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A platform for research: civil engineering, architecture and urbanism
Predicting blast-induced ground vibration using general regression neural network
Blasting is still an economical and viable method for rock excavation in mining and civil works projects. Ground vibration generated due to blasting is an undesirable phenomenon which is harmful for the nearby inhabitants and dwellings and should be prevented. In this study, an attempt has been made to predict the blast-induced ground vibration and frequency by incorporating rock properties, blast design and explosive parameters using the general regression neural network (GRNN) technique. To validate this methodology, the predictions obtained were compared with those obtained using the artificial neural network (ANN) model as well as by multivariate regression analysis (MVRA). Among all the methods, GRNN provides excellent predictions with a high degree of correlation.
Predicting blast-induced ground vibration using general regression neural network
Blasting is still an economical and viable method for rock excavation in mining and civil works projects. Ground vibration generated due to blasting is an undesirable phenomenon which is harmful for the nearby inhabitants and dwellings and should be prevented. In this study, an attempt has been made to predict the blast-induced ground vibration and frequency by incorporating rock properties, blast design and explosive parameters using the general regression neural network (GRNN) technique. To validate this methodology, the predictions obtained were compared with those obtained using the artificial neural network (ANN) model as well as by multivariate regression analysis (MVRA). Among all the methods, GRNN provides excellent predictions with a high degree of correlation.
Predicting blast-induced ground vibration using general regression neural network
Xue, Xinhua (author) / Yang, Xingguo (author)
Journal of Vibration and Control ; 20 ; 1512-1519
2014
8 Seiten, 31 Quellen
Article (Journal)
English
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