Faculty of Engineering, Tarbiat Modares University, Tehran, Iran.
Department of Mining Engineering, Urmia University of Technology, Urmia, Iran.
Sci Rep. 2023 Apr 21;13(1):6591. doi: 10.1038/s41598-023-33796-7.
Ground vibration due to blasting is identified as a challenging issue in mining and civil activities. Peak particle velocity (PPV) is one of the blasting undesirable consequences, which is resulted during emission of vibration in blasted bench. This study focuses on the PPV prediction in the surface mines. In this regard, two ensemble systems, i.e., the ensemble of artificial neural networks and the ensemble of extreme gradient boosting (EXGBoosts) were developed for PPV prediction in one of the largest lead-zinc open-pit mines in the Middle East. For ensemble modeling, several ANN and XGBoost base models were separately designed with different architectures. Then, the validation indices such as coefficient determination (R), root mean square error (RMSE), mean absolute error (MAE), the variance accounted for (VAF), and Accuracy were used to evaluate the performance of the base models. The five top base models with high accuracy were selected to construct an ensemble model for each of the methods, i.e., ANNs and XGBoosts. To combine the outputs of the top base models and achieve a single result stacked generalization technique, was employed. Findings showed ensemble models increase the accuracy of PPV predicting in comparison with the best individual models. The EXGBoosts was superior method for predicting of the PPV, which obtained values of R, RMSE, MAE, VAF, and Accuracy corresponding to the EXGBoosts were (0.990, 0.391, 0.257, 99.013(%), 98.216), and (0.968, 0.295, 0.427, 96.674(%), 96.059), for training and testing datasets, respectively. However, the sensitivity analysis indicated that the spacing (r = 0.917) and number of blast-holes (r = 0.839) had the highest and lowest impact on the PPV intensity, respectively.
爆破产生的地面振动被认为是采矿和土木工程中的一个挑战性问题。峰值粒子速度 (PPV) 是爆破不良后果之一，它是在爆破台面振动释放时产生的。本研究专注于露天矿山的 PPV 预测。为此，针对中东最大的铅锌露天矿之一，开发了两种集成系统，即人工神经网络集成和极端梯度提升 (EXGBoosts) 集成，用于 PPV 预测。对于集成建模，分别使用不同的架构设计了几个 ANN 和 XGBoost 基础模型。然后，使用系数确定 (R)、均方根误差 (RMSE)、平均绝对误差 (MAE)、方差解释 (VAF) 和准确率等验证指标来评估基础模型的性能。选择五个具有高精度的顶级基础模型，为每种方法（即 ANN 和 XGBoost）构建一个集成模型。为了结合顶级基础模型的输出并实现单个结果，采用堆叠泛化技术。研究结果表明，与最佳单个模型相比，集成模型提高了 PPV 预测的准确性。EXGBoosts 是预测 PPV 的优越方法，它获得了与 EXGBoosts 相对应的 R、RMSE、MAE、VAF 和准确率的值分别为 (0.990, 0.391, 0.257, 99.013(%)、98.216) 和 (0.968, 0.295, 0.427, 96.674(%)、96.059)，用于训练和测试数据集。然而，敏感性分析表明，间距 (r = 0.917) 和爆破孔数量 (r = 0.839) 对 PPV 强度的影响最大和最小。
