Empirical estimation of rock mass deformation modulus of rocks:comparison of intact rock properties and rock mass classifications as inputs

Abstract Deformation modulus of rock mass (Em) is an important parameter for the analysis and design of mining engineering projects. However, field tests for measuring deformation modulus of rock mass are difficult, time-consuming, and capital intensive. This has led to the development of numerous empirical models for estimating rock mass deformation modulus, which are in different forms and scattered in the literature. The numerous models available in the literature use different types of inputs. Therefore, this study provides a comprehensive compilation of different empirical models for estimating the deformation modulus of rock masses. The compiled models are grouped based on their type of input parameter(s) into three categories such as those using intact rock properties, rock mass classification indices, and combination of intact rock properties and rock mass classification indices. Then, a comparative analysis was performed using absolute average relative error percentage (AAREP) and variance accounted for (VAF) to assess the reliability of using different types of inputs for estimation of deformation modulus of rock masses using data from two sites. The results of the analyses show that rock mass classification indices are the most reliable indices for estimating the deformation modulus of rock masses among the categories considered for analyses. For AAREP analyses in the two illustrative examples considered in this study, models (7 out of 10) using rock mass classification indices in the estimation of Em have the best performances with AAREP values ranging from 24.07 to 55.15%. For VAF analyses in the two examples, models (8 out of 10) using rock mass classification indices in the estimation of Em have the best performances with values ranging from 59.81 to 88.11%. The lowest errors and highest deviation similarities from models using rock mass classification indices indicate that they produce the most reliable estimations of Em. It is important to note that the reliability of deformation modulus estimated from empirical models depends on the quality of input data as the models performed differently across the sites used in this study. This study therefore provides a compilation of available models for estimating deformation modulus, performance evaluation of available models for estimating deformation modulus, and guidelines for selecting appropriate model for estimating deformation modulus of rock mass

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

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