Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees

Tiryaki B.

ENGINEERING GEOLOGY, vol.99, pp.51-60, 2008 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 99
  • Publication Date: 2008
  • Doi Number: 10.1016/j.enggeo.2008.02.003
  • Journal Name: ENGINEERING GEOLOGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.51-60
  • Hacettepe University Affiliated: Yes

Abstract

Mechanical rock excavation projects require uniaxial compressive strength (UCS) and static modulus of elasticity (E) of the intact rock material. High-quality core specimens of proper geometry are needed for the direct determination of these parameters. However, it is not always possible to obtain suitable specimens from highly fractured and/or weathered rocks for this purpose. Therefore, models predicting UCS and E based on rock index tests and intact rock properties have become alternative methods.