Strength classification of rock material based on textural properties


Ozturk C. A. , Nasuf E.

TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY, cilt.37, ss.45-54, 2013 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 37
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1016/j.tust.2013.03.005
  • Dergi Adı: TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
  • Sayfa Sayıları: ss.45-54

Özet

Rock, as a construction material, has great importance during the construction and service phases in a rock environment. The classification of rock materials based on their strength behavior provides a simple and fast solution to determine the type and application of support system as well as the method for opening underground structures. Intact rock materials are generally classified with regard to the strength, such as uniaxial compressive and point load strength. Rock texture, which consists of grains and matrix, directly affects the strength. The relation between the textural and mechanical properties of rock materials has been investigated, and rock texture was quantified from the texture coefficient (TC). The coefficient can be used to put a number on rock textures with experimental studies carried out on thin sections of rock material using image analysis. The main scope of this research is to classify the rock material according to its TC values based on the binary and fuzzy domain. In this study. TC is divided into five classes from very low to very high, and a fuzzy model is proposed to predict the uniaxial compressive strength from TC. A dataset is prepared to construct an objective study with 12 litho-type rock materials from 19 locations in Turkey. The binary and fuzzy classification as well as fuzzy model for the prediction of compressive strength is also applied to the dataset to illustrate the use of the proposed classification and model for underground construction in rock engineering. The model is applied to determine the intact rock material's rating in rock mass rating classification (RMR) from the proposed classification as well as from the fuzzy model. The results of the example encourage the application of the proposed methods, especially for pre-feasibility studies of rock engineering projects. (C) 2013 Elsevier Ltd. All rights reserved.