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Source publication
Rock Mass Classification is the process of placing a rock mass into groups or classes
on defined relationships (Bieniawski, 1989), and assigning a unique description (or
number) to it on the basis of similar properties/characteristics such that the behavior of
the rock mass can be predicted. Rock mass is referred to an assemblage of rock material
s...
Contexts in source publication
Context 1
... rock mass classification schemes that are often used in rock engineering for assist- ing in designing underground structures are RMR, Q and GSI systems. Some well- known systems are listed in Table 1. ...
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... is used for analyzing the roof support, and height of the wall is used in case of wall support. The value of ESR (Table 10) depends upon the intended use of the excavation and the degree of its safety demanded (Singh and Geol, 1999). ...
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... of Modulus Ratio (MR) for different rock types are presented by Hoek and Diedrichs (2006) as shown in Table 12. ...
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... discussed earlier that, several other classification systems have been developed, some of them are listed in Table 1. Two of them are briefly discussed for their unique application in a certain aspect. ...
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... other words, it denotes uniaxial compressive strength of the rock mass in MPa and is ex- pressed as c RMi JP where σc is the uniaxial compressive strength of the intact rock material in MPa. JP is the jointing parameter; composed of 4 joint characteristics, namely, block volume or joint density, joint roughness jR (Table 13a), joint size jL (Table 13b) and joint altera- tion jA (Table 13c). JP is reduction factor representing the effects of jointing on the strength of rock mass. ...
Context 6
... other words, it denotes uniaxial compressive strength of the rock mass in MPa and is ex- pressed as c RMi JP where σc is the uniaxial compressive strength of the intact rock material in MPa. JP is the jointing parameter; composed of 4 joint characteristics, namely, block volume or joint density, joint roughness jR (Table 13a), joint size jL (Table 13b) and joint altera- tion jA (Table 13c). JP is reduction factor representing the effects of jointing on the strength of rock mass. ...
Context 7
... other words, it denotes uniaxial compressive strength of the rock mass in MPa and is ex- pressed as c RMi JP where σc is the uniaxial compressive strength of the intact rock material in MPa. JP is the jointing parameter; composed of 4 joint characteristics, namely, block volume or joint density, joint roughness jR (Table 13a), joint size jL (Table 13b) and joint altera- tion jA (Table 13c). JP is reduction factor representing the effects of jointing on the strength of rock mass. ...
Context 8
... overall rating of RMi and the classification is presented in Table 14. For irregular joints, a rating of jR = 5 is suggested *for filled joints: jR = 1; ** for slickensided joints the value of R depends on the presence and outlook of the striations; the highest value is used for marked striations *Based on joint thickness division in the RMR system (Bieniawski, 1973). ...
Citations
... These parameters are crucial in understanding and assessing the stability and behaviour of rock mass [2,3]. The rock mass classification systems including rock mass rating (RMR) [4], new RMR for tunnelling and Mining [5], The tunnelling quality index (Q) [6], rock mass index (RMi) [7], geological strength index (GSI) [8] are widely used in geotechnical projects. The RMR is mostly used in slope stability, but a modified RMR were proposed by Bieniawski for tunnelling and mining projects [9]. ...
... As the UCS and durability index were not considered in calculation of GSI, while RMR 14 incorporate these parameters as well [10]. Considering the fact that some of the intact properties of rock mass and discontinues are not included in GSI, while the RMR considered these properties [30] the effect of UCS, RQD and discontinuities has been presented from Equations (3) and (4). It was observed that UCS and RQD played positive impact while J v shows negative impact in the correlation. ...
In this study, efforts were made to incorporate the influence of discontinuities and failure modes of rock into the classification of rock masses. The past tectonic activities may create microfractures in the rock body therefore the failure moods have been determined carefully under uniaxial compression. The results of the discontinuity analysis, conducted through kinematic study, highlighted the significant impact of wedge failure on the failure of the rock mass. In correlating the geological strength index with rock mass rating, it was observed that joint volume played a negative role, whereas compressive strength played a positive role. These correlations are particularly applicable for a certain rock type, as the compressive strength is inherently dependent on the type of rock. The analysis of failure modes under uniaxial compression reveals that the dissipation energy coefficient initially undergoes rapid increase before reaching its minimum value at the failure stage. The microstructures of the rock effect significantly the elastic and dissipation energy characteristics. Specifically, the axial splitting failure mode emerges as predominant. Given the area's past tectonic activity, these results emphasize the impact of microfractures within the rock body. Relating the failure criteria with the chemical composition of rock types reveals that rocks abundant in SiO2, such as gabbronorite, tend to exhibit brittle failure. Additionally, a dominance of Al2O3 over Fe2O3 suggests a predisposition towards brittle failure, while an increased ratio of CaO to MgO implies increased susceptibility to compression.
... After this many classification systems have been developed from single-parameter to multiple-parameter schemes. The details of the different rock mass classification systems developed can be studied in [6,7]. Among these Rock Mass Rating (RMR) [4], Q system [8], and GSI [9,10] are the ones used extensively in tunnel engineering. ...
... It does this using intelligent software that uses the inputs to train a model and achieve the desired result, replicating learning [21]. Machine learning is a subfield of artificial intelligence whereas Deep Learning (DL) comes under ML [6]. AI particularly focuses on understanding and synthesizing intelligence in an agent-based approach. ...
... The recent developments in artificial intelligence algorithms have opened up the possibility of addressing issues across multiple disciplines. One can envision effective automatic rock mass categorization processes in the tunneling industry by advancing these applications [6]. In the field of tunneling, these advancements have led to the development of effective automatic rock mass categorization processes. ...
Among all of the subsystems and components of hydropower, the tunnel is one of the crucial structures that face most of the pre-operation, real-time and post-operation challenges. Tunnels used as a water conveyance system in the majority of the projects lead to significant importance in the hydropower sector. The engineering rock mass classification is a crucial step in the accomplishment of underground constructions, particularly tunnels and caverns within the rock mass. The quality of rock mass parameters must be assessed and predicted with greater accuracy. However, the difficulties lie in the proper assessment of these parameters to define their quality. Studies have shown that despite the use of numerical and empirical methods in construction practices, numerous problems are reported when it comes to dealing with such structures. Over the years, various approaches based on machine learning (ML) techniques have been used for the minimization of these difficulties. As in recent trends, the development of machine learning technologies is currently bringing new methodologies in assessing rock mass classification. Although, because a sufficiently large database is required for such initiatives, there are a number of difficulties in their practical execution. Thus, a review of studies on the use of machine learning techniques in rock mass classification is presented in this paper. To determine the future directions of ML in RMC, it presents a comprehensive assessment to examine how ML models are being used. This study offers recommendations for creating an industry-specific model focusing on the strengths and weaknesses of ML techniques
... The objectives and constraints essentially constitute the degree of flexibility in which the project can operate and can be the project management boundary conditions. [1][2]. ...
Over 40% percent of the accidents encountered at Unki Mines are related to ground failure and other geotechnical complications. This research sought to address the monetary losses incurred in revenue and productivity through absenteeism and injury. With the development of mine production in Zimbabwe, the depth of mines gradually increased, and the ecological environment developed complex conditions. Deep mining unlike shallow mining is characterized by extra ground pressure, more gas, and faster deformation rates. These factors affect the safety of mining production. Therefore, as the mining depth and breadth increase, the difficulty of mine rock engineering is also increasing. The deepening of mining depth and the improvement of the mechanization level have brought increasing difficulties regarding the stability of surrounding rock hence risk issues arise. The ultimate objective of this study was to ensure a robust design of support systems at Unki Mine that would eventually reduce the risks associated with rock engineering excavations. Findings from the study and its analysis established the following conclusions. On the fore is the fact that hanging wall instability at Unki Mine are predominantly governed by geological and span attributes. The computed k value is 7% less the k value of 57.33 MPa used for existing pillars. An analysis of FOS and its relationship with recovery and Pillar W/H ratio shows that over-break has a huge impact on the span stability hence the effect of the ANFO need to be reviewed for an alternative explosive that ensures recovery within 80% range to ensure that the FOS is maintained above 1.6. A decrease in FoS increases the probability of failure, hence it is also important to device a pillar support system that is less prone to effects of over-break since the ground conditions are poor.
... Rock mass classification systems provide common basis for communication and used for stability analysis based on most integral and structural parameters. They can be applied to determine deformability of rock mass, their strength, slope stability assessment, in construction purposes also (Taherniya et al. 2014;Romana et al. 2015;Abbas and Konietzky 2015;Mondal et al. 2016). With time, advance numerical stimulations and monitoring tools were also used by Jing and Hudson (2002), Naithani (2007), Umrao et al. (2011), Singh and Tamrakar (2017), Vishal et al. (2017), Regmi et al. (2016), Verma et al. (2016), Li and Xu (2016), Kumar et al. (2017) and Siddique (2018) for more precision and quantification. ...
... But as RMR is treated with adjustment factors (F 1 , F 2 , F 3 and F 4 ) to find Slope Mass Rating, the slope is found to be unstable with bad rock mass (Table 7). Hence, based on the RMR and modified SMR approach, the angle of internal friction and cohesion is detected to be 37.5º and 2.8 kg/cm 2 (BIS 13,365 (Part 1), 1998aBieniawski 1979Abbas and Konietzky 2015). ...
... These RMR values after treatment with adjustment factors give SMR as 23.87 (Table 7) falling in class IV with bad rock quality and unstable conditions. Using these values, angle of internal friction and cohesion of the slide zone are 35º and 2.1 kg/cm 2 , respectively (Bieniawski 1979;Abbas and Konietzky 2015). Hence, the intersection of joints (Ψ s ), slope angle (Ψ f ) and angle of internal friction (ϕ) are 46º, 49º and 35º, respectively, and are fulfilling the condition of wedge failure. ...
The Lesser Himalayan belt, being the most complex litho-tectonic units of Himalaya, is in frequent danger of slope destabilisation. The present study intends to assess geotechnical characteristics of identified perilous slide zones for their detailed slope stability analysis in one of the highly complex structural entities of Lesser Himalaya, named as Purola schuppen zone. It involves imbricate thrusting and embraces a number of slide zones in weathered quartzite, phyllite, schist and shales. On the basis of discontinuity characteristics, viz. their orientations and interrelationship with slope, structural attributes and debris materials, slide zones are showing planar, wedge or circular failure patterns. Kinematic analysis is attempted to identify the possible mode of structurally controlled failure in rock mass. As per their failure types, these slide zones are analysed for stability using geotechnical parameters evaluated in lab. For circular failure zones, angle of internal friction and cohesion, are attained quantitatively by direct shear test and varying from 30.8° to 36.8° and from 0.04 to 0.41 kg/cm2, respectively. In case of planar and wedge failures, modified slope mass rating technique is used that assigns class V status for Kothgaon slide zone indicating very bad rock mass and unstable to completely unstable slope conditions with probability of failure as high as 0.9 while Taluka slide is partially stable with normal rock mass falling in class IV. Factor of safety, estimated by circular failure charts using slope, soil and strength characteristics, is varying from 0.75 to 1.50 in dry conditions, signifying critical stable state while it reduces less than unity in wet conditions as saturation upsurges due to nearby percolation of rainwater through discontinuities and cracks. Imbricate thrust zone, substantial weathering and jointing in rock masses, steep slopes and nearby vicinity to stream channels are major contributory reasons of instability in the area, while road widening and rainfall are acting as catalysts prompting the failure.
... Most of the classification schemes work with multiple parameters including intact rock strengths, measure of intensity of fracturing, joint orientation and spacing, joint conditions, groundwater conditions, in-situ stresses, and geological structure (Bieniawski, 1993). Rock mass classification methods are either qualitative, for example GSI and Rock Load, or quantitative like, Q, RMR, etc. (Abbas and Konietzky, 2017). The most common rock mass classification schemes include the NATM (New Austrian Tunneling Method), Norwegian Method of Tunneling, RQD (Rock Quality Designation), RSR (Rock Structure Rating), RMR (Rock Mass Rating) and Q (Rock Tunneling Quality Index) (COSAR, 2004). ...
... Both are used to assess stability, Q gives no clue of support limit while RMR system calculates stand-up time. Q system, and RMR to a minor extent, calculates the ground support design in terms of liner thickness and rock-bolt spacing etc. (Abbas and Konietzky, 2017). Both RMR and Q-Systems follow similar system, with ratings on different log-scale. ...
This research work presents the rock mass characteristics and tunnel support system recommendations for hydroelectric power tunnels at Dasu dam site Pakistan. Two inverted U-shaped tunnels are proposed at the left bank of Indus river. The tunnels have inlet portals at an elevation of 773.00 m and outlet portals at an elevation of 758.00 m. The thickness of rock cover above the tunnels is between 100 and 200 m. Three types of rock are encountered at project site including Granulite, Amphibolite and Gabbronorite. Granulite rocks are encountered along the alignment of tunnels. Rock mass is classified using Rock mass rating (RMR) and Tunneling quality index (Q system). Support system is suggested based on values of Q and RMR. Correlation between Q-index and RMR is also derived.
... Rock support and reinforcement in the development openings and stopes will increase the operational costs and time (delay the mine life), and further affect the quantity and sequence of rock material extracted from the stopes to the processing plants. To incorporate the rock formation's strength into the formulation, rock mass classification systems [73,74] such as the Geological Strength Index (GSI), Rock Structure Rating (RSR), Rock Mass Rating (RMR), and Q system are determined to characterize the rock formation, and then Kriging applied to populate the block model. According to Abbas and Konietzky [73], these classification systems could be grouped as qualitative or descriptive (e.g., GSI) and quantitative (e.g., Q system, RMR, and RSR) with RMR being more applicable to tunnels and mines. ...
... To incorporate the rock formation's strength into the formulation, rock mass classification systems [73,74] such as the Geological Strength Index (GSI), Rock Structure Rating (RSR), Rock Mass Rating (RMR), and Q system are determined to characterize the rock formation, and then Kriging applied to populate the block model. According to Abbas and Konietzky [73], these classification systems could be grouped as qualitative or descriptive (e.g., GSI) and quantitative (e.g., Q system, RMR, and RSR) with RMR being more applicable to tunnels and mines. According to Kaiser and Cai [74], data obtained from the geology and geomechanics of the rock formation are fundamental to mine planning and development designs. ...
It is important that the strategic mine plan makes optimum use of available resources and provides continuous quality ore to drive sustainable mining and profitability. This requires the development of a well-integrated strategy of mining options for surface and/or underground mining and their interactions. Understanding the current tools and methodologies used in the mining industry for surface and underground mining options and transitions planning are essential to dealing with complex and deep-seated deposits that are amenable to both open pit and underground mining. In this study, extensive literature review and a gap analysis matrix are used to identify the limitations and opportunities for further research in surface-underground mining options and transitions optimization for comprehensive resource development planning.
... L'identification du matériau fait partie de la caractérisation de la discontinuité car il aura un impact sur les propriétés mécaniques et hydraulique. 1. 1 ...
... Cette approche ne permet pas de prendre en compte les spécificités du problème de stabilité de blocs (position du bloc autour de l'excavation, volume du bloc, comportement des joints), mais elle est couramment utilisée en ingénierie pour les problèmes de mécanique des roches et la détermination des soutènements y compris si le problème est celui de la stabilité de blocs. Ces indices sont accompagnés d'abaques d'aides à la décision du soutènement à mettre en place ( [97], [16], [1]). Ils procurent une solution rapide pour estimer en première approche le comportement d'un massif et une stratégie de soutènement. ...
... Les simulations sont relancées, sur la même géométrie de bloc dans le même état initial de contraintes, avec une telle densité pour les 5 modèles, dont le modèle 5 qui est le modèle de référence avec une loi de comportement semi-logarithmique tronquée. Les modèles ont été présentés dans le paragraphe 3. 1 Densité critique Pour conclure l'analyse de sensibilité sur la loi de comportement normal, on cherche à déterminer la densité de boulonnage critique qui permet d'assurer l'équilibre du bloc. ...
De nombreux secteurs d’activités impliquent une occupation de l’espace souterrain. Le creusement dans un massif rocheux présente différents modes de ruine qu’il faut savoir identifier pour utiliser les outils de conception adaptés. Parmi eux, l’instabilité de blocs est un problème courant dans les massifs rocheux fracturés. Les approches numériques de blocs multiples permettent de considérer le massif dans son ensemble mais leur utilisation peut s’avérer lourde et requiert beaucoup de données d’entrée parfois indisponibles. L’approche Isobloc est basée sur le concept de bloc isolé et, comparée aux autres méthodes du même type, elle est plus rigoureuse dans la résolution du problème de mécanique du bloc. Dans cette thèse, cette méthode a été étudiée afin de pouvoir l’intégrer dans la démarche de conception en milieu rocheux fracturé. Une première partie s’intéresse spécifiquement à la loi de comportement normal des joints, et à la définition d’indicateurs afin de quantifier la sécurité de l’état d’équilibre du bloc. Une autre partie présente les réflexions et manières de modéliser une solution de soutènement avec Isobloc. Trois types sont proposées suivant le degré de connaissance du soutènement. Enfin une application de la méthode Isobloc dans la démarche d’étude de stabilité de blocs a été proposé à partir de données d’un site d’étude réel.
... Sistem klasifikasi ini juga telah dikalibrasi dan direvisi berdasarkan sejarah penggunaannya pada tambang batubara, teknik sipil, dan terowongan kedalaman dangkal [16]. Parameter RMR dikenal sebagai sistem klasifikasi geomekanik yang paling umum digunakan yang terdiri atas 6 parameter [17][18][19] Data UCS diperoleh dari nilai kekerasan batuan yang diambil melalui survei palu Schmidt [20]. Penggunaan palu Schmidt relatif mudah dan tidak bersifat destruktif [21,22]. ...
Terowongan Eksplorasi Uranium Eko Remaja Kalan (TEURK), Kalimantan Barat yang dibangun pada 1980 merupakan salah satu sarana penelitian cebakan uranium di Indonesia. Karena tingginya kerapatan stuktur geologi, terdapat beberapa zona lemah di dalam terowongan. Pada zona tersebut dipasang penyangga sementara berupa tiang-tiang kayu. Saat ini tiang-tiang kayu tersebut tidak lagi mampu menyangga terowongan sehingga sering terjadi longsor batu dan/atau tanah di dalam terowongan. Penelitian ini bertujuan untuk mengetahui kualitas massa batuan aktual dan menentukan jenis perkuatan yang dibutuhkan agar terowongan tetap aman digunakan. Untuk mencapai dua tujuan tersebut, dilakukan survei palu Schmidt dan survei pindai-garis pada zona tak berpenyangga (kedalaman 50-297 m dan 355-538 m) untuk mengambil data parameter klasifikasi Rock Mass Rating (RMR). Dari 0 sampai 100, massa batuan TEURK memiliki nilai RMR 52-71 (sedang-baik). Perkuatan yang direkomendasikan adalah baut batu dan beton semprot konvensional.
... The main features of the RMR methods were uniaxial compressive strength, rock quality designation, spacing of discontinuities, condition of discontinuities, groundwater conditions, and orientation of discontinuities, rock and soil strength classification Brown (1981) in Abbas and Konietzky (2017), as shown in Table 1. ...
... Based on the field test of uniaxial compressive strength by Brown (1981) in Abbas and Konietzky (2017), the rock strength distribution in this campus area can be shown in Figure 4. The limestone strengths in this research area are categorized into the medium to very strong rock. ...
The research location is in the PPSDM Geominerba field campus. The campus is located in Padalarang, West Java that is surrounded by the open-pit mining of limestone and marble. This limestone was formed in Oligo-Miocene of Rajamandala Formation. The research objective was to determine the condition of the slopes around the campus based on geomechanical characteristics. Based on field observations, the slope angle in the area is dominated by steep slopes. The rock hardness level is dominated by hard rock with a hardness ranging from 50-100 MPa. Rock Mass Rating shows that the area is dominated by good rocks. While the Slope Mass Rating calculation show that the maximum slope angle is between 52-75°. Level of deformation and intensive weathering process will reduce the strength of the rock in the future. Several rock fall occurrences on this research area support this assumption. Yet, some local open pit mining area activity near the toe hill of the area need to be concerned regarding the effect of the local rock fall occurrences.
... Many rock mass classification systems have been proposed to date, of which Terzaghi classification, Lauffer classification, Deere's rock quality designation (RQD), and Wickham's rock structure rating (RSR) are considered as the earlier main classification methods. However, the influences caused by the properties of discontinuities or intact rock material were disregarded in some of the previous classifications of rock masses [55]. Bieniawski [56,57] developed a modified geomechanical rock mass rating (RMR) classification system, which is used for the design and construction of excavations in rock, such as tunnels, mines, slopes, and foundations. ...
... The two systems mentioned above are the most influential classification systems proposed to date. Additionally, some other classifications have been developed which are widely adopted in different fields, such as the New Austrian tunneling method (NATM), size-strength classification, International Society for Rock Mechanics (ISRM) classification, geological strength index (GSI), and the BQ method used in China [54][55][56][57][58][59][60]. According to the rock mass classification systems of the corresponding standard [60,61], the bedded structure tunnel investigated in the present study has a surrounding rock ranking of III. ...
The Johnson–Holmquist-II(JH-2) model is introduced as the constitutive model for rock materials in tunnel smooth blasting. However, complicated and/or high-cost experiments need to be carried out to obtain the parameters of the JH-2 constitutive model. This study chooses Barre granite as an example to propose a quick and convenient determination method for the parameters of the JH-2 model using a series of computational and extrapolated methods. The validity of the parameters is verified via comparing the results of 3D numerical simulations with laboratory blast-loading experiments. Subsequently, the verified parameter determination method, together with the JH-2 damage constitutive model, is applied in the numerical simulation of smooth blasting in Zigaojian tunnel, Hangzhou–Huangshan high-speed railway. The overbreak/underbreak induced by rock blasting and joints/discontinuities is well estimated through comparing the damage contours resulting from the numerical study with the tunnel profiles measured from the tunnel site. The peak particle velocities (PPVs) of the near field are extracted to estimate the damage scope and damage degree for the surrounding rock mass of the tunnel on the basis of PPV damage criteria. This method can be used in the excavation of rock tunnels subjected to large strains, high strain rates, and high pressures, thereby reducing safety risk and economic losses.
