Kuz-Ram Calibration Dataset for 12 Rock Types

The Kuz-Ram model is the most widely used fragmentation prediction tool in blast engineering, combining the Kuznetsov energy-based fragmentation equation with the Rosin-Rammler distribution function to estimate the size distribution of fragmented rock. This dataset provides empirical calibration data for 12 rock types—including granite, basalt, limestone, sandstone, iron ore, copper porphyry, and coal—enabling engineers to validate model predictions against measured field data. Each entry includes the mean fragment size (X50), uniformity index (n), characteristic size (Xc at 63.2% passing), and the in-situ block size distribution parameters (JRC, JCS, RQD). The dataset supports sensitivity analysis by providing multiple calibration points at different powder factors and charge configurations. Engineers use this data to back-calculate the Kuz-Ram model constants for site-specific conditions, improving prediction accuracy for production blast designs. The CSV format enables direct import into spreadsheet-based blast design tools and fragmentation analysis software.