Rock Mass Classification Research Articles

Rock slope stability analysis in Melasti Beach area – Ungasan, Bali

Melasti Beach is one of the main tourist attractions developed by the Bali Government. This beach is located in the southern area of Bali, precisely in Ungasan village, Badung Regency, Bali. At the moment, the region administrator is constructing a tourism facility by excavating a rock mass near the beach. Therefore, to avoid damage to nearby shops and beach facilities, a study is needed to be conducted about the safety of the rock mass that is being excavated. Direct observation of the rock mass and rock mass classification is conducted to obtain the physic and mechanical characteristics of the rock. To do the stability analysis of the slope, Geo-Studio 2012 software is used by inputting the data obtained from the field and laboratory data. The result of this study is a mass rock in Melasti Beach is classified as limestone which has a Fair rock classification and the factor of the safety of the rock slope/cliff is 1.631 which means the cliff is safe and is no need to build an extra reinforcement on the slope/cliff.

BILSTM-Based Deep Neural Network for Rock-Mass Classification Prediction Using Depth-Sequence MWD Data: A Case Study of a Tunnel in Yunnan, China

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.

Role of Exploration Geologists in Geotechnical Engineering of Open-Pit Mines

Abstract The process followed by geotechnical engineers when designing open-pit mine slopes requires a specialized knowledge of the geology of a prospective orebody and the host terrain. The sooner this information is obtained, the earlier that geotechnical engineers will be able to prepare conceptual slope designs, which will enable the deposit owner to assess whether the project is technically feasible and economically viable. The first information will usually come from early district- to mine-scale outcrop mapping and drilling by exploration geologists. To be of maximum use to geotechnical engineers, the data collected by exploration geologists must be recorded in a manner that supports the rock mass classification and strength assessment used by geotechnical engineers for mine design studies. The essentials that exploration geologists need to know when performing this task underscore why close working relationships between exploration geologists and engineering geologists are essential to improve the geotechnical outcomes in open-pit mines. Our objective is to encourage exploration geologists to extract the maximum mining-related data and information possible from rock outcrops and exploration drill holes, within the limitation of the exploration budget for discovery and mineral resource definition. The types of data that geotechnical engineers use in open-pit slope design include geologic, structural, rock mass strength, and hydrogeological data. Most exploration geologists are not trained as geotechnical engineers, and collecting geotechnical data of the quality required by engineers will be beyond the responsibility and experience of many of them. However, early discussion with a geotechnical engineer will assist exploration geologists in designing a drill core logging format that will collect the most important data that the engineer requires, within the constraints of discovering and defining the mineral resource.

Evaluation of slope stability through rock mass classification and kinematic analysis of some major slopes along NH-1A from Ramban to Banihal, North Western Himalayas

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.

„ხობი 2 ჰესის“ ჰიდროტექნიკური კომპლექსის სათავე ნაგებობის და ჰესის შენობის სამშენებლო მოედნის ამგები გრუნტების ფიზიკურ-მექანიკური თვისებები

Modern methods of rock evaluation, such as Rock Quality Index (RQD), Rock Mass Rating (RMR) and Rock Mass Classification System (Q), are used within the field of geotechnical surveys. RQD was determined by D.U. Deere in 1963, as a simple classification system of rock mass stability. While using RQD index five classes of rocks (A-E) are determined. Q value can be determined in different ways: during mapping in underground excavations, on the surface or alternatively – on basis of core description. The most accurate values are obtained during underground geological mapping. Dividing the underground excavations into several parts might be required during mapping, so that variation of Q value is moderate in each section, meaning that such variation should not exceed the rock class variation index according to the reinforcement scheme. During excavation works single blasting often represents natural section for individual mapping. A variation may take place in sections of several meters, but for showing this variation histograms can be used during mapping.

A Comprehensive Review of Mechanisms, Predictive Techniques, and Control Strategies of Rockburst

Brittle failure, also known as rockburst, is a violent phenomenon that occurs during the excavation of hard rock in areas with high geostress, particularly at great depths. The induced hazards have been a global concern since the first half of the 20th century, leading to extensive efforts to understand the mechanisms and develop effective approaches to predict and control such rock mass failures. To understand the current state of research and future prospects of rockburst, a comprehensive review of past developments has been conducted. Firstly, we briefly summarize different definitions and types of rockburst. Next, we delve into the various technologies and methods employed in rockburst research and analyze the current knowledge on the mechanisms, classifications, and prediction of rockbursts. Then, we examine the existing methods for preventing and controlling these events. Despite the advancements made, the relationship between the behavior of rock masses at different scales, both in the laboratory and real-world conditions, remains an area of challenge. Additionally, the absence of rock mass classifications under high in-situ stress and lack of accuracy in rockburst prediction methods remain persistent issues that need to be addressed. This study delves into cutting-edge rockburst research, illuminating potential ground support strategies and empowering the mining industry in order to unleash innovative solutions for controlling these explosive phenomena.

A Hybrid Methodology of Rock Support Design for Poor Ground Conditions in Hard Rock Tunnelling

The design of rock support in hard rock tunnelling is often assisted by the use of empirical engineering rock mass classification systems. This has involved support design in a wide variety of geological- and rock mechanical-conditions, including poor ground conditions like fault zones and weak rocks. For most of the encountered situations, the empirical support designs have performed well. However, the empirical classification systems pose several limitations to describe ground behavior and derive optimal rock support design in poor ground conditions as discussed in several publications during the last decades. In the present study, an analysis of 118 case records, mostly from monitored Norwegian tunnel sites, has been undertaken to investigate the performance of the current empirical design practice with the Q-system when exposed to poor ground conditions. The results have revealed significant improvement possibilities which relate to a wide variety of ground conditions. Among others, weak rocks, anisotropic rock, and rock mass conditions in the transition from self-bearing rock masses to rock masses requiring load-bearing support in the tunnel, and typically with rock mass quality Q < 1. As a result, a set of improvements and design recommendations have been developed and an integrated or hybrid design methodology specific for poor ground conditions proposed. The hybrid approach combines the main components and advantages of different design methodologies and provides guidance for design optimization where the empirical approach presents limitations.

Stability Analysis of Rocky Slopes on the Cuenca–Girón–Pasaje Road, Combining Limit Equilibrium Methods, Kinematics, Empirical Methods, and Photogrammetry

The 3D point clouds obtained from the low-cost, remote, and precise SfM (Structure from Motion) technique allow the extraction and acquisition of discontinuities and their characteristics both manually, with the compass and virtual ruler of the Cloud Compare software, and automatically with the DSE (Discontinuity Set Extractor) program, which is faster, more accurate, and safe. Some control plans have been used, which basically consist of identifying one or several fractures and taking measurements on them manually and remotely. The difference between both types of measurements is around 5°, which we believe is reasonable since it is within the precision and repeatability of measurements with a geologist’s compass. This work analyzes the stability of six slopes (five excavated and one natural) by applying five different analysis methodologies based on the rock mass classification system (SMR, RHRSmod, and Qslope), kinematic analysis, and analytical analysis (limit equilibrium). Their results were compared with what was observed in the field to identify the most appropriate analysis methodologies adjusted to reality. The necessary parameters for analyzing each of the slopes, such as orientation, quantity, spacing, and persistence of the discontinuities, were obtained from the automatic analysis. This type of analysis eliminates the subjectivity of the authors, although the findings are related and resemble those obtained manually. The main contribution of the article consists of the application of fast and low-cost techniques to the evaluation of slopes. It is a type of analysis that is in high demand today in many Andean countries, and this work aims to provide an answer. These methodologies suggested by scientific articles such as this one will later be integrated into some procedures and will be taken into account by technical reports. The results show that with the available information and by applying low-cost techniques, the SMR system is the methodology that presents the best results and adjusts better to the reality of the study area. Therefore, SMR is a necessary parameter to determine rockfall hazards through modified RHRS.

Quantitative granitic weathering assessment for rock mass classification optimization of tunnel face using image analysis technique

Weathering degree is one of the key criteria used to determine tunnel support with the main difficulty is in assessing the weathering grade of the tunnel face. This study applied CIEL*a*b colour space and image analysis to a rock mass tunnel face. The authors used the Japanese Highway (JH) Rock Mass Classification System to classify the rock mass tunnel face. JudGeo converted the RGB photographs to L*a*b values. This study compared the quantitative evaluation system's performance to manual tunnel face mapping by geologists using six JH Rock Mass Classification samples (Class B, CI, CII, DI, DII, and E). The analysis showed good correlations between four (B; CI; CII; DI) of six rock mass classes. The tunnel face mapping showed two rock mass classes above grade (DII and E). This technique can effectively identify the degree of weathering grades of the tunnel face, especially for fresh to moderately weathered tunnel faces.

Determination of Support Systems According to Rock Classes-Tlemcen-Akkid Abbas High Speed Railway Project-Algeria

Tüneller için yapılan kaya kütle sınıflamalarında karşılaşılan en büyük sorunlardan birisi, tünel kesitinde farklı birimler geçilmesi durumunda hangi desteklerin uygulanacağıdır. Tünel kesitinde üst yarıda zayıf birim ile karşılaşılırken alt yarı ve invert kesimi ise sağlam birimde yer alabilmektedir. Bu durumlarda ise kaya kütlesi en kötü koşullara göre belirlenirken, destek sisteminde ise farklılıklar olabilmektedir. Üst yarıda ağır destek sistemi gerekirken invert kesiminin sağlam birimde kalması durumunda invert kazısı yapılmayabilir. Ayrıca zayıf zeminlerde açılan tünellerde karşılaşılan sorunlar genelde sıkışma mekanizmasına bağlı olarak tünel destek sistemlerinde yenilmeler ile tünel aynasında karşılaşılan stabilite sorunları olarak karşımıza çıkmaktadır. Zayıf zeminlerde genelde destek sistemlerinin tamamlanmasından sonra uzun dönemde yenilmeler meydana gelmektedir. Sığ tünellerde ise, tünel çevresinde kemerlenme sağlanamaması sebebi ile destek sistemlerimin tasarımı daha kritiktir. Zira deformasyonlara müsaade edilmesi tünel çevresinde yüzeye kadar etkileyecek deformasyonları tetikleyebilecektir. Ayrıca tünel aynasında kazı esnasında oluşabilecek bir yenilmenin etkisi yüzeye kadar obruk şekilde yansıması olacak ve tüm tünel destek sistemlerini etkileyecektir. Bu sebeple sığ örtü altında zayıf zeminlerde açılan tünellerde deformasyonlara müsaade edilmeden destek sistemleri tamamlanmalıdır. Ayrıca tünel aynasında herhangi bir göçüğün oluşmaması için ayna destek sistemlerinin yapılması zorunludur. Bu çalışma kapsamında Telmcen Akkid Abbas Yüksekl Hızlı Demıryolu Projesinde açılan T10 tüneli destek sistemleri ve tünel tasarımı incelenmekedir. Yapılan çalışma sonucunda zayıf zeminlerde tünel destek sistemlerinin belirlenmesinde ayna ve tavan stabilitesinin son derece önemli olduğu görülmüştür. Ayrıca uzun dönemde sıkışma sorunlarının önüne geçmek için tünel destek sistemi basıncı belirlenirken, kritik destek sistemi basıncından en az 2-3 kat yüksek olması gerektiği görülmüştür.

Statistical study of squeezing for soft rocks based on factor and regression analyses of effective parameters

The time-dependent deformation of rocks due to stress released by excavation is referred to as squeezing. Accurate evaluation of the squeezing at the design stage can dramatically reduce technical problems and the financial costs of underground structures. Although various methods are presented to predict tunnel squeezing at the preliminary stage, being site-specific and incorporating incomplete databases are deficiencies of the available procedures. In this study, based on a comprehensive literature review, we prepared a database of tunnel squeezing for soft rocks, including possible effective parameters. Statistical processing methods such as univariate, reduction, and cleaning were employed to improve the statistical quality of the database. The statistically-processed datasets were also validated based on various scales such as accuracy, convergence, and usefulness. Significant predictors of squeezing are recognized as the ratio of strength to stress and the rock mass classification system. New squeezing criteria were developed using binary and multi-class regression methods to predict the squeezing occurrence and intensity of soft rocks. The results are confirmed by a Multilayer Perceptron Feed-Forward Neural Network and are compared to well-known empirical equations. The developed equations are more accurate comparing the empirical equations used to predict the squeezing of soft rocks. This methodology can be utilized at the design stage for another database to predict squeezing rocks for topographic-stress and tectonic-stress-based cases.

Characteristic Impedance and Its Applications to Rock and Mining Engineering

The characteristic impedance of a rock is defined as the product of the sonic velocity and the density of the rock. Based on previous studies, this article finds that: (1) For an intact rock, its characteristic impedance is a comprehensive physical property, since it is closely related with strengths, fracture toughness, Young’s modulus, and Poisson’s ratio. (2) For rock masses, their characteristic impedances either increase markedly or slightly with increasing depth. (3) The bursts of intact rocks in laboratory are dependent on their characteristic impedances to a great extent, and strong rock bursts happen mostly in the rocks with large characteristic impedance. (4) Rock burst occurrence in tunnel and mines has a close relation with the characteristic impedances of the rocks. (5) Laboratory experiments on different rock samples show that seismic velocity increases as applied stress rises, and field monitored results from coal mines indicate that in the areas where rock bursts happened, the seismic velocity was increasing markedly before or during the bursts. (7) Drillability of rock depends on the characteristic impedance of the rock and the rock with larger impedance has lower drillability or lower penetration rate. (8) The potential applications of characteristic impedance include evaluation and classification of rock masses, and prediction of rock burst proneness and drillability.

Prediction of rock mass class ahead of TBM excavation face by ML and DL algorithms with Bayesian TPE optimization and SHAP feature analysis

In this paper, field construction data from the Singapore Metro Line project were used to study the mapping relationship and establish the prediction model between TBM operation data and the ground condition ahead of the excavation face. The study presents a multi-classifier competition mechanism to construct ten separate classifiers, including logistic regression, support vector machine, random forest, extremely randomized trees, adaptive boosting machine, extreme gradient boosting (Xgboost), light gradient boosting (LightGBM), categorical boosting, long short-term memory and convolutional neural network. The acquired data were used to select 28 key TBM operating parameters by a correlation-based feature selection method, and the selected parameters in the stabilization phase after removing the outliers were calculated as the input to the classifier, and a relatively balanced training set was obtained by the synthetic minority oversampling technique. The hyperparameters of each classifier were optimized using tree Parzen estimator Bayesian optimization. The prediction results show that LightGBM presents the best results among ten different machine and deep learning algorithms with an accuracy of 96.22%, precision of 96.94%, recall of 97.33% and F1-score of 97.33%. In addition, the effect of the input parameters of the LightGBM model on the prediction accuracy of the model was analyzed using Shapley additive explanations, and the effect of sample imbalance on the prediction performance was discussed.

A detailed study of the CHN-BQ rock mass classification method and its correlations with RMR and Q system and Hoek-Brown criterion

A CHN-BQ method is firstly defined to present the system and connotation of the current Chinese national standard “Standards for Engineering Classification of Rock Mass” GB/T 50,218. The core idea and content of the CHN-BQ method are studied in detail. Based on a qualitative classification of rock masses at the sites of in situ rock deformation and shear strength tests with a total number of 1439 test sets in nearly 90 projects, the statistical characteristics of in situ test results of different grades of rock mass are analyzed; Thus, the mechanical parameters corresponding to each grade of rock mass basic quality are proposed. Based on 997 groups of various rock mass classification data in 10 projects, the comprehensive CHN-BQ method verifies, and some essential remarks are obtained. Firstly, the 312 sets of parallelly measured data show that the consistency of less than one-grade discrepancy of the qualitative and quantitative classification in the CHN-BQ method is up to 71% in percentage. Secondly, a highly linear correlativity of the CHN-BQ method with the RMR method is found, and a linear regression equation is proposed. Thirdly, a logarithmic regression equation between the CHN-BQ method and the Q system is obtained, and equivalent grading divisions of the Q system from 9-level intervals towards traditional 5-level intervals are proposed. Finally, the research shows that the GSI concept in the Hoek-Brown criterion and the qualitative classification of rock mass structure in the CHN-BQ method are essentially coincident. Based on the measured data in quartz mica schist rock of an arch dam project, a linear regression equation of the Kv index in the CHN-BQ method and GSI index in the Hoek-Brown criterion is obtained, and the comparison of the strength parameters of the CHN-BQ method and Hoek-Brown criterion is discussed.

Analyzing Support Stability of Deep Shaft Based on Plastic Softening and Dilatancy of Hard Rock Mass

To explore the stability analyses and control methods for surrounding rocks in deep hard rock shafts, this paper is based on field engineering geological surveys and laboratory rock mechanics tests and relies on the main shaft being constructed in the Shaling Gold Mine of China as the engineering background. The quality of the rock mass is evaluated by the Q system, rock mass rating (RMR) and geological strength index (GSI). The mechanical parameters of the surrounding rock mass of the shaft are calculated by using the generalized Hoek–Brown failure criterion, and the main support system is determined based on the rock mass classification system. Based on the finite element method, a two-dimensional plane strain model is constructed to analyze and evaluate the deformation and plastic region range of surrounding rocks for different support systems. On this basis, considering the dilatancy and plastic softening characteristics of hard rock masses, an analytical solution of the stress, strain and plastic region radius of hard rock around shafts in homogeneous media is proposed. Finally, the plastic region of the surrounding rock is measured by the P-wave velocity test method. The results show that after considering the dilatancy and plastic softening characteristics of the rock mass, the numerical simulation, theoretical analytical solution and measured results are basically consistent, and the proposed support system can effectively ensure the stability of the shaft.

Analisis Kelas Massa Batuan Terhadap kestabilan Lereng Bendungan Lolak Menggunakan Metode Rock Mass Rating

The process of displacement or movement of slope stability constituent materials followed by the sedimentation process of the deposited material is a landslide event. The negative impact of the landslide if it occurs in residential areas, can cause material and infrastructure losses, or economic losses in the affected area if the landslide products can cover the road access. The purpose of this study was to determine the level of rock mass and its effect on slope stability, and to determine the rock mass quality of the study area. The research area is located in Lolak Dam. Based on the results of the analysis carried out, the conclusion is that the observations starting from station 1, station 2, station 3, and station 4 show moderate-good rock strength. The condition of the discontinuity shows continuity of less than 1 meter, the aperture of discontinuity are open to a bit wide, with varying roughness, namely coarse, and slightly coarse, with the infilling material calcite veins. The level of weathering starts from weathered to very weathered. Groundwater conditions are dominated by discontinuities that have a dry level. Based on the discontinuity characteristic, station 1 has good rock mass class, station 2 has medium rock mass class, station 3 has medium rock mass class, and station 4 has good rock mass class. this value can be used as a reference value for the next stage of decision making regarding the strengthening of the slopes in the study area.

A real-time intelligent classification model using machine learning for tunnel surrounding rock and its application

ABSTRACT Real-time and accurate prediction of surrounding rock grade is crucial for tunnel dynamic construction and design. However, the internationally accepted semi-quantitative methods (e.g. rock mass rating (RMR), Q, and basic quality (BQ)) cannot provide fast and accurate classification in construction. This study proposed an intelligent surrounding rock classification method and a tunnel information management system, which can predict the surrounding rock grade in real-time and accurately. A database is collected with 286 cases in China, including seven geological parameters and surrounding rock grades. Based on different training parameters, 12 classification models are established using VGGNet, ResNet, and support vector machine (SVM) algorithms. The accuracy of the SVM classifier is 93.02%, which performs better than the VGGNet and ResNet classifiers. Moreover, precision, recall, F-measure, receiver operating characteristic (ROC), and 20-case verification show that the SVM classification model has greater robustness in learning and generalising for small and imbalanced samples. Additionally, a tunnel information management system is developed with cloud technology, which can accurately predict the surrounding rock grade within 10 s. Overall, the achievements of this study can provide valuable references for real-time rock mass classification in traffic tunnels and underground powerhouses.

Multi-Rock Mass Classification Systems for the Assessment of Excavation Method and Support Design of Spillway Tunnel, Sidan Dam, Bali, Indonesia

This paper presents the implementation of rock mass classification systems to determine the excavation method and the support design of the Spillway Tunnel, Sidan Dam, Bali, Indonesia. In order to mitigate the uncertainty associated with using single rock mass classification during designing tunnels, this study provides an evaluation by applying multiple rock mass classifications. Rock Mass Rating (RMR), QSystem, Geological Strength Index (GSI), Japan Society of Civil Engineering (JSCE), and Rock Mass Index (RMi) were among the rock mass classification systems used for comprehensive analysis. Rock mass parameters were determined based on engineering geological mapping and rock &amp; soil core analysis. The rock mass quality of pyroclastic rock along the tunnel is generally poor to fair. Based on the GSI rating, digging and ripping excavation were recommended. The top heading with benching (1-3 m advance in top heading) was the proposed excavation method by RMR and JSCE. Each classification system advised the combination of reinforced shotcrete and rock bolt for the primary tunnel support with varying shotcrete thickness (30-400 mm), rock bolt length (2.6-6 m), and rock bolt spacing (1-2.5 m). However, field investigations during excavation and numerical stability analysis were recommended to eliminate risks and ensure safe construction.

Geotechnical Investigation and Monitoring of Underground Excavation of Tunnel-2 in Srisailam Left Bank Canal Tunnel Project (AMRP), Nalgonda District, Telangana, India

Construction stage geotechnical investigation for tunnels within rocks includes detailed engineering geological mapping of excavated strata and its subsequent rock mass classification for the purpose of selection and installation of suitable supports. Monitoring of ground deformations during excavation of rock tunnels serves as the principal means of verifying the design of tunnel supports and stability of the excavation. Geology of the area comprises of Archean gneissic complex with basic intrusives overlain unconformably by the Srisailam Quartzites. The contact of basic intrusives with granite reaches was expected to be fractured and sheared and identified as adverse geological conditions and may warrant instrumental monitoring. In this work, the underground excavation of Srisailam Left Bank Canal (SLBC) Tunnel2, excavated in the Archean gneisses and granites was mapped by face mapping and 3D geological logging on 1:250 scale during construction stage, for the entire length of 7121Mresulting in identification of Chainages 2776M, 3200M and 4170M for field monitoring. Field instrumentation comprising of MPBX/SPBX, Load cells were installed at the adverse locations. The ground deformations were found to have attained stability with the installation of primary supports comprising shotcrete 50MM thick and pattern rock bolts 4M long full grouted for Rock Mass Class III (Fair Rock), shotcrete 100MM thick and pattern rock bolts)/ Steel Ribs with back fill concrete for Rock Mass Class IV and V(Poor Rock and Very Poor Rock). Keywords: Geological Investigation, Rock Mass Classification, Excavation Monitoring, Ground Deformation, Tunnels, Nalgonda District

ASSESSMENT OF ROCK SLOPE STABILITY IN A HUMID TROPICAL REGION: CASE STUDY OF A COAL MINE IN SOUTH KALIMANTAN, INDONESIA

PT.X, a coal mining company in South Kalimantan, Indonesia, plans to use the highwall mining method to excavate marginal reserves on the final slope to maintain production. However, the stability of the slope and determination of the highwall mining dimensions are major concerns due to unfavourable rock mass conditions caused by intensive weathering and tectonics. This paper aims to evaluate the feasibility of highwall mining in the study area using empirical, analytical and numerical methods. The innovation of this research is the integration of these methods, which include rock mass classification, analytical calculation of load and rock support strength, 2D and 3D numerical modelling, and estimation of recovered coal from the highwall design. The initial condition assessment using rock mass classification and analytics calculation of the mining geometry model determined mine openings and pillar dimensions. Numerical modelling re-evaluated the geometry models to obtain an optimal design. The suggested optimal thickness, mine opening, web pillars, and barrier pillars are 3.20, 3.00, 3.50, and 4.00 m, respectively, with four web pillars in one panel at Seam-C and 2.50, 3.00, 3.50, and 4.00 m with four web pillars in one panel at Seam-D. The recovery of coal for Seam-C and Seam-D is estimated to be 40.54%. Deformation was found to have the closest relationship with the dimensions of the mine opening, and the safety factor is most sensitive to changes in the depth of the mine opening. This study provides a reference for future highwall mining in Indonesia and other regions with similar conditions.