Abstract

DAS, A.; SINHA, S.  and  GANGULY, S.. Development of a blast-induced vibration prediction model using an artificial neural network. J. S. Afr. Inst. Min. Metall. [online]. 2019, vol.119, n.2, pp.187-200. ISSN 2411-9717.  http://dx.doi.org/10.17159/2411-9717/2019/v119n2a11.

In an opencast mine explosives are used for fragmentation of rock. Inefficient use of explosive energy in an opencast operation produces excessive ground vibration, which is measured by peak particle velocity (PPV). To mitigate ground vibration, it is essential to develop a model to predict PPV. At present empirical models are used. These models are based on only a few input variables, hence they fail to take into account the effects of the myriad factors that cause ground vibration. Due to lack of explicit knowledge about the complex mine blasting system the scope of application of mathematical and statistical modeling techniques is limited. The artificial neural network (ANN) technique is a learning algorithm that can remove some of these limitations and can be applied to predict PPV. In this paper an ANN model is developed for prediction of blast vibration using 248 data records collected from three coal mines with diverse geomining conditions. The correlation coefficient between measured PPV and model output was found to be 0.96 and the average error percentage 11.85. The ANN model output was compared with the output of three empirical models that are widely used for prediction of PPV. The correlation coefficient between the PPV predicted by an empirical model and measured PPV data was 0.63 and the relative error percentage 38.47. This result demonstrates the superiority of the ANN model compared to empirical blast models. By using site-specific structural discontinuities as input the model performance can be further improved. Sensitivity analysis and 3D plotting were used to gain further knowledge about blast-induced ground vibration.

Keywords : artificial neural network; peak particle velocity; sensitivity analysis; 3D plot.

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