Blasting operation is among the most common methods of rock excavation in the civil engineering and mining operations. Ground vibration is the most unfavorable effect of blasting operation such that failure to accurately control this problem causes damage to adjacent structures. In this regard, geotechnical engineers face the challenge of accurately predicting blast-induced ground vibrations. Geographical location of Bakhtiari Dam (located in the southwest of Iran) is needed to construct an access road to its nearest city through the rough topography. To establish the access road in the plan, blasting operation methods have been used. In this study, blast-induced ground vibrations in the study area are evaluated using five common functional forms of the empirical model and their corrected regression coefficient for the area. Then, the ground vibrations generated in the study area were predicted by designing an artificial neural network model. For this purpose, the maximum charge per delay, the distance between the blast point and monitoring stations, and the ground vibration values were surveyed for 80 blast events, and their necessary parameters were determined. A total of 64 datasets were used to obtain the coefficients of the empirical models and to create the artificial neural network model. In addition, 16 datasets were used to estimate the performance and accuracy of each model. To measure the accuracy of the constructed models, some statistical parameters were also used. The results show that in the study area, the artificial neural network model presents the most accurate and appropriate model for predicting blast-induced ground vibrations. The neural network proposed in this research is suggested for areas with geological features resembling those of the present study.

中文翻译：

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使用经验模型和人工神经网络预测爆炸引起的地面振动（以Bakhtiari大坝出入隧道为例）

爆破作业是土木工程和采矿作业中最常见的岩石开挖方法之一。地面振动是爆破操作的最不利影响，因此，无法精确控制此问题会导致相邻结构损坏。在这方面，岩土工程师面临着准确预测爆炸引起的地面振动的挑战。需要Bakhtiari大坝（位于伊朗西南部）的地理位置，以通过崎top的地形建造通往其最近城市的道路。为了在计划中建立通道，已经使用了爆破操作方法。在这项研究中，使用经验模型的五种常见函数形式及其在该区域的校正回归系数，对研究区域中爆炸引起的地面振动进行了评估。然后，通过设计人工神经网络模型来预测研究区域产生的地面振动。为此，针对80次爆炸事件，调查了每个延迟的最大电荷，爆炸点与监测站之间的距离以及地面振动值，并确定了它们的必要参数。总共使用64个数据集来获得经验模型的系数并创建人工神经网络模型。此外，使用16个数据集来估计每个模型的性能和准确性。为了衡量所构建模型的准确性，还使用了一些统计参数。结果表明，在研究区域，人工神经网络模型为预测爆炸引起的地面振动提供了最准确，最合适的模型。