Prediction of The Uniaxial Compressive Strength of Rocks Materials


Ceryan N., Can N. K.

HANDBOOK OF RESEARCH ON TRENDS AND DIGITAL ADVANCES IN ENGINEERING GEOLOGY, pp.31-96, 2018 (Journal Indexed in SCI) identifier

  • Publication Type: Article / Article
  • Volume:
  • Publication Date: 2018
  • Doi Number: 10.4018/978-1-5225-2709-1.ch002
  • Title of Journal : HANDBOOK OF RESEARCH ON TRENDS AND DIGITAL ADVANCES IN ENGINEERING GEOLOGY
  • Page Numbers: pp.31-96

Abstract

This study briefly will review determining UCS including direct and indirect methods including regression model soft computing techniques such as fuzzy interface system (FIS), artifical neural network (ANN) and least sqeares support vector machine (LS-SVM). These has advantages and disadvantages of these methods were discussed in term predicting UCS of rock material. In addition, the applicability and capability of non-linear regression, FIS, ANN and LS-SVM SVM models for predicting the UCS of the magnatic rocks from east Pondite, NE Turkey were examined. In these soft computing methods, porosity and P-durability secon index defined based on P-wave velocity and slake durability were used as input parameters. According to results of the study, the performanc of LS-SVM models is the best among these soft computing methods suggested in this study.