Evaluation and prediction of the rock static and dynamic parameters

Determination of rock properties as materials, foundations and sites of civil projects, is one of the priorities. This study aimed to assess the relationship between static elastic modulus (Es) and dynamic elastic modulus (Ed) and to estimate static properties and shear wave velocity (Vs) using simple regression (SR), support vector regression (SVR), multivariate linear regression (MVLR) and artificial neural network (ANN) methods based on compressional wave velocity and physical properties. For this purpose, first geomechanical characteristics of 80 specimens of the limestone rocks from the Asmari and Ilam formations in Karun 4 (K4) and Karun 2 (K2) dam sites, in southwestern Iran were measured. Then, data related to the various studies from different parts of the world were collected and a global relationship was presented. The average Ed obtained from the various relationships of different researchers was equal to 19.90 GPa, which is less than the average Ed of the present study (31.20 GPa). According to the most accurate fit, the presented relationship between Es and Ed was power. The analysis of all model hypotheses by MVLR showed that it is possible to estimate the static and dynamic properties. Predicting dynamic and static parameters of the limestone rocks using Sigmoid transfer functions and Hyperbolic tangents and various training rules showed that the Sigmoid transfer function and Levenberg-Marquardt training law have the best performance in predictions. Comparison of the methods performance in estimating Vs and static properties showed that the SVR has higher accuracy than other methods.