Development of a predictive model of rock fragmentation for Nui Phao open-pit mine in Vietnam using multiple-output Neural Networks and Monte Carlo Dropout technique

Abstract Precise and reliable prediction of blast fragmentation is essential for the success of mining operations. Over the years, various machine learning models using artificial neural network have developed and proven to be efficient in predicting the blast fragmentation. In this research, we design multiple-output neural networks to forecast the cumulative distribution function (CDF) of blast fragmentation to improve this prediction. The model architecture contains multiple response variables in the output layer that correspond to the CDF curve’s percentiles. We apply Monte Carlo dropout procedure to estimate the uncertainty of the predictions. Data collected from a Nui Phao open-pit mine in Vietnam are used to train and validate the performance of the model. Results suggest that multiple-output neural network models provide better accuracy than single-output neural network models that predict each percentile on a CDF independently. Whereas, Monte Carlo dropout technique can give valuable and relative reliable information during decision making. Article highlights: • Precise and reliable prediction of blast fragmentation is essential for the success of mining operations. • A predictive model based on the multi-output neural network and Monte Carlo dropout technique was designed to predict the fragmentation CDF curve in the blasting operation of an open-pit mine. • The predictive model was proven reliable and provided better accuracy than models based on a single-output neural network.