Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Empirical Estimation of Uniaxial Compressive Strength of Rock: Database of Simple, Multiple, and Artificial Intelligence-Based Regressions
https://orcid.org/0000-0002-0599-3799
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 $ R^{2} $ values were evaluated and summarised. Most of the regression equations were found to be developed from reasonable quantity of data with moderate to high $ R^{2} $ values. The database can be easily assessed to select appropriate regression equation when there is need to estimate UCS for a specific site.
Empirical Estimation of Uniaxial Compressive Strength of Rock: Database of Simple, Multiple, and Artificial Intelligence-Based Regressions
https://orcid.org/0000-0002-0599-3799
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 $ R^{2} $ values were evaluated and summarised. Most of the regression equations were found to be developed from reasonable quantity of data with moderate to high $ R^{2} $ values. The database can be easily assessed to select appropriate regression equation when there is need to estimate UCS for a specific site.
Empirical Estimation of Uniaxial Compressive Strength of Rock: Database of Simple, Multiple, and Artificial Intelligence-Based Regressions
https://orcid.org/0000-0002-0599-3799
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 $ R^{2} $ values were evaluated and summarised. Most of the regression equations were found to be developed from reasonable quantity of data with moderate to high $ R^{2} $ values. The database can be easily assessed to select appropriate regression equation when there is need to estimate UCS for a specific site.
Empirical Estimation of Uniaxial Compressive Strength of Rock: Database of Simple, Multiple, and Artificial Intelligence-Based Regressions
Aladejare, Adeyemi Emman (Autor:in) / Alofe, Emmanuel Damola (Autor:in) / Onifade, Moshood (Autor:in) / Lawal, Abiodun Ismail (Autor:in) / Ozoji, Toochukwu Malachi (Autor:in) / Zhang, Zong-Xian (Autor:in)
2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
BKL:
57.00$jBergbau: Allgemeines
/
38.58
Geomechanik
/
57.00
Bergbau: Allgemeines
/
56.20
Ingenieurgeologie, Bodenmechanik
/
38.58$jGeomechanik
/
56.20$jIngenieurgeologie$jBodenmechanik
Estimation of Intact Rock Uniaxial Compressive Strength Using Advanced Machine Learning
| Springer Verlag | 2024
Estimation of Intact Rock Uniaxial Compressive Strength Using Advanced Machine Learning
| Springer Verlag | 2024
| British Library Online Contents | 2017