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The geometry of rock blocks, including shape and size, is a crucial factor affecting the quarry's profitability. The block's dimension is limited by the fracture network properties, especially discontinuity's orientation, and spacing. Volumetric joint count (Jv), and weighted joint density (WJd) are two well-known indexes to determine the average block's volume. In this investigation the discontinuity properties of eight quarries are determined through the line and aerial mapping surveys. The investigation was performed along 2258 m scanline in 63 slope benches. Different indexes were used to compare the obtainable rock block's geometry. An empirical index was proposed considering different aspects of discontinuity network to increase the accuracy of measurements. The index was introduced based on the number of joints in each joint set, average spacing of joints in each joint set, and the total number of joints recorded during the investigation. The joint mean spacing index allows us to estimate the average volume of blocks and evaluate the quarry's profitability. By increasing the ratio of the joint mean spacing index, the obtainable rock block's volume and mine profitability increase. [ABSTRACT FROM AUTHOR]

Realistic discontinuity network modelling of rocks requires the accurate input of the three-dimensional discontinuity orientation distribution (3DDOD), but in practice, access to such three-dimensional information is limited. Fouché recently developed a method for estimating the 3DDOD from one-dimensional scanline-mapping observations. However, little is known about the error in such 3DDOD estimates. This study focuses on (1) the error and its possible contributors, (2) error prediction, and (3) error mitigation. An error investigation based on synthetic discontinuity geometry data reveals that the 3DDOD estimates are not always accurate. The error is significantly impacted by three factors: the Fisher constant (к), the angle between the major discontinuity plane and the mapping scanline (θ), and the discontinuity sample size (n), with larger к , θ , or n values contributing to lower errors. This к -, θ -, and n- dependent error appears to be inherently related to the sample density, which is defined as the pole number per unit cell (1° × 1°) in the Fouché method. Additionally, a predictor of the error based on an error-and-factors empirical relationship is developed to provide precise error prediction and optimize the scanline sampling for error mitigation. The weakness of the developed predictor is also disclosed. [ABSTRACT FROM AUTHOR]