Intelligent systems for ground vibration measurement: a comparative study

This paper deals with the application of genetic algorithm (GA) optimization technique to predict peak particle velocity (PPV). PPV is one of the important parameters to be determined to minimize the damage caused by ground vibration. A number of previous researchers have tried to use different empirical methods to predict PPV but these empirical methods have their limitations due to their less versatile application. In this paper, GA technique is used for the prediction of PPV by incorporating blast design and explosive parameters and the suitability of one technique over other has been analyzed based on the results. Datasets have been obtained from one of the Kurasia mines. 127 data sets were used to establish GA architecture and 10 data sets have been used for validation of GA model to observe its prediction capability. The results obtained have been compared with different traditional vibration predictors, multivariate regression analysis, artificial neural network and the superiority of application of GA over previous methodology have been discussed. The mean absolute percentage error in the proposed architect is very low (0.08) as compared to other predictors.

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