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Predicting Ground Vibrations Due to Mine Blasting Using a Novel Artificial Neural Network-Based Cuckoo Search Optimization
Natural Resources Research ( IF 5.4 ) Pub Date : 2021-02-12 , DOI: 10.1007/s11053-021-09823-7
Xuan-Nam Bui , Hoang Nguyen , Quang-Hieu Tran , Dinh-An Nguyen , Hoang-Bac Bui

Blasting plays a fundamental role in rock fragmentation, and it is the first preparatory stage in the mining extraction process. However, its undesirable effects, mostly ground vibration, can cause severe damages to the surroundings, such as cracks/collapses of buildings, instability of slopes, deformation of underground space, affect underground water, to name a few. Therefore, the primary purpose of this study was to predict the intensity of ground vibration induced by mine blasting operations with high accuracy, aiming to reduce the severe damages to the surroundings. A novel artificial neural network (ANN)-based cuckoo search optimization (CSO), named as CSO–ANN model, was proposed for this aim based on 118 blasting events that were collected at a quarry mine in Vietnam. Besides, stand-alone models, such as ANN, support vector machine (SVM), tree-based ensembles, and two empirical equations (i.e., USBM and Ambraseys), were considered and developed for comparative evaluation of the performance of the proposed CSO–ANN model. Afterwards, they were tested and validated based on three blasting events in practical engineering. The results revealed that the CSO algorithm significantly improved the performance of the ANN model. In addition, the comparative results showed that the accuracy of the proposed hybrid CSO–ANN model was superior to the other models with MAE (mean absolute error) of 0.178, RMSE (root-mean-squared error) of 0.246, R2 (square of the correlation coefficient) of 0.990, VAF (variance accounted for) of 98.668, and a20-index of 1.0. Meanwhile, the other models only yielded performances in the range of 0.257–0.652 for RMSE, 0.932–0.987 for R2, 20.942–98.542 for VAF and 0.227–0.955 for a20-index. The findings also indicated that explosive charge per borehole has a special relationship with ground vibration intensity. It should be considered and used instead of total explosive charge per blast in some cases, especially for the empirical models.



中文翻译:

基于新型基于人工神经网络的布谷鸟搜索优化,预测矿山爆破引起的地面振动

爆破在岩石破碎中起着基本作用,并且是采矿开采过程中的第一个准备阶段。然而,它的不良影响(主要是地面振动)会严重破坏周围环境,例如建筑物的裂缝/倒塌,斜坡的不稳定性,地下空间的变形,对地下水的影响等。因此,本研究的主要目的是高精度地预测爆破作业引起的地面振动强度,以减少对周围环境的严重破坏。为此,基于在越南一个采石场采集的118起爆炸事件,提出了一种基于人工神经网络(ANN)的布谷鸟搜索优化(CSO)模型,称为CSO–ANN模型。此外,独立模型(例如ANN),支持向量机(SVM),考虑并开发了基于树的合奏和两个经验方程(即USBM和Ambraseys),以比较评估所提出的CSO-ANN模型的性能。之后,根据实际工程中的三个爆破事件对它们进行了测试和验证。结果表明,CSO算法显着提高了ANN模型的性能。另外,比较结果表明,所提出的CSO-ANN混合模型的准确性优于其他模型,MAE(平均绝对误差)为0.178,RMSE(均方根误差)为0.246,他们在实际工程中基于三个爆炸事件进行了测试和验证。结果表明,CSO算法显着提高了ANN模型的性能。另外,比较结果表明,所提出的CSO-ANN混合模型的准确性优于其他模型,MAE(平均绝对误差)为0.178,RMSE(均方根误差)为0.246,他们在实际工程中基于三个爆炸事件进行了测试和验证。结果表明,CSO算法显着提高了ANN模型的性能。另外,比较结果表明,所提出的CSO-ANN混合模型的准确性优于其他模型,MAE(平均绝对误差)为0.178,RMSE(均方根误差)为0.246,R 2(相关系数的平方)为0.990,VAF(占方差)为98.668,a20指数为1.0。同时,其他模型对于RMSE仅产生在0.257-0.652之间的性能,对于R 2仅产生在0.932-0.987之间的性能,对于VAF而言,其产生的性能在20.27-98.542之间,对于20指数的性能在0.227-0.955之间。研究结果还表明,每个钻孔的炸药装药量与地面振动强度有特殊关系。在某些情况下,尤其是对于经验模型,应考虑并使用它代替每次爆炸的总装药量。

更新日期:2021-02-15
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