Rock brittleness is one of the most significant properties of rock having a major impact not only on the failure process of intact rock but also on the response of rock mass to rock excavation. In fact, the brittleness is a combination of rock properties including not only the uniaxial compressive strength (UCS) and Brazilian tensile strength (BTS) but also density and porosity of rocks. Due to that, the brittleness should be examined very carefully for any excavation projects, i.e., mechanized excavation, drilling and blasting. The aim of this paper is to compare the strength-based brittleness indices with both the rock brittleness index (BIo), directly obtained via punch penetration test (PPT) and also estimated via Yagiz’s approach (BI1) as a function of strengths and density of rocks. For the aim, database including more than 45 tunnel cases are used to compute common rock brittleness indices (BI2, BI3, BI4), different combination of UCS and BTS. Further, these indices are compared with both BIo and BI1 as well as each other. It is found that the BIo and BI1 have a significant relations (ranging of determination coefficients (r2) from 0.69 to 0.88 with strength-based brittleness indices commonly used in practice. Also, based on findings, several rock brittleness classifications are also revised herein.

Raise boring machines (RBMs) are commonly utilized for drilling of shaft and other inclined structures in mining and civil applications. This paper aim to introduce several empirical equations to estimate the performance and operational parameters of RBMs. For the aim, datasets having rock properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), brittleness (BI), rock quality designation (RQD), elastic modules (E) and Poisson ratio (v); and also RBMs operational parameters including Instantaneous penetration rate (IPR), Specific Energy (SE), Pushing Force (Fpush), reamerhead power (Pw) and rotational speed (RPM), were established from published case studies. After that, re-established dataset was utilized to develop multiple regression models to predict the performance and operational parameters of RBM. It is found that IPR, SE, Fpush, Pw and RPM could be estimated using several alternative rock properties with coefficients of determination ranging from 0.77 to 0.95. Based on utilized dataset, it is also concluded that RQD and BI are the most common rock properties for estimating the performance and operational parameters of RBMs.