Blasting is still being considered to be one the most important applicable alternatives for conventional excavations. Ground vibration generated due to blasting is an undesirable phenomenon which is harmful for the nearby structures and should be prevented. In this regard, a novel intelligent approach for predicting blast-induced PPV was developed. The distinctive Jaya algorithm and high efficient extreme gradient boosting machine (XGBoost) were applied to obtain the goal, called the Jaya-XGBoost model. Accordingly, 150 sets of data composed of 13 controllable and uncontrollable parameters are chosen as input independent variables and the measured peak particle velocity (PPV) is chosen as an output dependent variable. Also, the Jaya algorithm was used for optimization of hyper-parameters of XGBoost. Additionally, six empirical models and several machine learning models such as XGBoost, random forest, AdaBoost, artificial neural network and Bagging were also considered and applied for comparison of the proposed Jaya-XGBoost model. Accuracy criteria including determination coefficient (R²), root-mean-square error (RMSE), mean absolute error (MAE), and the variance accounted for (VAF) were used for the assessment of models. For this study, 150 blasting operations were analyzed. Also, the Shapley Additive Explanations (SHAP) method is used to interpret the importance of features and their contribution to PPV prediction. Findings reveal that the proposed Jaya-XGBoost emerged as the most reliable model in contrast to other machine learning models and traditional empirical models. This study may be helpful to mining researchers and engineers who use intelligent machine learning algorithms to predict blast-induced ground vibration.

爆破仍然被认为是常规挖掘的最重要的适用替代方法之一。由于爆破而产生的地面振动是一种不良现象，对附近的结构有害，应予以防止。在这方面，一种预测爆炸诱发PPV的新型智能方法发展了。应用独特的Jaya算法和高效的极端梯度提升机（XGBoost）来获得目标，称为Jaya-XGBoost模型。因此，选择由13个可控和不可控参数组成的150组数据作为输入自变量，并选择测得的峰值粒子速度（PPV）作为输出因变量。此外，Jaya 算法用于优化 XGBoost 的超参数。此外，还考虑并应用了六个经验模型和几种机器学习模型，例如 XGBoost、随机森林、AdaBoost、人工神经网络和 Bagging，用于比较所提出的 Jaya-XGBoost 模型。精度标准包括确定系数 (R ²)、均方根误差 (RMSE)、平均绝对误差(MAE) 和解释的方差 (VAF) 用于评估模型。在这项研究中，分析了 150 次爆破作业。此外，使用 Shapley Additive Explanations (SHAP) 方法来解释特征的重要性及其对 PPV 预测的贡献。结果表明，与其他机器学习模型和传统经验模型相比，所提出的 Jaya-XGBoost 成为最可靠的模型。这项研究可能对使用智能机器学习算法来预测爆炸引起的地面振动的采矿研究人员和工程师有所帮助。