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
|Author:||Aladejare, Adeyemi Emman1; Alofe, Emmanuel Damola2; Onifade, Moshood3;|
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
|Online Access:||PDF Full Text (PDF, 0.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021090245006
|Publish Date:|| 2021-09-02
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.
Geotechnical and geological engineering
|Pages:||4427 - 4455|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
1172 Environmental sciences
Open access funding provided by University of Oulu including Oulu University Hospital.
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