Relating rock mass properties with Lugeon value using multiple regression and nonlinear tools in an underground mine site

2017 ◽  
Vol 78 (2) ◽  
pp. 1113-1126 ◽  
Author(s):  
İbrahim Ferid Öge ◽  
Mustafa Çırak
Keyword(s):  
Rock Mass ◽  
Multiple Regression ◽  
Underground Mine ◽  
Rock Mass Properties ◽  
Mine Site ◽  
Mass Properties

Modelling of thermal rock mass properties at the potential sites of a Swedish nuclear waste repository

2009 ◽  
Vol 46 (6) ◽  
pp. 1042-1054 ◽  
Author(s):  
Jan Sundberg ◽  
Pär-Erik Back ◽  
Rolf Christiansson ◽  
Harald Hökmark ◽  
Märta Ländell ◽  
...  
Keyword(s):  
Rock Mass ◽  
Nuclear Waste ◽  
Nuclear Waste Repository ◽  
Rock Mass Properties ◽  
Mass Properties ◽  
Waste Repository

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

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2649
Author(s):  
Ali Kayabasi ◽  
Candan Gokceoglu
Keyword(s):  
Rock Mass ◽  
Nonlinear Regression ◽  
Geological Strength Index ◽  
Empirical Equations ◽  
Regression Equations ◽  
Regression Analyses ◽  
Rock Mass Properties ◽  
Joint Spacing ◽  
Mass Properties ◽  
Karstic Limestone

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.

Volcanic rock-mass properties from Snowdonia and Tenerife: implications for volcano edifice strength

2004 ◽  
Vol 161 (6) ◽  
pp. 939-946 ◽  
Author(s):  
Mark E. Thomas ◽  
Nick Petford ◽  
Edward N. Bromhead
Keyword(s):  
Rock Mass ◽  
Volcanic Rock ◽  
Rock Mass Properties ◽  
Mass Properties

Effect of rock mass properties on blast-induced rock fragmentation

2016 ◽  
Vol 7 (2) ◽  
pp. 89
Author(s):  
Bhanwar Singh Choudhary ◽  
Kumar Sonu ◽  
Keshav Kishore ◽  
Shahid Anwar
Keyword(s):  
Rock Mass ◽  
Rock Fragmentation ◽  
Rock Mass Properties ◽  
Mass Properties
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