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Determination of Unconfined Compressive Strength of rock is important in Geotechnical Engineering. Although laboratory test is the most direct way of rock compressive strength estimation, but UCS determination in laboratory is problematic if the rock masses are weathered because obtaining proper core segments is difficult. Hence, using index testing as an alternative for UCS prediction is investigated by researchers. It is well established that Indirect/Brazilian Tensile Strength is related to UCS. In this paper, to develop a correlation between UCS and BTS, collected data of laboratory tests on dry limestone specimen including 20 Unconfined Compression Tests and 20 Brazilian Tests have been used. Then, to apply Artificial Neural Networks, a Radial Basis Network is developed to reach a relationship between BTS and UCS. Based on the low Mean Squared Error of the network, a new correlation is introduced for prediction of the UCS of limestone core samples from BTS data.
Unconfined Compressive Strength (UCS) of rock is considered as an essential parameter in analysis of geotechnical problems such as rock blasting and tunneling. Although laboratory test is the most reliable and direct method for estimating UCS, direct determination of UCS in laboratory is time-consuming and expensive. In addition, in direct method of UCS determination, having sufficient number of high quality rock samples is a prerequisite. However, it is not always possible to extract proper cores for sampling purpose in highly weathered rocks. Therefore, the use of various correlations for UCS prediction has been highlighted in the literatures. These correlations often relate other rock index parameters such as point load index, rebound number of Schmidt hammer and indirect tensile strength of the rock to UCS. Implementing such correlations is of interest, mainly due to the fact that rock index tests have the advantages of being relatively fast and economical. Brazilian Test (BT) is used for indirect determination of tensile strength of rock samples. It is established that Brazilian tensile strength is related to UCS. One of the most agreed correlations between UCS and indirect tensile strength or Brazilian Tensile Strength (BTS) of the rock is highlighted in the study by Sheorey (1997). According to his study, the compressive strength of the rock is approximately 10 times its tensile strength. Nevertheless, Sheorey`s strength ratio variation is high (Cai 2006) and consequently cannot be generalized due to the fact that rock behavior varies from place to place and is site specific. This paper proposes a new correlation between UCS and BTS of specific type of rock i.e. limestone as the relationship between compressive and tensile strength of rock depends on rock type (Brook 1993).
Hisatake, M. (Kinki University, Osaka) | Cording, E.J. (University of Illinois, Ill) | Ito, T. (Osaka Institute of Technology, Osaka) | Sakurai, S. (Kobe University, Kobe) | Phien-Weja, N. (Asian Institute of Technology, Bangkok)
The shear strength of rocks along discontinuities is one of the key parameters for the determination of rock slope stability, the stability of rocks during underground space development and tunneling. Its value is influenced by numerous factors including surface roughness, which is one of the most widely investigated discontinuity property. The present paper introduces a simple graphical methodology for the classification of the surface roughness of rocks, based on the example of two different rock types, Bátaapáti Granites (Hungary), and Mont Terri Opalinus Claystones (Switzerland). The 3D surface of 24 rock samples was digitized using a photogrammetric surface detection method with the help of the ShapeMetrix3D software. The plane of each rock surface was defined by fitting a linear regression plane to the surface data. The distance between the data points of the surface roughness model and the regression plane was measured, and cumulative frequency diagrams of the measured distance values were constructed. This procedure allowed to define three surface roughness categories. The methodology proposed represents a promising new approach to surface roughness quantification, which could improve shear strength estimation.
Shear strength of rocks is one of the key input parameters for stability analyses of rock masses. The design of appropriate supporting systems (type and strength) used to ensure the ideal degree of safety for people interacting with any engineered rock surface depends on the results of these analyses. However, the value of the shear strength is influenced by several factors, such as the mechanical properties of the intact rock and the discontinuities, as well as the laboratory testing methods and testing machines (Barton, 1973, 2013; Grasselli 2001; Buocz, 2016, 2017a; Dzugala et al., 2017). Therefore, the exact calculation of this parameter is very challenging. Among others, surface roughness is one of the most widely investigated discontinuity property with significant influence on the direct shear strength of rocks. Forty years ago, Barton presented for the first time 10 typical 2D surface roughness profiles, which defined as many value intervals for the Joint Roughness Coefficient (JRC). Once implemented into his rock shear strength model, these values helped to provide an appropriate estimate of the shear strength (Barton and Choubey, 1977). With the fast development of technology and the increasing precision of methodologies for surface detection, i.e., laser scanning or photogrammetry (Gaich et al. 2006), the 3D analysis of surface roughness gained again a central attention (Ge et al. 2015). Different theories were elaborated for the quantification of surface roughness and the determination of values for JRC in three dimensions (Zhao, 1997; Grasselli 2001, Bae et al., 2011). However, due to its simplicity, the well-accepted 2D surface roughness determination still remains in use in practice.