University of Oulu

Characterization of physical and mechanical properties of rocks from Otanmäki, Finland

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Author: Osmanu, Abubakar1
Organizations: 1Kaivos- ja rikastustekniikan maisteriohjelma
Format: ebook
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 4.1 MB)
Pages: 97
Persistent link: http://urn.fi/URN:NBN:fi:oulu-202008192808
Language: English
Published: Oulu : A. Osmanu, 2020
Publish Date: 2020-08-19
Thesis type: Master's thesis (tech)
Tutor: Aladejare, Adeyemi
Reviewer: Zhang, Zongxian
Aladejare, Adeyemi
Description:

Abstract

Physical and mechanical properties of rocks are important parameters for geological engineering and design of engineering structures, be it in the civil and/or mining sector. Rock physical properties include density, porosity, etc., and Young’s modulus, Poisson’s ratio and rock strength include some mechanical properties of rocks. These properties can be obtained by laboratory tests. This study aims at characterizing selected rock physical and mechanical properties to assist in predicting rock mass behavior when used in engineering structures, to discuss key rock petrographical features that affect strength and compare the prediction capacities of multiple linear regression and artificial neural network (ANN) models.

The study investigates selected physical and mechanical properties from two igneous rock types, gabbro and granite, from the Otanmäki area, central Finland. The test results were used for the ANN and multiple regression models.

In the analyses, a total of 25 cases from the two rocks were tested for uniaxial compression strength (UCS), Young’s modulus, Poisson’s ratio, Brazilian tensile strength (BTS), density, porosity and water content. Samples were also analyzed for petrographic and chemical compositions. Results from the analyses indicate the importance of adhering to testing standards because of inconsistencies and wide variations observed between nonstandardized as opposed to standardized specimens, and the need for large database for reliable predictive models. It presents ANN techniques as having a good generalization capacity for multi-variable nonlinear prediction.

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Copyright information: © Abubakar Osmanu, 2020. This publication is copyrighted. You may download, display and print it for your own personal use. Commercial use is prohibited.