Prediction of uniaxial compressive strength of sandstones using petrography-based models

Zorlu K., Gokceoglu C., Ocakoglu F., NEFESLİOĞLU H. A., Acikalin S.

ENGINEERING GEOLOGY, vol.96, pp.141-158, 2008 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 96
  • Publication Date: 2008
  • Doi Number: 10.1016/j.enggeo.2007.10.009
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.141-158
  • Eskisehir Osmangazi University Affiliated: Yes


The uniaxial compressive strength of intact rock is the main parameter used in almost all engineering projects. The uniaxial compressive strength test requires high quality core samples of regular geometry. The standard cores cannot always be extracted from weak, highly fractured, thinly bedded, foliated and/or block-in-matrix rocks. For this reason, the simple prediction models become attractive for engineering geologists. Although, the sandstone is one of the most abundant rock type, a general prediction model for the uniaxial compressive strength of sandstones does not exist in the literature. The main purposes of the study are to investigate the relationships between strength and petrographical properties of sandstones, to construct a database as large as possible, to perform a logical parameter selection routine, to discuss the key petrographical parameters governing the uniaxial compressive strength of sandstones and to develop a general prediction model for the uniaxial compressive strength of sandstones. During the analyses, a total of 138 cases including uniaxial compressive strength and petrographic properties were employed. Independent variables for the multiple prediction model were selected as quartz content, packing density and concavo-convex type grain contact. Using these independent variables, two different prediction models such as multiple regression and ANN were developed. Also, a routine for the selection of the best prediction model was proposed in the study. The constructed models were checked by using various prediction performance indices. Consequently, it is possible to say that the constructed models can be used for practical purposes. (C) 2007 Elsevier B.V. All rights reserved.