A Modified Correlation Using Statistical Techniques in Estimating the Uniaxial Compressive Strength of Sedimentary Rocks Based on P-Wave Velocity- A Case Study

Abstract

Uniaxial Compressive Strength (UCS) is one of the main and most widely used rock mechanical properties in the construction and rock engineering projects. Due to difficulties, destructive tests, demanding and time consuming for preparation of high-quality core samples and associated sophisticated test equipment, application of indirect method to find possible empirical relations between the UCS and other rock mechanical parameters not only is interested but also has played an important role. Among the available indirect methods, P-wave velocity (VP) due to relation with rock mechanical parameters, non destructive and easy measurement method both in field and laboratory conditions can be a good and feasible alternative to determine the mechanical properties of rock materials. In this paper, the generated empirical models as a function of VP for estimating the engineering rocks properties using one of the previous proposed relations is modified for the study area. The accuracy of modified relation was tested using conducted comparison with previous correlations as well as different statistical error and analytical graphs. To achieve the aim, a database from extensive experimental tests on 36 retrieved cores consisting of 11 limestone, 15 marlstone and 10 sandstone form drilled boreholes in various locations of Chamshir dam site in southwest of Iran were collected. The VP, UCS, porosity (n), saturated and dry density (γ) and elasticity modulus (E) were measured parameters. The used data were implemented for linear, nonlinear and multivariable regression analyses between VP and relevant quantified rock parameters. The results showed that the nonlinear power regression between VP and UCS (R2 = 0.75) was improved in modified version (R2 = 0.894). In case of multivariable nonlinear analyses the power correlations between VP, γ and ‘n’ for estimation of UCS showed better performance. Moreover, the observed adaptability of the presented results with other previous proposed correlations can be a good evidence for applicability of obtained equations in the selected area.