Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system |
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| Authors: | Melih Iphar Mahmut Yavuz Hakan Ak |
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| Institution: | (1) Department of Mining Engineering, Eskisehir Osmangazi University, Meselik, Eskisehir, Turkey |
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| Abstract: | The aim of this study is to predict the peak particle velocity (PPV) values from both presently constructed simple regression
model and fuzzy-based model. For this purpose, vibrations induced by bench blasting operations were measured in an open-pit
mine operated by the most important magnesite producing company (MAS) in Turkey. After gathering the ordered pairs of distance
and PPV values, the site-specific parameters were determined using traditional regression method. Also, an attempt has been
made to investigate the applicability of a relatively new soft computing method called as the adaptive neuro-fuzzy inference
system (ANFIS) to predict PPV. To achieve this objective, data obtained from the blasting measurements were evaluated by constructing
an ANFIS-based prediction model. The distance from the blasting site to the monitoring stations and the charge weight per
delay were selected as the input parameters of the constructed model, the output parameter being the PPV. Valid for the site,
the PPV prediction capability of the constructed ANFIS-based model has proved to be successful in terms of statistical performance
indices such as variance account for (VAF), root mean square error (RMSE), standard error of estimation, and correlation between
predicted and measured PPV values. Also, using these statistical performance indices, a prediction performance comparison
has been made between the presently constructed ANFIS-based model and the classical regression-based prediction method, which
has been widely used in the literature. Although the prediction performance of the regression-based model was high, the comparison
has indicated that the proposed ANFIS-based model exhibited better prediction performance than the classical regression-based
model. |
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| Keywords: | Blasting Open pit Peak particle velocity ANFIS |
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