Evaluation Of Rock Mass Quality Research Articles

Data driven assessment of rock mass quality in red-bed hilly area: a case study of Guang’an city, SW China

The evaluation of geological suitability for urban underground space (UUS) development is an indispensable prerequisite for its optimal utilization. As the actual carrier of underground facilities, the evaluation of rock mass quality plays a crucial role in assessing geological suitability. However, it is notable that the evaluation of rock mass quality has regrettably remained somewhat marginalized within the broader framework of the geological suitability assessment in recent years. The selection of pertinent indicators for the evaluation of rock mass quality inherently presents an appreciable degree of subjectivity. Predominantly subjective evaluation methods continue to dominate the field, while the application of objective algorithms, such as unsupervised clustering, remains in its nascent stage. Furthermore, there is a lack of comprehensive investigations into distinct combinations of attributes. This limitation confines the broader applicability of the evaluation outcomes in the context of urban underground space. Within this study, we meticulously amassed rock core test data from over 40 boreholes of engineering geological significance within the urban planning ambit of Guang'An City. Utilizing the K-means unsupervised clustering algorithm and the Principal Component Analysis (PCA) algorithm. We successfully conducted an unsupervised clustering procedure with nine distinct physical and mechanical attributes. This yielded an aggregation into five discernible clusters. Building upon the derived clustering outcomes, a stratification of rock mass quality was effectuated into three distinct tiers: Level 1 (characterized by pure sandstone), Level 2 (primarily dominated by sandstone), and Level 3 (denoting fair conditions predominantly influenced by mudstone). This structured stratification facilitates a relatively objective and comprehensive evaluation of rock mass quality within the context of the red-bed hilly terrain. In the course of this analytical trajectory, we conducted a dissection of the clustering efficacy. For strongly correlated attributes, we propose a preliminary dimensionality reduction procedure prior to the clustering endeavor. Moreover, we recommend intervals of 10 m for the stratified evaluation in red bed hilly urban terrains.

Rock mass quality evaluation via statistically optimized geophysical datasets

Rock mass quality evaluation via statistically optimized geophysical datasets

An intelligent method for TBM surrounding rock classification based on time series segmentation of rock-machine interaction data

Accurate, continuous real-time perception of surrounding rock conditions on the tunnel boring machine (TBM) tunnel face provides particularly important prior information to ensure safe, economic, and efficient tunneling. This study presents an intelligent method for assessing the TBM surrounding rock conditions using a combination of time series segmentation and a deep clustering model (SSAE-FCM). In data preprocessing, fifteen operational parameters were selected as input features. And an adaptive piecewise constant approximation method was employed for the segmentation and compression of stable phase data. Then, a total of 69,214 data samples that can characterize the stable rock-machine interaction were obtained to construct a dataset. Thereafter, the SSAE-FCM model was established and trained to classify the surrounding rock conditions into four clusters. The distribution result of the main operational parameters and composite indexes (TPI, FPI, and SE) within these clusters demonstrated that the classification results are compatible with rock-machine interaction in engineering practice. Additionally, the rock mass assessment performance of the classification results was compared to BQ classification in terms of various rock mass parameters (UCS, Jv), fault sections, and collapse sections. These results indicate that the proposed classification method accurately reflects the stability and boreability of rock mass and is more sensitive to unfavorable geology than the BQ classification. From the three clustering evaluation indexes (SC, CHI, and DBI) perspective, SSAE-FCM was superior to the other clustering models (BIRCH + k-means++, FCM, and SSAE + FCM). In conclusion, the proposed intelligent method for TBM surrounding rock classification is reasonable and applicable for the evaluation of rock mass quality, as well as accurate and sensitive in real-time perception.

Application of K-PSO Clustering Algorithm and Game Theory in Rock Mass Quality Evaluation of Maji Hydropower Station

In this study, the K-means algorithm based on particle swarm optimization (K-PSO) and game theory are introduced to establish the quality evaluation model of a rock mass. Five evaluation factors were considered, i.e., uniaxial saturated compressive strength of rock, discontinuity spacing, acoustic velocity, rock quality designation (RQD), and integrity coefficient. The rock mass of an elevation adit at the abutment of Maji hydropower station was taken as a case study. The subjective weight of the evaluation factor was determined by the weighted least squares method, and the objective weight of the evaluation factor was determined by the entropy method. The combined weights of each influencing factor were determined by game theory to be 0.142, 0.179, 0.035, 0.116, and 0.108. The rock mass quality evaluation in the study area was analyzed by K-PSO algorithm. The results indicate that the K-PSO clustering results are almost the same as the evaluation results of the traditional basic quality (BQ) classification method and the widely used extension evaluation method and are consistent with the preliminary judgment of the expert field. The results are consistent with the field observation law. It is considered that the K-PSO clustering theory can reflect the engineering geological characteristics of the rock mass of the hydropower project in the rock mass quality evaluation.

Rock mass quality classification based on deep learning: A feasibility study for stacked autoencoders

Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable stability assessment. To develop a tool that can deliver quick and accurate evaluation of rock mass quality, a deep learning approach is developed, which uses stacked autoencoders (SAEs) with several autoencoders and a softmax net layer. Ten rock parameters of rock mass rating (RMR) system are calibrated in this model. The model is trained using 75% of the total database for training sample data. The SAEs trained model achieves a nearly 100% prediction accuracy. For comparison, other different models are also trained with the same dataset, using artificial neural network (ANN) and radial basis function (RBF). The results show that the SAEs classify all test samples correctly while the rating accuracies of ANN and RBF are 97.5% and 98.7%, repectively, which are calculated from the confusion matrix. Moreover, this model is further employed to predict the slope risk level of an abandoned quarry. The proposed approach using SAEs, or deep learning in general, is more objective and more accurate and requires less human intervention. The findings presented here shall shed light for engineers/researchers interested in analyzing rock mass classification criteria or performing field investigation.

Determination of rock mass integrity coefficient using a non-invasive geophysical approach

Determination of rock mechanical parameters is the most important step in rock mass quality evaluation and has significant impacts on geotechnical engineering practice. Rock mass integrity coefficient (KV) is one of the most efficient parameters, which is conventionally determined from boreholes. Such approaches, however, are time-consuming and expensive, offer low data coverage of point measurements, require heavy equipment, and are hardly conducted in steep topographic sites. Hence, borehole approaches cannot assess the subsurface thoroughly for rock mass quality evaluation. Alternatively, use of geophysical methods is non-invasive, rapid and economical. The proposed geophysical approach makes useful empirical correlation between geophysical and geotechnical parameters. We evaluated the rock mass quality via integration between KV measured from the limited boreholes and inverted resistivity obtained from electrical resistivity tomography (ERT). The borehole-ERT correlation provided KV along various geophysical profiles for more detailed 2D/3D (two-/three-dimensional) mapping of rock mass quality. The subsurface was thoroughly evaluated for rock masses with different engineering qualities, including highly weathered rock, semi-weathered rock, and fresh rock. Furthermore, ERT was integrated with induced polarization (IP) to resolve the uncertainty caused by water/clay content. Our results show that the proposed method, compared with the conventional approaches, can reduce the ambiguities caused by inadequate data, and give more accurate insights into the subsurface for rock mass quality evaluation.

Rock mass classification for rock mass in tunnelling and underground excavation - Development, limitation and way forward

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.

Quantitative assessment of rock mass stress–strain behavior at Severomuysky Tunnel

The article presents the selected geotechnical engineering studies accomplished in 2021–2022 by the Research Center for Applied Geomechanics and Convergent Technologies in Mining, College of Mining, NUST MISIS for the reconstruction project of Severomuysky Tunnel (ST) of the Baikal–Amur Mainline. The studies included the quantitative and qualitative geotechnical assessment of rock mass, its stress–strain analysis and susceptibility to rock bursts. Within the rock mass quality rating of ST, using the geological logs on 441 sample boreholes, the lab-scale test data on the physical and mechanical properties of 1820 rock samples were processed. The average values of the rock mass quality rating are presented for three geotechnical elements. The stress–strain analysis of ST rock mass and the lab-scale tests of rock brittleness made it possible to detect sites susceptible to rock bursts along the transportation tunnel, and to develop appropriate geotechnical monitoring activities. The recommendations are given to implement the described geotechnical surveys in construction and reconstruction of analogous objects and facilities, and in solid mineral mining.  The geotechnical research and other operations were carried out under guidance of RSRS GmbH Railway Infrastructure Projects CEO E. V. Dorot and the Company’s experts. The authors appreciate participation of the Project Engineers of the Research Center for Applied Geomechanics and Convergent Technologies in Mining, College of Mining, NUST MISIS N. G. Vysotin, A. M. Yanbekov, M. A. Kosyreva and S. S. Shermatova.

A probability prediction method for the classification of surrounding rock quality of tunnels with incomplete data using Bayesian networks

The classification of surrounding rock quality is critical for the dynamic construction and design of tunnels. However, obtaining complete parameters for predicting the surrounding rock grades is always challenging in complex tunnel geological environment. In this study, a new method based on Bayesian networks is proposed to predict the probability for the classification of surrounding rock quality of tunnel with incomplete data. A database is collected with 286 cases in 10 tunnels, involving nine parameters: rock hardness, weathering degree, rock mass integrity, rock mass structure, structural plane integrity, in-situ stress, groundwater, rock basic quality, and surrounding rock level. Moreover, the Bayesian network structure is built using the collected database and quantitatively verified by strength analysis. Then, the accuracy, precision, recall, F-measure and receiver operating characteristic (ROC) curves are utilized for model evaluation. The average values of accuracy, precision, recall, F-measure, and area under the curve (AUC) are approximately 89.2%, 91%, 92%, 91%, and 0.98, respectively. These results indicate that the established classification model has high accuracy, even with small sample size and imbalanced samples. Ten additional sets of tunnel cases (incomplete data) are also used for verification. The results reveal that compared with the traditional Q-system (Q) and rock mass rating (RMR) classification methods, the proposed classification model has the lowest error rate and is capable of using incomplete data to predict sample results. Finally, sensitivity analysis suggests that the rock hardness and rock mass integrity have the strongest impact on the quality of tunnel surrounding rock. Overall, the findings of this study can serve as a useful reference for future rock mass quality evaluation in tunnels, underground powerhouses, slopes, etc.

Methodology of geomechanical block modeling of rock mass in Taimyrsky Mine field

High-quality geomechanics research counts for much regarding two critical factors in mineral mining—safety and efficiency. The Mining Geology Information Systems enable detecting structural features in rock mass to be taken into account in mine planning and operation, which minimizes risks and, as a consequence, enhances efficiency of mining. The article describes a methodology of constructing a geomechanical block model of the Taimyrsky Mine field, Norilsk Nickel’s Polar Division. The block modeling was implemented in MGIS software Micromine. The source data analyses used the empirical, statistical and analytical methods of verification, the structural domains were determined using the CUSUM method, the data were geostatistically interpolated to the geomechanical block model, and the results were verified. The geomechanical block modeling methodology for the Taimyrsky Mine field provides highly reliable processing of source data before modeling, localization of highly fractured zones in rock mass and detection of consistent areas. Ordinary kriging makes it possible to image the patterns of changes in rock mass parameters in the block model. In its turn, the geomechanical block model fosters the real-time assessment of the rock mass behavior, the prompt adjustment of location of underground excavations, as well as the choice and design of mine support systems for permanent, development and breakage headings based on the rock mass quality rating systems.

Hard-rock investigation using a non-invasive geophysical approach

Investigation of a hard-rock terrain is essential for the development of engineering infrastructures and groundwater assessment. In such investigations, evaluation of rock mass quality and faults/fractures is a challenging task. Conventionally, borehole tests are conducted to acquire subsurface geological information, and rock mass quality parameters such as rock quality designation (RQD). However, such tests are expensive, slow, limited to some specific locations, require more equipment, provide point measurements, and are hardly conducted in a steep topographic terrain. Generally, the non-invasive geophysical methods, such as geoelectrical methods are performed to assess the hard-rock sites. However, such parameters alone provide ambiguity in interpreting the geological units. In this work, a joint approach of four geophysical parameters (e.g., electrical resistivity (ER), self-potential (SP), magnetic, and induced polarization (IP)) is proposed to assess a highly heterogeneous terrain for the installation of the Accelerator Driven System (ADS) facility in South China. Geophysical data were obtained via a variety of surveyed parameters along different profiles. Electrical resistivity tomography (ERT) was integrated with borehole tests data to determine RQD for rock mass quality evaluation over the entire area. The results of magnetic interpretation were calibrated with well data to constrain the subsurface into distinct layers. ERT and magnetic coupled with Joint Profile Method (JPM) detected several localized faults/fractures. IP was integrated with ERT to distinguish between water and clay. Groundwater flow path was marked using SP. Compared with the past investigations, this novel approach deciphers functional analogous relation of RQD with ERT, magnetic, IP and SP in the hard-rock, provides thorough insights into the subsurface, removes the ambiguity caused by single geophysical parameter or the inadequate well data, and bridges the gaps between accurate geological model and the limited data for the development of engineering structures and groundwater resources.

Geophysical evaluation of geological model uncertainty for infrastructure design and groundwater assessments

Uncertainty in geological models, mainly caused by natural heterogeneity and inadequate data, often leads to substantial societal risk including failures of engineered structures, geohazards, and groundwater and environmental problems. These models mainly depend on geological knowledge about evaluation of rock mass quality, fractures/faults and clay. Traditionally, geotechnical parameters of rock mass quality classification, such as rock mass integrity index (Kv) are obtained by drilling tests. However, the boreholes approaches are time-consuming and expensive, provide limited data and have topographic constraints. Alternatively, the inexpensive integrated approaches of geophysical methods can reduce geological ambiguity by bridging the gaps between accurate subsurface models and inadequate borehole data. In this work, we incorporate electrical resistivity tomography (ERT) with inadequate drilling data, and determine Kv over large area even where no borehole exists. Our results reduce a large number of wells and evaluate the subsurface thoroughly using 2D/3D imaging of Kv. The ambiguity in water-clay differentiation caused by low resistivity was resolved by integrating ERT with induced polarization (IP). The integration of ERT, Kv and IP identified several localized fractures/faults. Our novel approach, compared with the past studies, reduces uncertainty in the geological model and provides a thorough insight into the subsurface for infrastructure design and groundwater assessments. Our approach is applicable in most cases.

Integration of Russian and foreign rock mass stability classifications for validation of mine support systems

This research aims to develop a methodology of selecting and validating support system designs for different purpose roadways by means of synthesizing the rock mass quality index Q and the stability categories which are in application in mines in Russia. The existing approaches to rock mass quality evaluation based on different combinations of complementary classification criteria are reviewed and generalized. It is found that the stability categories in use in Russian mines comply with the Q-index, which enables their flexible adaptation to specific features of a deposit and allows revealing of average mining conditions for different mines. The multi-factor rock mass quality rating and classification made it possible to justify mine designs per different categories. On this basis, the safe shapes and cross-section sizes are determined for the face entries for the prevalent geological conditions of ore mining under equicomponent (hydrostatic) stress state. The analysis and generalization of actual data from operating mines in the ore provinces in Yakutia, Russian Far East, Krasnoyarsk Krai and Eastern Kazakhstan allowed correlating the maximum permissible widths and cross-section shapes of roadways, as well as the roadway purpose and the rock mass quality by the Q-system by Barton. The revealed patterns of rock mass stability effect on the roadway roof span size made it possible to define rockbolting design variables for the face entries of operating mines in rock masses characterized by the widely ranging Q-index. The implementation of the integrated approach to rock mass stability estimation can improve reliability of the pre-project and project design solutions aimed to enhance safety and efficiency of roadways in different geological and geomechanical conditions of mineral mining.

Classification Method of Rock Structure and Rock Mass Quality of Surface Granite: Geological Disposal of High-Level Radioactive Waste in China

The engineering quality of rock mass is a key factor to evaluate the long-term stability and safety of high-level radioactive waste (HLW) geological disposal engineering and is also the important basis for disposal site selection. Traditional rock mass quality classification methods, such as RMR and [Formula: see text], can meet underground engineering but still should be studied further for the site evaluation in the HLW disposal engineering. In this study, rock mass structure rating (RMSG) was proposed based on the quantitative control index of rock mass structure which was from the rock mass quality classification methods. Based on the statistical results of rock mass structure, the relationship between the number of RMSG and the modified RMR ([Formula: see text]) and [Formula: see text] ([Formula: see text]) was established, China, as a case study. Results from this study show that RMSG is linearly related to [Formula: see text] and negatively exponential to [Formula: see text]. The research results can solve the evaluation of rock mass quality for HLW geological disposal engineering, and the addition of more engineering examples over time will enable further verification.

Geophysical Research on Rock Mass Quality Evaluation for Infrastructure Design

AbstractEvaluation of rock mass quality is necessary for the proper design of engineering infrastructures. Rock quality designation (RQD) is an important geotechnical parameter in the rock mass quality classification. However, given the limited data and natural heterogeneity, rock mass quality evaluation is a challenging task. Traditionally, the rock mechanical parameters are obtained from the drilling tests. However, the drilling tests only provide single‐point information and thus cannot provide detailed underground information. Moreover, boreholes are costly and time‐consuming, and still cannot be carried out in the steep topographic terrains. Alternatively, electrical resistivity tomography (ERT) is non‐invasive, cost‐effective, and user‐friendly, can provide 2D plane information even 3D information, and can bridge gaps between the available well data and the accurate subsurface geological model. In this research, we establish important empirical relations between ERT and the limited borehole data. We estimated RQD along with the ERT profiles and evaluated the subsurface for rock mass quality even where no borehole exits. Furthermore, the ambiguity of water/clay distinction in the geological model was removed via integration between ERT and induced polarization. Compared with the past geotechnical studies, our work provides far better insights into the subsurface via 2D/3D evaluation of rock mass quality for the development of the engineered structures. Therefore, geophysical research based on ERT can be very helpful in the hard rock terrains lacking sufficient mechanical well data.

Application of electrical resistivity tomography (ERT) for rock mass quality evaluation

Rock mass quality evaluation is a challenging task in geotechnical investigations given the natural heterogeneity and the limited data. These investigations mainly depend on the traditional drilling tests. However, such tests are expensive and time consuming, provide point measurements, and cannot be conducted in steep topographic areas, and thus cause uncertainties in the geological model. Conversely, geophysical methods such as electrical resistivity tomography (ERT) are non-invasive, user-friendly, and fast. In this work, we establish empirical correlation between ERT and limited drilling data to obtain rock mass integrity coefficient (Kv). The estimated Kv provides 2D/3D imaging of the rock mass quality evaluation via weathered/unweathered rock and faults detection in order to cover the entire area even where no drilling test exists. Compared with the past geotechnical investigations, our work reduce the ambiguities caused by the inadequate well tests and provide more accurate geological model for infrastructures design. Our work proposes that, in case of sparse borehole data, the established empirical equations can be used to determine Kv along different geophysical profiles via 2D/3D insight of the subsurface. Our approach is applicable in any hard rock setting, and the established correlations can be used in areas even where no well test exists.

Geophysical Research on Rock Mass Quality Evaluation for Infrastructure Design

Geophysical Research on Rock Mass Quality Evaluation for Infrastructure Design

Research on Rapid Evaluation of Rock Mass Quality Based on Ultrasonic Borehole Imaging Technology and Fractal Method

The distribution of discontinuities in the deep stratigraphic rock mass is random and irregular, and the spatial distribution has statistical self-similarity, which can be analyzed by fractal theory. The borehole wall image obtained based on the ultrasonic borehole imaging technology can clearly reflect the rich structural surface information such as the inclination, density, and crack width of the structural surface in the rock mass. Due to its inherent fractal characteristics, the fractal box dimension of the hole wall image can be used to describe the complexity of the structural surface. In the study, the fractal box dimension of the hole wall image and the RQD value of the rock mass are compared, and it is found that there is a linear correlation between the two. Therefore, the rock quality can be evaluated according to the fractal box dimension value of the hole wall image. Finally, a rapid quantitative evaluation method for rock mass quality classification is established based on ultrasonic borehole imaging technology and fractal method.

Advances in statistical mechanics of rock masses and its engineering applications

To efficiently link the continuum mechanics for rocks with the structural statistics of rock masses, a theoretical and methodological system called the statistical mechanics of rock masses (SMRM) was developed in the past three decades. In SMRM, equivalent continuum models of stress–strain relationship, strength and failure probability for jointed rock masses were established, which were based on the geometric probability models characterising the rock mass structure. This follows the statistical physics, the continuum mechanics, the fracture mechanics and the weakest link hypothesis. A general constitutive model and complete stress–strain models under compressive and shear conditions were also developed as the derivatives of the SMRM theory. An SMRM calculation system was then developed to provide fast and precise solutions for parameter estimations of rock masses, such as full-direction rock quality designation (RQD), elastic modulus, Coulomb compressive strength, rock mass quality rating, and Poisson’s ratio and shear strength. The constitutive equations involved in SMRM were integrated into a FLAC3D based numerical module to apply for engineering rock masses. It is also capable of analysing the complete deformation of rock masses and active reinforcement of engineering rock masses. Examples of engineering applications of SMRM were presented, including a rock mass at QBT hydropower station in northwestern China, a dam slope of Zongo II hydropower station in D.R. Congo, an open-pit mine in Dexing, China, an underground powerhouse of Jinping I hydropower station in southwestern China, and a typical circular tunnel in Lanzhou-Chongqing railway, China. These applications verified the reliability of the SMRM and demonstrated its applicability to broad engineering issues associated with jointed rock masses.

Joint Investigation and 3D Visual Evaluation of Rock Mass Quality

In order to realize the high efficiency quality classification and three-dimensional visualization of engineering rock mass and to solve the technical difficulties of the traditional rock mass quality evaluation method such as high labor intensity, long process time consumption, many intervention processes such as scale measurement and manual calculation, and nonintuitive classification results, this paper puts forward a 3D visual rock mass quality evaluation method and system based on close-range photography, which optimizes the traditional rock mass quality evaluation method, makes the rock mass classification three-dimensional and visible, and realizes the estimation of unrevealed rock mass quality evaluation index. The research results show the following: (1) The method of storing joint information by close-range photography and extracting joint information by human-computer interaction improves the working efficiency and the process is safe and controllable compared with the traditional method of collecting fracture parameters. (2) Based on the statistical analysis of 97 groups of roadway survey data, the comprehensive statistical regression formula between BQ value of Chinese national standard and RMR value is given, and there is a good correlation between BQ value and RMR value of rock mass quality index. (3) Based on the power-inverse ratio method, the three-dimensional model of rock mass classification of the mine was established, and the cutting model obtained the current distribution diagram of rock mass quality grade, providing scientific reference for drilling, blasting, support, and other production design optimizations.