It is of a high importance to introduce intelligent systems for estimation and optimization of blasting-induced ground vibration because it is one the most unwanted phenomena of blasting and it can damage surrounding structures. Hence, in this paper, estimation and minimization of blast-induced peak particle velocity (PPV) were conducted in two separate phases, namely prediction and optimization. In the prediction phase, an artificial neural network (ANN) model was developed to forecast PPV using as model inputs burden, spacing, distance from blast face, and charge per delay. The results of prediction phase showed that the ANN model, with coefficient of determinations of 0.938 and 0.977 for training and testing stages, respectively, can provide a high level of accuracy. In the optimization phase, the developed ANN model was used as an objective function of firefly algorithm (FA) in order to minimize the PPV. Many FA models were constructed to see the effects of FA parameters on the optimization results. Eventually, it was found that the FA-based optimization was able to decrease PPV to 17 mm/s (or 60% reduction). In addition, burden of 3.1 m, spacing of 3.9 m, and charge per delay of 247 kg were obtained as the values optimized by FA. The results confirmed that both developed techniques of ANN and FA are powerful, accurate, and applicable in estimating and minimizing blasting-induced ground vibration and they can be used with caution in similar fields.

中文翻译：

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人工神经网络和萤火虫算法估计和最小化矿山爆破引起的地面振动。

引入智能系统来估计和优化爆破引起的地面振动非常重要，因为它是爆破中最不希望出现的现象之一，并且会损坏周围的结构。因此，在本文中，在两个独立的阶段，即预测和优化，对爆炸诱发的峰值粒子速度（PPV）进行了估计和最小化。在预测阶段，开发了一个人工神经网络（ANN）模型来预测PPV，它使用负担，间距，距爆破面的距离以及每次延迟的电荷作为模型输入。预测阶段的结果表明，在训练阶段和测试阶段分别具有0.938和0.977的确定系数的ANN模型可以提供较高的准确性。在优化阶段，为了将PPV最小化，将开发的ANN模型用作萤火虫算法（FA）的目标函数。构建了许多FA模型，以查看FA参数对优化结果的影响。最终，发现基于FA的优化能够将PPV降低到17 mm / s（或降低60％）。此外，通过FA优化后的值获得了3.1 m的负担，3.9 m的间距以及247 kg的每次延迟电荷。结果证实，ANN和FA两种已开发的技术都强大，准确，并且可用于估计和最小化爆破引起的地面振动，并且在类似领域中应谨慎使用。发现基于FA的优化能够将PPV降低到17 mm / s（或降低60％）。此外，通过FA优化后的值获得了3.1 m的负担，3.9 m的间距以及247 kg的每次延迟电荷。结果证实，ANN和FA两种已开发的技术都强大，准确，并且可用于估计和最小化爆破引起的地面振动，并且在类似领域中应谨慎使用。发现基于FA的优化能够将PPV降低到17 mm / s（或降低60％）。此外，通过FA优化后的值获得了3.1 m的负担，3.9 m的间距以及247 kg的每次延迟电荷。结果证实，ANN和FA两种已开发的技术都强大，准确，并且可用于估计和最小化爆破引起的地面振动，并且在类似领域中应谨慎使用。