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(R 2)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.

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 sta-bility 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 inter-vention.The findings presented here shall shed light for engineers/researchers interested in analyzing rock mass classification criteria or performing field investigation.

Classification and assessment of rock mass parameters in Choghart iron mine using P-wave velocity

Engineering rock mass classification,based on empirical relations between rock mass parameters and engineering applications,is commonly used in rock engineering and forms the basis for designing rock structures.The basic data required may be obtained from visual observation and laboratory or field tests.However,owing to the discontinuous and variable nature of rock masses,it is difficult for rock engineers to directly obtain the specific design parameters needed.As an alternative,the use of geophysical methods in geomechanics such as seismography may largely address this problem.In this study,25 seismic profiles with the total length of 543 m have been scanned to determine the geomechanical properties of the rock mass in blocks Ⅰ,Ⅲ and Ⅳ-2 of the Choghart iron mine.Moreover,rock joint measurements and sampling for laboratory tests were conducted.The results show that the rock mass rating（RMR） and Q values have a close relation with P-wave velocity parameters,including P-wave velocity in field(V;).P-wave velocity in the laboratory(V;) and the ratio of V;V;(i.e.K;= V;/V;.However,Q value,totally,has greater correlation coefficient and less error than the RMR,In addition,rock mass parameters including rock quality designation（RQD）,uniaxial compressive strength（UCS）,joint roughness coefficient（JRC） and Schmidt number（RN） show close relationship with P-wave velocity.An equation based on these parameters was obtained to estimate the P-wave velocity in the rock mass with a correlation coefficient of 91%.The velocities in two orthogonal directions and the results of joint study show that the wave velocity anisotropy in rock mass may be used as an efficient tool to assess the strong and weak directions in rock mass.

A generic method for rock mass classification

Rock mass classification(RMC) is of critical importance in support design and applications to mining,tunneling and other underground excavations. Although a number of techniques are available, there exists an uncertainty in application to complex underground works. In the present work, a generic rock mass rating(GRMR) system is developed. The proposed GRMR system refers to as most commonly used techniques, and two rock load equations are suggested in terms of GRMR, which are based on the fact that whether all the rock parameters considered by the system have an influence or only few of them are influencing. The GRMR method has been validated with the data obtained from three underground coal mines in India. Then, a semi-empirical model is developed for the GRMR method using artificial neural network(ANN), and it is validated by a comparative analysis of ANN model results with that by analytical GRMR method.

Application of rock mass classification systems to rock slope stability assessment:A case study

The stability of rock slopes is considered crucial to public safety in highways passing through rock cuts, as well as to personnel and equipment safety in open pit mines. Slope instability and failures occur due to many factors such as adverse slope geometries, geological discontinuities, weak or weathered slope materials as well as severe weather conditions. External loads like heavy precipitation and seismicity could play a significant role in slope failure. In this paper, several rock mass classification systems developed for rock slope stability assessment are evaluated against known rock slope conditions in a region of Saudi Arabia, where slopes located in rugged terrains with complex geometry serve as highway road cuts. Selected empirical methods have been applied to 22 rock cuts that are selected based on their failure mechanisms and slope materials. The stability conditions are identified, and the results of each rock slope classification system are compared. The paper also highlights the limitations of the empirical classification methods used in the study and proposes future research directions.

Real-time prediction of rock mass classification based on TBM operation big data and stacking technique of ensemble learning

Real-time prediction of the rock mass class in front of the tunnel face is essential for the adaptive adjustment of tunnel boring machines(TBMs).During the TBM tunnelling process,a large number of operation data are generated,reflecting the interaction between the TBM system and surrounding rock,and these data can be used to evaluate the rock mass quality.This study proposed a stacking ensemble classifier for the real-time prediction of the rock mass classification using TBM operation data.Based on the Songhua River water conveyance project,a total of 7538 TBM tunnelling cycles and the corresponding rock mass classes are obtained after data preprocessing.Then,through the tree-based feature selection method,10 key TBM operation parameters are selected,and the mean values of the 10 selected features in the stable phase after removing outliers are calculated as the inputs of classifiers.The preprocessed data are randomly divided into the training set(90%)and test set(10%)using simple random sampling.Besides stacking ensemble classifier,seven individual classifiers are established as the comparison.These classifiers include support vector machine(SVM),k-nearest neighbors(KNN),random forest(RF),gradient boosting decision tree(GBDT),decision tree(DT),logistic regression(LR)and multilayer perceptron(MLP),where the hyper-parameters of each classifier are optimised using the grid search method.The prediction results show that the stacking ensemble classifier has a better performance than individual classifiers,and it shows a more powerful learning and generalisation ability for small and imbalanced samples.Additionally,a relative balance training set is obtained by the synthetic minority oversampling technique(SMOTE),and the influence of sample imbalance on the prediction performance is discussed.

Applying rock mass classifications to carbonate rocks for engineering purposes with a new approach using the rock engineering system

Classical rock mass classification systems are not applicable to carbonate rocks,especially when these are affected by karst processes.Their applications to such settings could therefore result in outcomes not representative of the real stress-strain behavior.In this study,we propose a new classification of carbonate rock masses for engineering purposes,by adapting the rock engineering system(RES) method by Hudson for fractured and karstified rock masses,in order to highlight the problems of implementation of geomechanical models to carbonate rocks.This new approach allows a less rigid classification for carbonate rock masses,taking into account the local properties of the outcrops,the site conditions and the type of engineering work as well.

A novel approach to structural anisotropy classification for jointed rock masses using theoretical rock quality designation formulation adjusted to joint spacing

Rock quality designation(RQD)has been considered as a one-dimensional jointing degree property since it should be determined by measuring the core lengths obtained from drilling.Anisotropy index of jointing degree(AI_(jd))was formulated by Zheng et al.(2018)by considering maximum and minimum values of RQD for a jointed rock medium in three-dimensional space.In accordance with spacing terminology by ISRM(1981),defining the jointing degree for the rock masses composed of extremely closely spaced joints as well as for the rock masses including widely to extremely widely spaced joints is practically impossible because of the use of 10 cm as a threshold value in the conventional form of RQD.To overcome this limitation,theoretical RQD(TRQD_(t))introduced by Priest and Hudson(1976)can be taken into consideration only when the statistical distribution of discontinuity spacing has a negative exponential distribution.Anisotropy index of the jointing degree was improved using TRQD_(t) which was adjusted to wider joint spacing by considering Priest(1993)’s recommendation on the use of variable threshold value(t)in TRQD_(t) formulation.After applications of the improved anisotropy index of a jointing degree(AI'_(jd))to hypothetical jointed rock mass cases,the effect of persistency of joints on structural anisotropy of rock mass was introduced to the improved AI'_(jd) formulation by considering the ratings of persistency of joints as proposed by Bieniawski(1989)’s rock mass rating(RMR)classification.Two real cases were assessed in the stratified marl and the columnar basalt using the weighted anisotropy index of jointing degree(W_AI'_(jd)).A structural anisotropy classification was developed using the RQD classification proposed by Deere(1963).The proposed methodology is capable of defining the structural anisotropy of a rock mass including joint pattern from extremely closely to extremely widely spaced joints.

GREY CLASSIFICATION FOR EVALUATING THE STABILITY OF DANGEROUS ROCK-BLOCK MASSES

This paper introduces a grey classifica- tion method forevaluating the stability of dangerous rock- block masses according tothe Grey System Theory. This method is applied to the stability ofthe V~# dangerous rock- block masses of Qingjiang water conservancyproject, and better results are abtained. The method which isadvanced in the article is very single and practical, and it can meetall kinds of project's demands.

Kinematic Analysis and Rock Mass Classifications for Rock Slope Failure at USAID Highways

Rock slope kinematic analysis and rock mass classifications has been conducted at the 17^(th) km to 26^(th) km of USAID(United States Agency for International Development)highway in Indonesia.This research aimed to examine the type of rock slope failures and the quality of rock mass as well.The scan-line method was performed in six slopes by using a geological compass to determine rock mass structure on the rock slope,and the condition of joints such as persistence,aperture,roughness,infilling material,weathering and groundwater conditions.Slope kinematic analysis was performed employing a stereographic projection.The rock slope quality and stability were investigated based on RMR(rock mass rating)and SMR(slope mass rating)parameters.The rock slope kinematic analysis revealed that planar failure was likely to occur in Slope 1,3,and 4,the wedge failure in Slope 1 and 6,and toppling failure in Slope 2,5,and 6.The RMR rating is ranging from 57 to 64 and can be categorized as Fair to Good rock.The SMR rating revealed that the failure probability of Slope 3 was 90%,while it was from 40%to 60%for others.Despite the uniform RMR for all slopes,the SMR was significantly different.The detailed quantitative consideration of orientation of joint sets and geometry of the slope contributed to such differences in outcomes.

Coal mine roof rating(CMRR),rock mass rating(RMR)and strata control:Carborough Downs Mine,Bowen Basin,Australia

The rock mass rating(RMR)has been used across the geotechnical industry for half a century.In contrast,the coal mine roof rating(CMRR)was specifically introduced to underground coal mines two decades ago to link geological characterization with geotechnical risk mitigation.The premise of CMRR is that strength properties of mine roof rock are influenced by defects typical of coal measures stratigraphy.The CMRR has been used in longwall pillar design,roof support methods,and evaluation of extended cuts,but is rarely evaluated.Here,the RMR and CMRR are applied to a longwall coal mine.Roof rock mass classifications were undertaken at 67 locations across the mine.Both classifications showed marked spatial variability in terms of roof conditions.Normal and reverse faulting occur across the mine,and while no clear relationships exist between rock mass character and faulting,a central graben zone showed heterogeneous rock mass properties,and divergence between CMRR and RMR.Overall,the CMRR data fell within the broad envelope of results reported for extended cuts at Australian and U.S.coal mines.The corollary is that the CMRR is useful,and should not be used in isolation,but rather as a component of a strata control programme.

Prediction of rock mass rating using fuzzy logic and multi-variable RMR regression model

Rock mass rating system(RMR) is based on the six parameters which was defined by Bieniawski(1989)[1]. Experts frequently relate joint and discontinuities and ground water conditions in linguistic terms with rough calculation. As a result, there is a sharp transition between two modules which create doubts.So, in this paper the proposed weights technique was applied for linguistic criteria. Then by using the fuzzy inference system and the multi-variable regression analysis, the accurate RMR is predicted. Before the performing of regression analysis, sensitivity analysis was applied for each of Bieniawski parameters.In this process, the best function was selected among linear, logarithmic, exponential and inverse functions and finally it was applied in the regression analysis for construction of a predictive equation. From the constructed regression equation the relative importance of the input parameters can also be observed. It should be noted that joint condition was identified as the most important effective parameter upon RMR. Finally, fuzzy and regression models were validated with the test datasets and it was found that the fuzzy model predicts more accurately RMR than regression models.

Assessment of Rock Mass Quality and Deformation Modulus by Empirical Methods along Kandiah River, KPK, Pakistan

The pivotal aim of this study is to evaluate the rock mass characterization and deformation modulus. It is vital for rock mass classification to investigate important parameters of discontinuities. Therefore, Rock Mass Rating (RMR) and Tunneling quality index (Q) classification systems are applied to analyze 22 segments along proposed tunnel routes for hydropower in Kandiah valley, Khyber Pakhtunkhwa, Pakistan. RMR revealed the range of fair to good quality rocks, whereas Q yielded poor to fair quality rocks for investigated segments of the rock mass. Besides, Em values were acquired by empirical equations and computer-aided program RocLab, and both methods presented almost similar variation trend of their results. Hence, the correlations of Em with Q and RMR were carried out with higher values of the regression coefficient. This study has scientific significance to initially understand the rock mass conditions of Kandiah valley.

Updating of the hierarchical rock mass rating(HRMR) system and a new subsystem developed for weathered granite formations

The RMR system is still very much applied in rock mechanics engineering context. It is based on the evaluation of six weights to obtain a final rating. To obtain the final rating a considerable amount of information is needed concerning the rock mass which can be difficult to obtain in some projects or project stages at least with accuracy. In 2007 an alternative classification scheme based on the RMR, the Hierarchical Rock Mass Rating(HRMR) was presented. The main feature of this system was the adaptation to the level of knowledge existent about the rock mass to obtain the classification of the rock mass since it followed a decision tree approach. However, the HRMR was only valid for hard rock granites with low fracturing degrees. In this work, the database was enlarged with approximately 40% more cases considering other types of granite rock masses including weathered granites and based on this increased database the system was updated. Granite formations existent in the north of Portugal including Porto city are predominantly granites. Some years ago a light rail infrastructure was built in the city of Porto and surrounding municipalities which involved considerable challenges due to the high heterogeneity levels of the granite formations and the difficulties involved in their geomechanical characterization. In this work it is intended to provide also a contribution to improve the characterization of these formations with special emphasis to the weathered horizons. A specific subsystem applicable to the weathered formations was developed. The results of the validation of these systems are presented and show acceptable performances in identifying the correct class using less information than with the RMR system.

Source Rock Classification and the Basic Structure of Coal and Kerogen

In accordance with the confusion on classification of source rocks, the authors raised a source rock classification for its enriched and dispersed organic matter types based on both Alpern’s idea and maceral genesis/composition. The determined rock type is roughly similar to palynofacies of Combaz , whereas it is "rock maceral facies (for coal viz. coal facies)" in strictly speaking. Therefore, it is necessary to use the organic ingredients classification proposed by the authors so that it can be used for both maceral analysis and environment research . This source rock classification not only shows sedimentology and diagenetic changes but also acquires organic matter type even if hydrocarbon potential derived from maceral’s geochemical parameters. So, it is considered as genetic classification. The "rock maceral facies" may be transformed to sedimentary organic facies , which is used as quantitative evaluation means if research being perfect.Now, there are many models in terms of structure either for coal or for kerogen. In our opinion, whatever coal or kerogen ought be polymer, then we follow Combaz’s thought and study structure of amorphous kerogens which are accordance with genetic mechanism showing biochemical and geochemical process perfectly. Here, we use the time of flight secondary ion mass spectrometry (TOFSIMS) to expand Combaz’s models from three to five. They are also models for coal.

基于岩体质量的井筒保安矿柱优化与应用

为兼顾井筒稳定和减少资源的不必要浪费,在常规的岩层移动角井筒保安矿柱圈定法基础上,采用工程类比法,将Ⅱ级岩体质量围岩部分的井筒保安矿柱尺寸优化为80m,形成上部台体下部圆柱体的保安矿柱。同时对优化后的保安矿柱周边压覆资源采取控制采场顶板暴露面积和分层充填的保护性开采措施。数值模拟结果表明:按优化后的保安矿柱范围开采周边矿体后,地表井架、卷扬机房及井筒内壁变形量均未超过最大允许变形值,说明优化方案具有可行性。

基于BWO-RF模型的岩体质量评价方法

岩体质量分级是地下工程初期设计和施工的基础。为了更加高效准确地开展岩体质量评价,提出了一种基于白鲸优化(BWO)随机森林的岩体质量评价模型——BWO-RF模型,同时构建了麻雀搜索算法优化随机森林(SSA-RF)、粒子群优化随机森林(PSO-RF)和未优化随机森林(RF)的岩体质量评价模型进行对比。在模型构建前,建立了包含131组工程实例数据的数据库,运用该数据库最终完成了4种模型的训练和测试。基于模型测试结果,采用准确率、查准率、召回率、F1值和AUC值5个评价指标对模型进行对比优选。研究结果表明:BWO-RF模型各项评价指标均优于其余3种模型,具有更优的评价性能;经过工程实例验证,本研究所提出的BWO-RF模型预测准确率达90%,可为实际工程建设提供参考依据,具备实际工程应用价值。