Ground vibration is one of the major hazards produced by rock-blasting operation. The accurate prediction of vibration is necessary for designing controlled blasting parameters. The existing vibration predictors consider maximum explosive charge weight per delay and distance as the parameters responsible for ground vibration. These predictors are based on the assumption that the geometrical parameters of the blast will be constant for a site. However, the mining sites with bigger production targets have varying geometrical parameters to suit the excavator utility. Accordingly, the other blast design parameters will also have an impact on ground vibration intensity. A principal component analysis is a dimension reduction technique. This technique along with multivariate logarithmic regression has been used in this paper to predict the ground vibration. The technique has classified the blast design parameters into four principal components. The regression with the scores from these principal components has been carried out. The evaluation of the model performance of predictors along with the existing empirical predictors has been carried out using R² and RMSE values. The evaluation suggests that the predictor with logarithmic regression followed by principal component analysis gives better performance with respect to the existing empirical predictors.

地面振动是岩石爆破作业产生的主要危害之一。振动的准确预测对于设计可控爆破参数是必要的。现有的振动预测器将每个延迟和距离的最大炸药重量作为负责地面振动的参数。这些预测器基于爆炸的几何参数对于一个场地是恒定的假设。然而，具有较大生产目标的矿场具有不同的几何参数以适应挖掘机的实用性。因此，其他爆破设计参数也会对地面振动强度产生影响。主成分分析是一种降维技术。该技术连同多元对数回归已在本文中用于预测地面振动。该技术将爆破设计参数分为四个主要部分。已经对这些主成分的分数进行了回归。已经使用现有的经验预测器对预测器的模型性能进行了评估R ²和 RMSE 值。评估表明，对数回归和主成分分析的预测器相对于现有的经验预测器具有更好的性能。