University of Oulu

Aladejare, A.E., Alofe, E.D., Onifade, M. et al. Empirical Estimation of Uniaxial Compressive Strength of Rock: Database of Simple, Multiple, and Artificial Intelligence-Based Regressions. Geotech Geol Eng 39, 4427–4455 (2021). https://doi.org/10.1007/s10706-021-01772-5

Empirical estimation of uniaxial compressive strength of rock : database of simple, multiple, and artificial intelligence-based regressions

Saved in:
Author: Aladejare, Adeyemi Emman1; Alofe, Emmanuel Damola2; Onifade, Moshood3;
Organizations: 1Oulu Mining School, University of Oulu, Oulu, Finland
2Faculty of Earth Sciences, University of Uppsala, Uppsala, Sweden
3Department of Mining and Metallurgical Engineering, University of Namibia, Windhoek, Namibia
4Departent of Mining Engineering, Federal University of Technology, Akure, Nigeria
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.5 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021090245006
Language: English
Published: Springer Nature, 2021
Publish Date: 2021-09-02
Description:

Abstract

Empirical relationships for estimating Uniaxial Compressive Strength (UCS) of rock from other rock properties are numerous in literature. This is because the laboratory procedure for determination of UCS from compression tests is cumbersome, time consuming, and often considered expensive, especially for small to medium-sized mining engineering projects. However, these empirical models are scattered in literature, making it difficult to access a considerable number of them when there is need to select empirical model for estimation of UCS. This often leads to bias in estimated UCS data as there may be underestimation or overestimation of UCS, because of the site-specific nature of rock properties. Therefore, this study develops large database of empirical relationships between UCS and other rock properties that are reported in literatures. Statistical analysis was performed on the regression equations in the database developed. The typical ranges and mean of data used in developing the regressions, and the range and mean of their R² values were evaluated and summarised. Most of the regression equations were found to be developed from reasonable quantity of data with moderate to high R² values. The database can be easily assessed to select appropriate regression equation when there is need to estimate UCS for a specific site.

see all

Series: Geotechnical and geological engineering
ISSN: 0960-3182
ISSN-E: 1573-1529
ISSN-L: 0960-3182
Volume: 39
Issue: 6
Pages: 4427 - 4455
DOI: 10.1007/s10706-021-01772-5
OADOI: https://oadoi.org/10.1007/s10706-021-01772-5
Type of Publication: A1 Journal article – refereed
Field of Science: 1171 Geosciences
1172 Environmental sciences
Subjects:
Funding: Open access funding provided by University of Oulu including Oulu University Hospital.
Copyright information: © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
  https://creativecommons.org/licenses/by/4.0/