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Artificial neural network with a cross-validation approach to blast-induced ground vibration propagation modeling
Given their technical and economic advantages, the application of explosive substances to rock mass excavation is widely used. However, because of serious environmental restraints, there has been an increasing need to use complex tools to control environmental effects due to blast-induced ground vibrations. In the present study, an artificial neural network (ANN) with k-fold cross-validation was applied to a dataset containing 1114 observations that was obtained from published results; furthermore, quantitative and qualitative parameters were considered for ground vibration amplitude prediction. The best ANN model obtained has a maximum coefficient of determination of 0.840 and a mean absolute error of 5.59 and it comprises 17 input parameters, 12 neurons in a one-layer hidden layer, and a sigmoid transfer function. Compared with the traditional models, the model obtained using the proposed methodology demonstrated better generalization ability. Furthermore, the proposed methodology offers an ANN model with higher prediction ability.
Artificial neural network with a cross-validation approach to blast-induced ground vibration propagation modeling
Given their technical and economic advantages, the application of explosive substances to rock mass excavation is widely used. However, because of serious environmental restraints, there has been an increasing need to use complex tools to control environmental effects due to blast-induced ground vibrations. In the present study, an artificial neural network (ANN) with k-fold cross-validation was applied to a dataset containing 1114 observations that was obtained from published results; furthermore, quantitative and qualitative parameters were considered for ground vibration amplitude prediction. The best ANN model obtained has a maximum coefficient of determination of 0.840 and a mean absolute error of 5.59 and it comprises 17 input parameters, 12 neurons in a one-layer hidden layer, and a sigmoid transfer function. Compared with the traditional models, the model obtained using the proposed methodology demonstrated better generalization ability. Furthermore, the proposed methodology offers an ANN model with higher prediction ability.
Artificial neural network with a cross-validation approach to blast-induced ground vibration propagation modeling
Gustavo Paneiro (author) / Manuel Rafael (author)
2021
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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