Estimation of Mechanical Properties by Statistical Analysis, Artificial Neural Network and Support Vector Regression "Case Study: Samples Related To Godar-Khosh Reservoir Dam Site"

Authors

1 Department of Mining Engineering, Birjand University of Technology, Birjand, Iran

2 Department of Civil Engineering, Faculty of Engineering, Arak University, Arak, Iran

3 School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

4 Department of Industrial Engineering, Birjand University of Technology, Birjand, Iran

Abstract

Due to the difficulties of conducting tests, especially in weak rocks and the cost of these experiments, by examining the relationships between their mechanical and physical properties can be provided and reduce the cost of tests to identify mechanical properties (Minaeian and Ahangari, 2013; Azadan and Ahangari, 2014).
     In this study, petrographic, physical and mechanical experiments on 62 cores of shale and marl of Gurpi Formation were conducted in Godar-Khosh dam site, west of Iran. Non-destructive tests were performed on cores according to the ISRM standard. Physical properties such as water absorption, density and porosity of the samples were determined according to the ISRM standard. Also, uniaxial compressive strength (UCS) test according to the ASTM standard D2938 (ASTM, 1986) was performed. For each sample, the modulus of dynamic elasticity (Ed) and the dynamic Poisson ratio were calculated (Goodman, 1989). Using statistical analysis, artificial neural network (ANN( and  support vector regression (SVR) with radial base kernel function, several relationships for estimating UCS, Es and shear wave velocity were presented. The root mean square error (RMSE), the mean absolute percentage error (MAPE) and the variance account for (VAF) were also used to evaluate the results.

Keywords

Main Subjects

References

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Journal of Civil and Environmental Engineering
Volume 52.4, Issue 109 - Serial Number 109
Winter 1401
February 2023
Pages 173-190

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