Quantification of the GSI and D values in the Hoek–Brown criterion using the rock quality designation (RQD) and discontinuity surface condition rating (SCR)

Xia, Kaizong; Chen, Congxin; Wang, Tianlong; Pang, Hansong; Liu, Xuanting

doi:10.1007/s10064-021-02493-y

Cited by 12 publications

(10 citation statements)

References 40 publications

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“…9.09 MPa < UCS < 74.87 MPa : RMR 89 = RMR 14 −2 [47] with a W2 weathering grade GSI = 1.45RMR Santos(W3) − 3.74 for RMR Santos proposed by [47] with a W3 weathering grade GSI = 1.88RMR Santos(W4) − 4.87 for RMR Santos proposed by [47] with a W4 weathering grade GSI = 0.9RMR 14 − 6.86 for RMR 14 proposed by [48] [31] GSI = 1 2 SR + JCDI Where SR is the structure rating and JCDI is the joint condition digital imaging rating [49]…”

Section: Evolution Of Gsi and Different Variationsmentioning

confidence: 99%

Why Engineers Should Not Attempt to Quantify GSI

Yang

Elmo

2022

Geosciences

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In the past decade, there has been an increasing trend of digitalizing rock engineering processes. However, this process has not been accompanied by a critical analysis of the very same empirical methods that many complex numerical and digital methods are founded upon. As engineers, we are taught to use and trust numbers. Indeed, we would not be able to define the factor of the safety of a structure without numbers. However, what happens when those numbers are nothing but numerical descriptions of qualitative assessments? In this paper we present a critical review of the many attempts presented in the literature to quantify GSI (geological strength index). To the authors’ knowledge, this paper represents the first time that all the different GSI tables and quantification methods that have been proposed over the past two decades are collated and compared critically. In our critique, we argue against the paradigm whereby the quantification process adds the experience factor for inexperienced engineers. Furthermore, we discuss the limitations of the notion that GSI quantification methods could transform subjectivity into objectivity since the parameters under considerations are not quantitative measurements. Relying on empirically defined quantitative equivalences raises important questions, particularly when these quantitative equivalences are being used to define so-called accurate rock mass classification input for design purposes.

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Section: Evolution Of Gsi and Different Variationsmentioning

confidence: 99%

Why Engineers Should Not Attempt to Quantify GSI

Yang

Elmo

2022

Geosciences

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“…Based on Hoek, researchers Xia, Chen, Wang, Pang, and Liu perform a numerical estimation of the GSI values and the D parameter of the Hoek and Brown breakage criterion, using the RQD and the discontinuous surface condition (SCR) that depends on the infill, weathering, and roughness classifications of the rock to give correction and bring it closer to the original definition of GSI (Xia et al 2022). The described and corrective relationship is the one expressed in Eq.…”

Section: Rock Quality Designation (Rqd)mentioning

confidence: 99%

“…Although several correlations between the different classification systems have been identified, the most abundant are those relating GSI as a function of RMR and as a function of Q, which require much more information and precision to determine. As for GSI relationships as a function of RQD, only three recent relationships are available: Hoek et al (2013), Santa et al (2019), and Xia et al (2022.…”

Section: Introductionmentioning

confidence: 99%

“…Although several correlations between the different classification systems have been identified, the most abundant ones are those relating GSI as a function of RMR and as a function of Barton's Q. As for GSI relationships as a function of RQD, only three recent relationships are available: Hoek et al ( 2013), Santa et al (2019), andXia et al (2022). Therefore, this study presents a correlational analysis of the GSI and RQD classification systems, using robust nonparametric statistics, with the aim of determining an expression to estimate GSI in the field.…”

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confidence: 99%

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Proposal of a new method for calculating GSI

Vivanco¹,

Avendaño²

2022

Mining Science

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Rock mass classification systems are simple but valuable tools for the qualitative and quantitative classification of rock masses and for the planning of the fortification of mining excavations. Unfortunately, in the prefeasibility phase, not all the information needed for a preliminary project evaluation is always available, and one of the few available information is the RQD, however, although it is very necessary to determine the GSI to analyze the failure criteria, it is difficult to obtain at this stage of the project. Although several correlations between the different classification systems have been identified, the most abundant ones are those relating GSI as a function of RMR and as a function of Barton's Q. As for GSI relationships as a function of RQD, only three recent relationships are available: Hoek et al. ( 2013), Santa et al. (2019), andXia et al. (2022). Therefore, this study presents a correlational analysis of the GSI and RQD classification systems, using robust nonparametric statistics, with the aim of determining an expression to estimate GSI in the field. Among the results, it is highlighted that better GSI prediction results are obtained when 25% < RQD ≤ 87%, with a maximum error of ±14 points, improving the estimation accuracy by 62% with respect to current proposals. Despite the above, the difficulty of interpreting more accurately the specific geological characteristics of each rock mass remains.

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“…The selection of different parameters is difficult and dependent on the researcher's personal preferences because the mechanical behavior of rocks exhibits highly complicated nonlinear aspects [12,13]. Although some researchers have developed methods for quantitatively predicting rock mechanical characteristics in recent years [14][15][16], the aforementioned issues cannot entirely be avoided. New methods, for example, artificial intelligence, virtual reality, and big data, are being developed together with network technology.…”

Section: Introductionmentioning

confidence: 99%

A Constitutive Model Study of Chemical Corrosion Sandstone Based on Support Vector Machine and Artificial Bee Colony Algorithm

Lin,

Li,

Zhou

et al. 2023

Sustainability

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The mechanical characteristics of rock are greatly influenced by hydrochemical corrosion. The chemical corrosion impact and deformation properties of the meso-pore structure of rock under the action of different hydrochemical solutions for the stability evaluation of rock mass engineering are of high theoretical relevance and applied value. Based on actual data, a support vector machine (SVM) rock constitutive model based on artificial bee colony algorithm (ABC) optimization is constructed in this article. The impact of porosity (chemical deterioration), confining pressure, and other aspects is thoroughly examined. It is used to mimic the triaxial mechanical behavior of rock under various hydration conditions, with high nonlinear prediction ability. Simultaneously, the statistical damage constitutive model and the ABC-SVM constitutive model are used to forecast the sample’s stress–strain curve and compare it to the experimental data. The two models’ correlation coefficients (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE) are computed and examined. The correlation coefficient between the ABC-SVM constitutive model calculation results and the experimental results is found to be larger (R2 = 0.998), and the error is smaller (RMSE = 0.7730, MAPE = 1.51), indicating that it has better prediction performance on the conventional triaxial constitutive relationship of rock. It is a highly promising new way of describing the rock’s constitutive connection.

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Quantification of the GSI and D values in the Hoek–Brown criterion using the rock quality designation (RQD) and discontinuity surface condition rating (SCR)

Cited by 12 publications

References 40 publications

Why Engineers Should Not Attempt to Quantify GSI

Why Engineers Should Not Attempt to Quantify GSI

Proposal of a new method for calculating GSI

A Constitutive Model Study of Chemical Corrosion Sandstone Based on Support Vector Machine and Artificial Bee Colony Algorithm

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