR design chart based on the FoS. The value ranges given by different empirical classifications were RMR (37–74), GSI (27.3–58.5), SMR (11–59), and CoSMR (3.39–74.56). Good relationship was found among RMR & SSR and RMR & GSI with correlation coefficient (R2) value of 0.815 and 0.6866, respectively. Lastly, a comparative stability of all these slopes based on the above classification has been performed to identify the most critical slope along this road.

The geological condition monitoring and identification based on TBM-equipped sensors is of great significance for efficient and safe tunnel construction. Full-scale rotary cutting experiments are carried out using tunnel-boring machine disc cutters. Thrust, torque and vibration sensors are equipped on the rotary cutting machine (RCM). A stacking ensemble-learning model for real-time prediction of rock mass classification using features of mathematical statistics is proposed. Three signals, thrust, torque and a novel vibration spectrogram-based local amplification feature, are fed into the model and trained separately. The results show that the stacked ensemble-learning model has better accuracy and stability than any single model, showing a good application prospect in the rock mass classification.

Accurate rock mass quality classification is crucial for the design and construction of underground projects. Traditional methods often rely on expert experience, introducing subjectivity, and struggle with complex geological conditions. Machine learning algorithms have improved this issue, but obtaining complete rock mass quality datasets is often difficult due to high cost and complex procedures. This study proposed a hybrid XGBoost model for predicting rock mass quality using incomplete datasets. The zebra optimization algorithm (ZOA) and Bayesian optimization (BO) were used to optimize the hyperparameters of the model. Data from various regions and types of underground engineering projects were utilized. Adaptive synthetic (ADASYN) oversampling addressed class imbalance. The model was evaluated using metrics including accuracy, Kappa, precision, recall, and F1-score. The ZOA-XGBoost model achieved an accuracy of 0.923 on the test set, demonstrating the best overall performance. Feature importance analysis and individual conditional expectation (ICE) plots highlighted the roles of RQD and UCS in predicting rock mass quality. The model’s robustness with incomplete data was verified by comparing its performance with other machine learning models on a dataset with missing values. The ZOA-XGBoost model outperformed other models, proving its reliability and effectiveness. This study provides an efficient and objective method for rock mass quality classification, offering significant value for engineering applications.

The value of Poisson's ratio is a crucial parameter in rock mechanics and engineering for both intact rock and rock mass. Poisson's ratio has not gotten the attention it merits compared to other essential mechanical characteristics of intact rock and rock mass. Limited relationships exist between rock mass classification systems (such as RMR, RMQR, Q, and GSI) and Poisson's ratio. This paper provides a comprehensive review of models proposed by various researchers for estimating Poisson's ratio for rock mass. The different methods are compared, and new general equations are derived. The results indicate that the Poisson's ratio value of rock mass is inversely proportional to its quality and strength and depends on the Poisson's ratio value of the intact rock. Specifically, a linear equation is obtained using the RMR or GSI system, showing that the Poisson's ratio increases as the quality and strength of the rock mass decrease. The Q system has a logarithmic link between the rock mass quality and Poisson's ratio. It should be noted that the derived equations are applicable only under the assumption of a homogeneous isotropic rock mass. [ABSTRACT FROM AUTHOR]

This study covers field and laboratory studies on Rock Mass Characterization carried out in the main gallery of an underground Lead-Zinc (Pb-Zn) quarry located in the Yahyalı/Kayseri region. With detailed studies on rock material and rock mass, stability analysis was carried out by calculating all relevant parameters of Rock Mass Rating (RMR), Rock Mass Quality (Q) and, Geological Strength Index (GSI) systems. The results have shown that, considering the existing section of the main gallery opened within the limestone formation, it can generally survive for many years without support, provided that local support is applied in the necessary sections. However, it should not be forgotten that mines are dynamic structures and constantly change with operation. Rock mass conditions may change with engineering errors such as uncontrolled blasting. Although this conclusion has been reached with the rock mass classification systems used, the application of support must be continued and constantly inspected in locations where sudden rock mass quality changes may occur (especially ore zones, fault zones, melting gaps) and in places that pose danger. [ABSTRACT FROM AUTHOR]

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The most common classification method of drill cores is the Rock Quality Designation (RQD) value, which indicates the percentage of rock cores longer than 10 cm in a given core section. This core logging procedure is the basic parameter in the most useful rock mass classification methods like Rock Mass Rate (RMR) and Rock Mass Quality (Q). It is also used to determine the Geological Strength Index (GSI), which has become widely used in the last 20 years. One of the basic problems of the RQD value is that it does not distinguish different rock cores longer than 10 cm (100% is obtained for one piece of 1 m length and 10 pieces of 10 cm length) and a uniform result is obtained for shorter units. In this paper, the so-called Integrated RQD (Int_RQD) factor is introduced to eliminate these problems and to provide a better description of fracture density in the core logging procedure. As it uses the original core logging procedure, historical RQD data can also be reevaluated. Considering that RQD is an input parameter for most rock engineering classifications, these systems such as GSI can be reviewed based on the new RQD definition proposed herein. [ABSTRACT FROM AUTHOR]

The Q-slope (2015) geomechanical classification is the only empirical methodology for slope stability analysis that considers the effect of ice. This article shows the results of its application for icy slopes in Spain. Despite its sunny image, Spain is a mountainous country with cold winters. Slopes alongside a mountain road and a ski resort carpark have been studied in Puerto de Navacerrada (Madrid region) and Benasque (Pyrenees). This work combines the manual collection of geomechanical data on slope characteristics with an empirical analysis of slope stability and remote data acquisition techniques (Structure from Motion, SfM). The results of the field data collection are shown in a metaverse with two approaches: a 360° image virtual tour and a 3D image repository. One of the novelties of this work is that researchers who wish to replicate these analyses can access a table of input parameters and make a virtual field trip to see the parameters included in this study. The results show that the Q-slope methodology is useful for stability analysis and is conservative in terms of recommendations: All the slopes that are seen as unstable appear as such, but some slopes that do not present problems are shown as "transitional" slopes. The data and observations taken are useful for increasing the methodology database and for completing existing graphs of stable vertical slopes with good quality, as well as slopes with little slope and low quality. [ABSTRACT FROM AUTHOR]

Rock mass classification is essential for assessing the quality of macroscopic rock mass and is the basis for rock mass stability analysis and geotechnical engineering design. The joint observation technology limits traditional rock mass classification methods in that they only collect joint information from one-dimensional or two-dimensional space and cannot comprehensively obtain the joint occurrence in three-dimensional space. Consequently, empirical formulas are frequently used in studies on joint distribution laws, resulting in less accurate calculations of joint parameters. This study develops a method for classifying rock masses using a precise description of the joints. Initially, it utilizes the borehole camera and the Sirovision joint scanning system to acquire accurate three-dimensional joint occurrence data. The subjective and the objective weights of each evaluation index are derived from the analytic hierarchy process (AHP) and the CRITIC technique according to the cloud model theory. The game theory is then employed to determine the combined weight and evaluate the quality of a rock mass method with the cloud model (GA-CM). The proposed classification method is applied to the slope of an open-pit mine. The results indicate that compared to the traditional methods, the proposed method is objective, accurate, and field-applicable and also reduces the influence of subjective factors on rock mass quality evaluation and enhances the classification reliability. [ABSTRACT FROM AUTHOR]

The most common classification method of drill cores is the Rock Quality Designation (RQD) value, which indicates the percentage of rock cores longer than 10 cm in a given core section. This core logging procedure is the basic parameter in the most useful rock mass classification methods like Rock Mass Rate (RMR) and Rock Mass Quality (Q). It is also used to determine the Geological Strength Index (GSI), which has become widely used in the last 20 years. One of the basic problems of the RQD value is that it does not distinguish different rock cores longer than 10 cm (100% is obtained for one piece of 1 m length and 10 pieces of 10 cm length) and a uniform result is obtained for shorter units. In this paper, the so-called Integrated RQD (Int_RQD) factor is introduced to eliminate these problems and to provide a better description of fracture density in the core logging procedure. As it uses the original core logging procedure, historical RQD data can also be reevaluated. Considering that RQD is an input parameter for most rock engineering classifications, these systems such as GSI can be reviewed based on the new RQD definition proposed herein.

It is the foundation of tunnel engineering to classify the rock mass surrounding tunnels. However, it is not easy to precisely determine the class of rock mass in practice as sufficient geological exploration need to be completed before rock mass classification, and there exists some disputes referring to the rationalization of dozens of methods for rock mass classification through the world. The principles and procedures of the basic quality method, which are widely used in China, are presented in this paper, and the application process of the basic quality method is showed with a project case of Zhongnanshan highway tunnel which has operated in safety for nearly a decade. Then, both the advantages and disadvantages of the basic quality method are analyzed in terms of practical engineering applications. In consideration of the defects of the basic quality method, the concept of the subclassing of surrounding rock in grade III–V is developed in the end and the criterion is given to determine the subclass of rock mass. This study is aimed at providing some useful ideas and a reference for rock classification in highway tunnel engineering.

AbstractIn this study, combined empirical, numerical, and in-situ monitoring methods were combined to carry out stability analyses and support design for a haulage drift subjected to mining activity. The rock mass quality of the haulage drift was characterized by the RMR, Q, and GSI, and the rock mass properties were calculated. The support requirements for haulage drift during mining were determined by rock mass classification systems. RS2 was used to analyze the plastic zone and displacement of the haulage drift during mining. After the stope was mined, the surrounding rock exhibited a butterfly plastic zone with an asymmetric distribution, and the roof damage was most severe near the stope side. Overall, the haulage drift tended to move in the stope direction, which is consistent with engineering expectations. The support systems determined using the empirical method were analyzed using RS2 and UNWEDGE software. The maximum plastic zone depth of the roof decreased from 4.2 to 2.01 m, and the safety factor of the unstable wedge block increased from 0 to 10.2 after support. In-situ drilling detection shows that the failure depth of the haulage drift roof is 2.37 m. Therefore, a combination of empirical, numerical, and in-situ monitoring methods can be effective for quantitative stability assessments and support design optimization of haulage drifts in the vicinity of mined stopes.

AbstractThe surrounding rock control has been a difficult problem for fractured rock mass in hard rock mines. This article describes a case study of the failure mechanisms and the support design technology for fractured rock mass drifts in Xinli Gold Mine. Based on field investigation, the geology characteristics, failure types, influencing factors, support types, and their failure types were analyzed. The rock mass classification, rock mass physical and mechanical parameters were obtained by using Q, RMR, and GSI systems. The zoning of surrounding rock, stability analysis and zoning support schemes design were carried out based on rock mass classification results. The pretension is designed by China underground mine experiences and verified by numerical simulation. RS2 was used to compare the plastic zone under pre- and post-support conditions. The plastic zone is significantly reduced after support is installed, which indicates that the designed support schemes can effectively control the failure of surrounding rock. In view of difficulties in the excavation and support of fractured rock mass, the short excavation and short support technology was proposed to ensure the success excavation of the drift in fractured rock mass. The field application shows that the short excavation and support technology are effective.

With the depletion of shallow mineral resources, deep mining has become an important part of the mining industry. One of the key characteristics of deep mining is the dramatic deterioration of geological conditions in mining engineering under the influence of "three highs and one disturbance". The surrounding rock exhibits significant jointing, which brings great challenges to the control of deep tunneling. Taking the typical jointed tunnels below-900 m level in Sanshandao Gold Mine as the engineering background, this study conducted engineering geological investigations, literature surveys, and laboratory rock mechanics tests to obtain basic data. The Dips software was used for structural plane grouping, and the Unwedge software was employed for wedge analysis and stability assessment. The Rock Mass Rating（RMR), Q, and Geological Strength Index（GSI） methods were used to classify the rock mass and estimate its mechanical parameters. The mechanism of discontinuous face reinforcement with rock bolts was elaborated, and the required support pressure for tunnel support was estimated. Based on the Q and RMR support charts, a support scheme was designed, and its effectiveness was validated using RS2 numerical modeling. Industrial experiments were conducted at the-960 m level, and the results demonstrated that the designed support scheme effectively controlled the deformation and failure of deep high-stress jointed surrounding rock in tunnels, providing a basis for safe and efficient mining in deep mines. [ABSTRACT FROM AUTHOR]

Located 145 km away from Konya, the Körükini cave is one of the most important caves of the Derebucak area. In this study, it is aimed to evaluate the stability of the Körükini cave. For this purpose, we conducted geological field trips, took block samples and made in-situ field tests. Following these steps, we applied laboratory tests to determine physico-mechanical properties on rock samples taken from the cave. Based on the RMR, GSI, Q, RMi classification, rock mass values were determined for the Körükini Cave. Thus, average RMR is 60, average Q is 8.2, average GSI is 75 and average RMi 11.64. the obtained data show that the Körükini Cave is unstable in all locations according to empirical stability evaluation. Thus, support (e.g. systematic bolting, fibre reinforced shotcrete and bolting, and fibre reinforced shotcrete and bolting) is needed for the Körükini Cave. [ABSTRACT FROM AUTHOR]

Technological advancements have made rock engineering more data-driven, leading to increased use of machine learning (ML). While the use of ML in rock engineering has the potential to transform the industry, several methodological issues should first be addressed: (i) rock engineering’s use of biased (poor quality) data, resulting in biased ML models and (ii) limited rock mass classification and characterization data. If these issues are not addressed, rock engineering risks using unreliable ML models that can have potential real-life adverse impacts. This paper aims to provide an overview of these methodological issues and demonstrate their impact on the reliability of ML models using surrogate models. To take full advantage of the benefits of ML, rock engineers should make sure that their ML models are reliable by ensuring that there are sufficient unbiased data to develop reliable ML models. In the context of this paper, the term sufficient retains a relative meaning since the amount of data that is sufficient to develop reliable a ML models depends on the problem under consideration and the application of the ML model (e.g., pre-feasibility, feasibility, design stage).

The differences in geomechanical properties and the uncertainty in the spatial distribution of Bimrock pose significant challenges to the construction and disaster prediction of geotechnical engineering. To clarify the geomechanical characteristics of Bimrock, this paper summarizes the basic concepts and classification methods of Bimrock at home and abroad. It discusses the methods and characteristics of determining the geometric features of Bimrock blocks and explores the influencing factors and laws of failure modes and strength under different stress states of Bimrock. The study finds that the failure mode of Bimrock is mainly influenced by factors such as block proportion, degree of welding between blocks and matrix, strength ratio between blocks and matrix, and geometric properties of blocks. Among these factors, block proportion is the most significant, and the degree of welding is a controlling factor. However, due to the complexity of Bimrock structures, there is a lack of applicable methods and mechanical models for the evaluation of geomechanical characteristics of Bimrock in engineering practice. This article also explores the influence and research methods of the geological characteristics of Bimrock in slope and tunnel engineering and, finally, provides prospects for the future research trends relating to Bimrock.

The Q-system and Q-slope are empirical methods developed for classifying and assessing rock masses for tunneling, underground mining, and rock slope engineering. Both methods have been used extensively to guide appropriate ground support design for underground excavations and stable angles for rock slopes. Using datasets obtained from igneous, sedimentary, and metamorphic rock slopes from various regions worldwide, this research investigates different relationships between the geological strength index (GSI) and the Q-system and Q-slope. It also presents relationships between chart-derived GSI with GSI estimations from RMR89 and Q' during drill core logging or traverse mapping. Statistical analysis was used to assess the reliability of the suggested correlations to determine the validity of the produced equations. The research demonstrated that the proposed equations provide appropriate values for the root mean squared error value (RMSE), the mean absolute percentage error (MAPE), the mean absolute error (MAE), and the coefficient of determination (R-squared). These relationships provide appropriate regression coefficients, and it was identified that correlations were stronger when considering metamorphic rocks rather than other rocks. Moreover, considering all rock types together, achieved correlations are remarkable. [ABSTRACT FROM AUTHOR]

Measurement while drilling (MWD) data reflect the drilling rig–rock mass interaction; they are crucial for accurately classifying the rock mass ahead of the tunnel face. Although machine-learning methods can learn the relationship between MWD data and rock mechanics parameters to support rock classification, most current models do not consider the impact of the continuous drilling-sequence process, thereby leading to rock-classification errors, while small and unbalanced field datasets result in poor model performance. We propose a novel deep neural network model based on Bi-directional Long Short-Term Memory (BILSTM) to extract information-related sequences in MWD data and improve the accuracy of the rock-mass classification. Two optimization modules were designed to improve the model's generalization performance. Stratified K-fold cross-validation was used for model optimization in small and unbalanced datasets. Model validation is based on the MWD dataset of a highway tunnel in Yunnan, China. Multiple metrics show that the prediction ability of the network is significantly better than those of a multilayer perceptron (MLP) and a support-vector machine (SVM), while the model exhibits an improved generalization performance. The accuracy of the network can reach 90%, which is 13% and 15% higher than the MLP and SVM, respectively. [ABSTRACT FROM AUTHOR]

In the last few decades, rock mass classification had been used intensively in engineering design. The concern arises for the potential occurrence of localized or global rock mass failure due to geological (such as discontinuities, water, weathering) and non-geological (such as construction load, vibration) factors should be taken into consideration. The ability of a classification system to represent the rock mass condition in a straightforward and effective way can assist in engineering works. Two classification systems can be applied in underground construction such as tunneling and mining. In tunnel construction, the Rock Mass Rating (RMR), Q System, Japanese Highway (JH) Method, Geological Strength Index (GSI) and Rock Mass Quality Rating (RMQR) are commonly used. However, these systems cannot be applied for rock mass covered by shotcrete due to difficulty in observing the discontinuity planes. Therefore, a new investigation method and rock mass classification for rock mass covered by shotcrete is vital to overcome this limitation. [ABSTRACT FROM AUTHOR]

The Zenica tunnel is located as part of the highway on the Vc corridor, Ponirak section-southern exit from the Zenica tunnel. The tunnel is a two-tube with a length of about 3.300 meters. During the excavation of the Zenica tunnel, large amounts of groundwater were recorded in several sections in both tunnel pipes, from wet to flowing. During the tunnel excavation, the engineering-geological data on the rocks through which the excavation was carried out was regularly recorded, and on the basis of these data, the RMR classification of the rock mass was determined. The paper presents one of the possible solutions to control the inflow of groundwater during tunnel excavation by making subhorizontal wells and installing self-drilling anchors for the purpose of directing groundwater into drainage pipes or tunnel sides. The main goal of controlling the groundwater inflow is to reduce the impact of groundwater on further tunnel excavation and to modify the category of rock mass (additional benefit), and thus to reduce the unit price of tunnel excavation.