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Artificial Intelligent Approaches for Prediction of Longitudinal Wave Velocity in Rocks
Abstract
Intelligent techniques are quickly gaining importance in the field of geophysics, mining and geology. In this chapter the significance of intelligent techniques like ANN and ANFIS for prediction of longitudinal wave velocity and its advantages over other conventional methods of computing have been discussed. Longitudinal wave measurement is an indicator of peak particle velocity during blasting in a mine and it is a significant factor to be predicted to minimize the damage caused by ground vibrations. Wave velocity measurements have wide applications in the different fields of geophysics, mining and geology. In this chapter, ANN and ANFIS models are designed to predict the longitudinal wave velocity of different rocks and correlation have been developed with fracture properties. The fracture roughness coefficient and physico-mechanical properties are taken as input parameters and longitudinal wave velocity as output parameters. The mean absolute percentage error for the Longitudinal wave velocity predicted by Adaptive Neuro Fuzzy Inference System has been found to be the least.
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