Abstract
Back break, defined as rock fracturing that extends beyond the last row of blastholes into the intended final wall or the next bench, is a persistent problem in open-pit mining and quarrying that compromises slope stability, increases dilution, and raises downstream costs. This study develops an empirical multiple linear regression (MLR) model to predict backbreak from five commonly logged blast design parameters: burden, spacing, stemming length, powder factor, and a geometric stiffness ratio, following the variable framework established in prior backbreak-prediction studies [3], [5]. A dataset of 36 field blast records was compiled, and predictor collinearity, model significance, and residual behaviour were assessed using standard regression diagnostics. A reduced four-variable model (burden, spacing, powder factor, and geometric stiffness) explained 97.6% of the variance in the observed dataset (in-sample R² = 0.976, adjusted R² = 0.972, F(4,31) = 310.1, p < 0.001), with a root-mean-square error of 0.35 m and mean absolute percentage error of 9.4%; six-fold cross-validation produced a mean out-of-fold R² of 0.84, indicating somewhat weaker but still reasonable performance on held-out data. Burden and powder factor were the strongest positive predictors, while spacing exerted a negative, moderating effect, consistent with established blast mechanics. The results support the use of routinely collected design parameters for practical, low-cost backbreak forecasting and pre-blast design adjustment, while highlighting multicollinearity among burden-related variables as a limitation warranting cautious interpretation of individual coefficients.