Finding the Relationship between RQD and Fracture Frequency in the Different Ok Tedilithologies

Vali, Bradley; Arpa, Gabriel

doi:10.1016/j.proeps.2013.01.053

Cited by 9 publications

(1 citation statement)

References 3 publications

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“…This formation is impermeable and about 1 This unit outcrops at the reservoir area and downstream side of the Mut dam site. boreholes showed that this formation has a thickness of approximately 40-50 m alo Regression analyses as statistical methods are widely used in geotechnical and rock engineering practice (e.g., Hoek and Brown [27]; Apte et al [28]; Akca [29]; Uddameri [30]; Sivrikaya [31]; Zorlu et al [32]; Yagiz et al [33]; Yagiz and Gokceoglu [34]; Chen-Chang et al [35]; Valia and Arpa, [36]; Azimian and Ajalloeian R [10]; Farid and Rizwan [15]; Öge and Çırak [37]; Rahimi et al [38]). Average values of rock mass properties of karstic limestone block (Q value, RQD, GSI, JSP, Ap, Lu, SPI, k) and average Gt were calculated (Table 9).…”

Section: Simple and Multiple Regression Analysesmentioning

confidence: 99%

An Assessment on Permeability and Grout Take of Limestone: A Case Study at Mut Dam, Karaman, Turkey

Kayabaşı

Gökçeoğlu

2019

Water

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The main purposes of the present study are to evaluate pilot grouting and to develop regression equations for prediction of grout intake. There are no permeability problems with the sandstone-siltstone-claystone alternations and basement clayey limestone at the dam site. Karstic limestone block is permeable due to karstification and heavy discontinuities. For the purpose of the study, Q system, geological strength index (GSI), secondary permeability index (SPI), joint spacing (JSP), joint apertures (Ap), Lugeon (Lu), and the permeability coefficient (k) were determined. Karstic limestone block rock mass properties correlated with grouting material amount. A series of simple and multiple nonlinear regression analyses was performed between grout take material amount (Gt) and average values of these rock mass properties. Significant determination coefficients were determined. Prediction capacity of the empirical equations were also examined with root mean square error (RMSE), values account for (VAF), mean absolute percentage error (MAPE), and prediction error evaluations. Considering simple regression analyses, the equation derived with Gt-SPI gives the best performance. The best prediction is determined with the equation derived with rock quality designation values (RQD), SPI, and joint aperture as input parameters with the multiple nonlinear regression analysis, in addition to this, other empirical equations also provide acceptable results.

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