This paper presents a forecasting model for powder factors in tunnel blasting using artificial neural networks (ANN). Case data of a railway tunnel recently under construction in Taiwan were used to establish the model. The main rock type in the tested case was metamorphic rock. In this study, the rock mechanical factors influencing the powder factors were empirically identified first. Rock mechanical parameters having a significant influcncc were then filtered to train and test the ANN. The ANN model for predicting powder factors had a testing root mean square (RMS) of 0.02983 on average. Rock quality designation (RQD) was the most important parameter of all the selected rock mechanical characteristics. Validation was also performed to show that the neural networks outperformed the multiple nonlinear regression method in analyzing relationships between powder factors and rock characteristics.
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