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Rock mass classification systems have gained wide attention and are frequently used in rock engineering and design. However, all of these systems have limitations, but applied appropriately and with care they are valuable tools.The paper describes the history of the Q-system that was introduced in 1974, and its later development. The individual par...
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... Q-values are combined with the dimensions of the tunnel or cavern in a Q-support chart (see Fig. 7). This chart is based on more than 1000 cases of rock support performed in tunnels and caverns. Using of a set of tables with a number of footnotes, the ratings for the different input parameters can be established based on engineering geological observations in the field, in tunnels, or by logging of rock cores. The structure of the ...
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... the value of Q, is used to define a number of support categories in a chart published in the original paper by Barton et al. (1974). This chart has later been updated to directly give the support. Grimstad and Barton (1993) made another update to reflect the increasing use of steel fibre reinforced shotcrete in underground excavation support. Fig. 7 is reproduced from this updated ...
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... Q-values and support in Fig. 7 are related to the total amount of support (temporary and permanent) in the roof. The diagram is based on numerous tunnel support cases. Wall support can also be found by applying the wall height and the following adjustments to ...
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... from tunnel to tunnel. This is a problem when experience from different regions is used to calibrate the support recommendation in a classification system. Fig. 9 shows the correlation between bolt spacing and the Q-value in areas without sprayed concrete. It is on the basis of such crude results that many of the support recommendations in Fig. 7 are established. The users quickly forget on which averaged, inaccurate basis the rock support given in the support chart, is based. This is not a special limitation of the Q system, but is a common feature for most quantitative classification systems used for rock ...
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... Good or better ground qualities in rock class A (in Fig. 7) is where RQD > approx. 90 occur. As shown in Fig. 1 large blocks are outside the limits of RQD to correctly characterize jointing. However, this reduced ability of RQD may give significant errors in the Q to characterize the ground where blocks larger than approx. 1 m 3 occur. The following two examples where RQD = 100, J r = 4, and J ...
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... Q-values are combined with the dimensions of the tunnel or cavern in a Q-support chart (see Fig. 7). This chart is based on more than 1000 cases of rock support performed in tunnels and caverns. Using of a set of tables with a number of footnotes, the ratings for the different input parameters can be established based on engineering geological observa- tions in the field, in tunnels, or by logging of rock cores. The structure of the ...
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... the value of Q, is used to define a number of support categories in a chart published in the original paper by Barton et al. (1974). This chart has later been updated to directly give the support. Grimstad and Barton (1993) made another update to reflect the increasing use of steel fibre reinforced shotcrete in underground excavation support. Fig. 7 is reproduced from this updated ...
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... Q-values and support in Fig. 7 are related to the total amount of support (temporary and permanent) in the roof. The diagram is based on numerous tunnel support cases. Wall support can also be found by applying the wall height and the following adjustments to ...
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... from tunnel to tunnel. This is a problem when experience from different regions is used to calibrate the support recommendation in a classification system. Fig. 9 shows the correlation between bolt spacing and the Q-value in areas without sprayed concrete. It is on the basis of such crude results that many of the support recommendations in Fig. 7 are established. The users quickly forget on which averaged, inaccurate basis the rock support given in the support chart, is based. This is not a special limitation of the Q system, but is a common feature for most quantitative classification systems used for rock ...
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... Good or better ground qualities in rock class A (in Fig. 7) is where RQD > approx. 90 occur. As shown in Fig. 1 large blocks are outside the limits of RQD to correctly characterize jointing. However, this reduced ability of RQD may give significant errors in the Q to characterize the ground where blocks larger than approx. 1 m 3 occur. The following two examples where RQD = 100, J r = 4, and J ...
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Rock mass classification systems are often used in many empirical design practices in rock engineering contrasting with their original intent and applications, as for instance in estimation of TBM performance in various tunneling projects. However, parameters in these classifications were related to support design; they were not selected to describ...
Citations
... In case of category (a), Palmstrom and Broch (2005) have given an example of difficulty to follow the use of SRF value for a weakness zone (Figure 3.5). This example considers two cases both with over burden depth of 40 m: in case A, tunnel is excavated in competent rock. ...
... According to Q-system, the amount of rock support will be significantly higher for case B. In practice, it is opposite, as in a weakness zone the arching effect will contribute to increase stability. (Palmstrom and Broch, 2005). ...
... Figure 3.6 How is the value of Q in equation H > 350 Q 1/3 found to assess squeezing? (Palmstrom and Broch, 2005). ...
... Dhang [23] in the geotechnical investment of Udhampur railway tunnel in Jammu and Kashmir area found poor to very poor (q-value ranging 0.05-2, and RMR 20-40) in the rock mass of sub-Himalaya region which is in similar range to our study; however, NATM approach was used for tunnel support designing while our study uses q-system and RMR system as it is suggested of the best approach for preliminary support design [24]. ...
The Siddhartha Highway is a critical economic artery in Nepal, providing a vital trade link with China and India. However, this roadway has been beset by the frequent occurrence of rock falls and dry landslides, which can cause severe damage and disruption. The main causes of these issues are differential weathering patterns of the rocks, activation of the Main Frontal Thrust (MFT), and a young mountain chain with fragile geology. Because of road instability, the road tunnel has been proposed to prevent damage. The proposed tunnel road passes between the chainage of 29+000 and 30+370 in the rocks of the Lower and Middle Siwaliks. The Lower Siwaliks are composed of thick to thin-bedded shale, mudstone and calcareous sandstone whereas the Middle Siwaliks are represented by presence of friable sandstone, shale and mudstone. The rock mass of this section varies from poor rock to extremely poor rock with RMR values ranging from 29 to 45 and Q values ranging from 0.19 to 1.11. The support system for each class is derived according to RMR and Q-value. The kinematic analysis reveals the potential of wedge failure at the southern portal of the road tunnel, whereas the northern portal has shown a low potential for failure.
... Many of these systems also provide direct design recommendations based on the rockmass classification. Each rockmass classification system has the potential to be incorrectly used in scenarios when rockmass conditions do not fit the assumptions used to create the classification system (Hadjigeorgiou and Harrison 2012;Marinos et al. 2005;Palmstrom and Broch 2006). ...
For many civil and mining engineering structures, rock is the primary construction medium. At engineering scales, the material of interest is the “rockmass,” which contains sections of intact rock separated by a network of pre-existing joints (i.e. fractures). The mechanical behavior of a rockmass is defined by attributes of its intact rock (e.g. strength, stiffness, dilatancy), in addition to attributes of the pre-existing joints (e.g. orientation, surface condition, persistence). Testing representative rockmass specimens in the laboratory is uncommon because of its difficulty due to the required scale. The present study uses laboratory-scale rockmass analog specimens to study the effects that the pre-existing joints have on rockmass mechanical behavior. Smooth joints were sawed into decimeter-scale cylindrical specimens of Carrara marble. Specimens with two joint sets and two different degrees of jointing were tested. Specimens were loaded in compression using a triaxial apparatus under varying levels of confining pressure, and mechanical attributes were calculated including peak strength, residual strength, and pre-peak damage threshold parameters. At the higher end of confining pressures tested (12 MPa), the peak strength of the Carrara marble specimens was not influenced by the presence of pre-existing joints. These results were compared to the results of previous jointed specimen testing on the more brittle Blanco Mera granite. Notably, the drops in peak strength with increasing degrees of jointing were smaller for Carrara Marble than for Blanco Mera granite, which suggests that the Generalized Hoek–Brown failure criterion may not be appropriate to estimate rockmass strength for less brittle rocks.
... Generally, the deformation and damage of a rock mass are mainly controlled by the rock characteristics and the structure of joints [12]. Rock quality evaluation methods can be categorized into single-factor and multi-factor evaluation methods, including the elastic wave velocity method [13], rock quality designation (RQD) [14], rock mass rating (RMR) [15], Q system [16], and so on. The lower the rock quality, the denser the distribution of joints in the rock, and the higher the degree of heterogeneity of the rock mass. ...
A jointed rock mass (JRM) is the usual case in practical engineering, which has significant effects on its mechanical performance. To clarify the difference in the seismic responses of underground structures in JRM sites or homogeneous rock mass (HRM) sites, two models were prepared to take shaking table tests in a structural laboratory. The HRM site was prepared following the similitude relations of material; meanwhile, underground structures of a metro station were embedded during the casting of the models. The JRM site and structure were made with the same material but produced random joints after the natural drying process. Different frequencies of harmonics were used to excite along the two models in the transverse or the longitudinal direction, respectively. The dynamic effect was evaluated by time-frequency and frequency-domain analyses. The test results compared with the HRM model indicated that the JRM model had a 22% reduction in the transverse fundamental frequency, but the dynamic response of the ground surface was enhanced due to the effect of the joints. Under harmonic excitations of the same intensity, the JRM model produced a greater energy response to the station structure and reduced the acceleration response of the platform in the high-frequency region. Meanwhile, the JRM model produced a peak tensile strain at the connections of the main and subsidiary structures that was 31% larger than that of the HRM model, and the range of tensile strains observed at the platform connecting the horizontal passage was 1.5 times larger than that of the HRM model.
... Several of these tunnels are inspected from the floor at a safe location, leading to a poor assessment. [6,7] 4. Constraints on Quantification: Quantifying representative values for the newly exposed rock mass, such as the Rock Quality Designation (RQD), within the limited time frame of a fast-paced tunnel cycle presents significant challenges. The RQD metric, which involves measuring core pieces longer than 10 cm for every meter of rock, is tough to assess accurately under these conditions. ...
... The RQD metric, which involves measuring core pieces longer than 10 cm for every meter of rock, is tough to assess accurately under these conditions. [8,9]. 5. Conservative Over-Supporting: Typically, the poorest rock mass conditions dictate the support for the entire blasted area, which can lead to unnecessary over-support in better-quality sections [6,7] 6. Empirical Data Limitations: The original empirical data may not cover all geological conditions, construction geometries, or site-specific factors, resulting in an oversimplified approach to complex geologies [6,7,9] 7. System Update Challenges: Updates to these systems are infrequent and labourintensive, hindered by non-transparent processes and inherent biases. The process involves trial and error to adapt input configurations to experienced stability in existing sites [5][6][7]. ...
... The RQD metric, which involves measuring core pieces longer than 10 cm for every meter of rock, is tough to assess accurately under these conditions. [8,9]. 5. Conservative Over-Supporting: Typically, the poorest rock mass conditions dictate the support for the entire blasted area, which can lead to unnecessary over-support in better-quality sections [6,7] 6. Empirical Data Limitations: The original empirical data may not cover all geological conditions, construction geometries, or site-specific factors, resulting in an oversimplified approach to complex geologies [6,7,9] 7. System Update Challenges: Updates to these systems are infrequent and labourintensive, hindered by non-transparent processes and inherent biases. The process involves trial and error to adapt input configurations to experienced stability in existing sites [5][6][7]. ...
Rock mass classification systems are crucial for assessing stability and risk in underground construction globally and guiding support and excavation design. However, systems developed primarily in the 1970s lack access to modern high-resolution data and advanced statistical techniques, limiting their effectiveness as decision-support systems. Initially, we outline the limitations observed in this context and later describe how a data-driven system, based on drilling data as detailed in this study, can overcome these limitations. Using extracted statistical information from thousands of MWD-data values in one-meter sections of a full tunnel profile, thus working as a signature of the rock mass, we have demonstrated that it is possible to form well-defined clusters that can act as a foundational basis for various rock mass classification systems. We reduced the dimensionality of 48-value vectors using nonlinear manifold learning techniques (UMAP) and linear principal component analysis (PCA) to enhance clustering. Unsupervised machine learning methods (HDBSCAN, Agglomerative Clustering, K-means) were employed to cluster the data, with hyperparameters optimised through multi-objective Bayesian optimisation for effective clustering. Using domain knowledge, we experienced improved clustering and system tuning opportunities in adding extra features to core clusters of MWD-data. We struc-tured and correlated these clusters with physical rock mass properties, including labels of rock type and rock quality, and analysed cumulative distributions of key MWD-parameters for rock mass assessment to determine if clusters meaningfully differentiate rock masses. The ability of MWD data to form distinct rock mass clusters suggests substantial potential for future classification systems grounded in this objective, data-driven methodology, free from human bias.
... He contended that Q TBM takes into account far too many parameters and that this then leads to confusion rather than clarification. Palmstrom and Broch (2006) regarded several of the Q TBM input parameters as irrelevant or even as misguiding for TBM performance and concluded that the total effects of the input parameters are difficult to follow in the model. They stated that comparisons are indicating very poor or no correlations to obtained results and like Blindheim (2005) they recommended the Q TBM system not be used. ...
One of the TBM performance prediction models that has been introduced in recent years and used for estimating penetration rate of TBM in hard rock conditions is the QTBM model. This model was introduced by Barton in the early 2000s. There have been many reviews of this model and the estimated penetration rates in various projects. This study was also conducted to check the validity of this model and propose modifications to models for different geological conditions. For this purpose, a database of TBM field performance along with geological information was prepared from 109 tunnel sections (with reliable geological and machine data). The results of calculated penetration rate by the QTBM model were compared with the field observation in the database. The results showed that this model offers low accuracy in predicting the machine penetration rate in different geological conditions. Subsequently, the relationship between input parameters of the QTBM model with the penetration rate was re-examined to see the impact of individual input parameters on the penetration rate of the machine. A new model is proposed, where the input parameters with lower impact on the penetration rate were removed and an attempt was made to obtain an empirical relationship between the more influential parameters and the field penetration index (FPI). New empirical relationships are more accurate in predicting machine penetration rates due to their simplicity and the need for fewer input data.
... • Design of a tunnel for a hydropower in a high-tectonic stress regime, for tunnel engineering due to the potential for rock mass instabilities. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited by guest of discontinuity parameters are fundamental prerequisites for ensuring the enduring safety and prosperity of tunnel projects (Palmstrom & Broch 2006). This research has focused on the empirical assessment of headrace tunnels to characterize rock mass and assess support, highlighting the importance of accurate rock classification and understanding of discontinuity parameters (Uljarevic). ...
The study for the design of tunnels in similar geological settings, providing insights into potential challenges that may arise during excavation and offering strategies for mitigating risks in District Kalam on the Ushu River, Khyber Pakhtunkhwa, Pakistan. The methodology involved geological mapping, rock sampling, discontinuity surveys, and laboratory testing for empirical analysis of tunnel parameters at the Weir House, Powerhouse, and tunnel alignment locations. Empirical analysis of tunnel parameters using three rock classification systems, rock mass rating (RMR), rock quality tunneling index, and rock mass index (RMi). Based on the classification, the rock quality was found to be fair, indicating favorable rock properties. The Q-system rated the rock as poor to fair, suggesting low discontinuity intensity, medium rock strength, or medium deformation modulus. According to the RMi, rock was rated as medium to strong, indicating low discontinuity intensity, high rock strength, or low deformability. The support design for the tunnel is based on empirical analysis, it recommends support design for the tunnel reinforcement elements such as rock bolts, wire mesh, and shotcrete lining. Overall, the tunnel is stable and does not have complex structure and weak zones.
... To investigate the potential for reduced noise in labelling, we adapted our model to utilise the Q-base value as the label. This approach involves omitting the Jw/SRF term from the Q-value calculation-a component sometimes contested in literature for its indirect representation of rock mass qualities compared to the initial two terms [57]. This adjustment aims to refine the model's accuracy by focusing on more directly relevant factors of rock mass classification. ...
Current rock engineering design in drill and blast tunnelling primarily relies on engineers' observational assessments. Measure While Drilling (MWD) data, a high-resolution sensor dataset collected during tunnel excavation, is underutilised, mainly serving for geological visualisation. This study aims to automate the translation of MWD data into actionable metrics for rock engineering. It seeks to link data to specific engineering actions, thus providing critical decision support for geological challenges ahead of the tunnel face. Leveraging a large and geologically diverse dataset of ~500,000 drillholes from 15 tunnels, the research introduces models for accurate rock mass quality classification in a real-world tunnelling context. Both conventional machine learning and image-based deep learning are explored to classify MWD data into Q-classes and Q-values-examples of metrics describing the stability of the rock mass-using both tabular-and image data. The results indicate that the K-nearest neighbours algorithm in an ensemble with tree-based models using tabular data, effectively classifies rock mass quality. It achieves a cross-validated balanced accuracy of 0.86 in classifying rock mass into the Q-classes A, B, C, D, E1, E2, and 0.95 for a binary classification with E versus the rest. Classification using a CNN with MWD-images for each blasting round resulted in a balanced accuracy of 0.82 for binary classification. Regressing the Q-value from tabular MWD-data achieved cross-validated R2 and MSE scores of 0.80 and 0.18 for a similar ensemble model as in classification. High performance in regression and classification boosts confidence in automated rock mass assessment. Applying advanced modelling on a unique dataset demonstrates MWD data's value in improving rock mass classification accuracy and advancing data-driven rock engineering design, reducing manual intervention.
... The intact rock mass is considered homogeneous, even few discontinuities does not represent the strength of rock mass [3]. The mislead interpretation of rock mass classification provides uneconomical support system [5], hence reliable estimation of strength and deformation is prerequisite for the analysis and design of underground structure. The strength of intact rock is obtained through laboratory test such as uniaxial compressive test, tri-axial test and point load test [6]. ...
Ground classification systems have been widely used to characterize the soil or rock mass for the analysis and design of underground structures. Deformation characteristics of rock mass around the underground excavation boundary varies according to types of rock and their properties, in situ stress condition and types of support used. Broadly used classification systems for underground structures are Rock Mass Rating (RMR) and Rock Quality Index (Q- Systems) but in-case of the Nagdhunga-Naubisea road tunnel Nippon Expressway Company Limited used NEXCO-System. Literally, the NEXCO classification is based on the velocity of elastic wave, geological condition, boring core condition, competence factor, stability of tunnel face and convergence. However, in-case of Nagdhunga-Naubisea road tunnel, grade point is computed using the similar parameters that are used in RMR- System and Q-System. This research focuses about comparison between ground classification from RMR, Q-system and NEXCO system. The geological descriptions obtained from project office at chainages (0+497 to 0+502), (0+508 to 0+513), (0+581.8 to 0+587.8) and (0+584.2 to 0+590.2) were used to correlate the relationship between RMR, Q-system and NEXCO system of rock mass classification systems. Results showed that from RMR, the ground classification is “poor” at all the chainages whereas from Q-system is “very poor” at chainage (0+508 to 0+513) and “poor” at other chainages. Similarly, from NEXCO- system it was found that “DII” at chainages (0+497 to 0+502) and (0+508 to 0+513) and CII for chainages (0+581.8 to 0+587.8) and (0+584.2 to 0+590.2). These results revealed that the ground classification from NEXCO as DII is similar to poor from RMR and poor or very poor from Q system where as CII from NEXCO is similar to poor from both RMR and Q system.
... There are some challenges relevant to the Q system. According to Palmstrom and Broch [27], the Q system fails to provide sufficient support in high-flow conditions. The employment of SRF is not clear for squeezing and buckling ground environment [28]. ...
At the initial phases of tunnel design, information on rock properties is often limited. In such instances, the engineering classification of the rock is recommended as a primary assessment of its geotechnical condition. This paper reviews different rock mass classification methods in the tunnel industry. First, some important considerations for the classification of rock are discussed, such as rock quality designation (RQD), uniaxial compressive strength (UCS) and groundwater condition. Traditional rock classification methods are then assessed, including the rock structure rating (RSR), rock mass rating (RMR), rock mass index (RMI), geological strength index (GSI) and tunnelling quality index (Q system). As RMR and the Q system are two commonly used methods, the relationships between them are summarized and explored. Subsequently, we introduce the detailed application of artificial intelligence (AI) method on rock classification. The advantages and limitations of traditional methods and artificial intelligence (AI) methods are indicated, and their application scopes are clarified. Finally, we provide suggestions for the selection of rock classification methods and prospect the possible future research trends.
