首页 | 本学科首页   官方微博 | 高级检索  
     


Prediction of uniaxial compressive strength of sandstones using petrography-based models
Authors:K. Zorlu   C. Gokceoglu   F. Ocakoglu   H.A. Nefeslioglu  S. Acikalin
Affiliation:

aDepartment of Geological Engineering, Applied Geology Division, Mersin University, Ciftlikkoy Mersin, 33342, Turkey

bDepartment of Geological Engineering, Applied Geology Division, Hacettepe University, Beytepe Ankara 06800, Turkey

cDepartment of Geological Engineering, General Geology Division, Eskisehir Osmangazi University, 26030 Bademlik, Eskisehir, Turkey

dGeneral Directorate of Mineral Research and Exploration, Department of Geological Research, 06520 Ankara, Turkey

Abstract: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.
Keywords:Sandstone   Multiple regression   Uniaxial compressive strength   Artificial neural networks   Petrography
本文献已被 ScienceDirect 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号