Air overpressure (AOp) and peak particle velocity (PPV) are the most undesirable effects of blasting in mines. It is an urgent need to design a predictor model for AOp and PPV for universal applications to minimize environmental effects and damages that occur due to blasting. The present study attempts to design an artificial neural network (ANN) model for the prediction of AOp and PPV in different conditions. In this study, the blast design parameters (number of holes, depth of the blast hole, stemming length, spacing, burden, distance of vibration monitoring location from the blast site, charge weight per delay) of two active mines (coal and iron ore) are considered in four different conditions for training and testing of the model for the prediction of AOp and PPV. The model was trained and tested in four different combinations of data (trained using data of one mine and tested using data of another mine, trained and tested using data of coal mine, trained and tested using data of an iron ore mine, trained and tested using combined data of both the mines) for examining the applicability in different conditions. The results indicate that R² values are ranged from 0.886 to 0.908 and 0.8728 to 0.8959, respectively, in the prediction of PPV and AOp. A comparative study of the performances of the developed model with the other empirical model is also demonstrated. For this, the site constants of different empirical models were estimated individually for both the mines. The study results indicated that the ANN model performs much better than all the empirical models. It can be inferred from the results that blast vibration cannot be accurately predicted only from charge per delay and distance from the blast hole. The ANN model was considered many other factors and thus can predict the vibration level more accurately.

空气超压 (AOp) 和峰值粒子速度 (PPV) 是矿井爆破最不利的影响。迫切需要设计一种适用于普遍应用的 AOp 和 PPV 预测模型，以最大限度地减少爆破造成的环境影响和破坏。本研究试图设计一种人工神经网络 (ANN) 模型，用于在不同条件下预测 AOp 和 PPV。在这项研究中，两个活跃矿山（煤和铁矿石）的爆破设计参数（孔数、爆破孔深度、堵塞长度、间距、载荷、振动监测位置距爆炸现场的距离、每次延迟的装药重量） ) 在四种不同的条件下被考虑用于训练和测试模型以预测 AOp 和 PPV。该模型在四种不同的数据组合中进行训练和测试（使用一个矿山的数据进行训练，使用另一个矿山的数据进行测试，使用煤矿的数据进行训练和测试，使用一个铁矿的数据进行训练和测试，训练和测试）使用两个矿山的综合数据）来检查在不同条件下的适用性。结果表明，R²值的范围分别为 0.886 至 0.908 和 0.8728 至 0.8959，分别用于预测 PPV 和 AOp。还展示了对开发模型与其他经验模型性能的比较研究。为此，对两个矿山分别估算了不同经验模型的场地常数。研究结果表明，人工神经网络模型的性能比所有经验模型都要好得多。从结果可以推断出，仅从每次延迟的装药量和距爆破孔的距离无法准确预测爆破振动。ANN 模型考虑了许多其他因素，因此可以更准确地预测振动水平。