Predicting Burden
Rock Velocity in Limestone Mines using Artificial Neural Network Models
Channabassamma N., Avchar Akhil, Sastry Vedala Rama, Swamy V. Sahas and Kolkar Ranjit
Disaster Advances; Vol. 18(5); 133-138;
doi: https://doi.org/10.25303/185da1330138; (2025)
Abstract
The prediction of burden rock velocity is crucial in optimizing the efficiency of
mining and excavation operations. This study presents a novel approach utilizing
Artificial Neural Networks (ANNs) to accurately predict the velocity of burden rocks
based on various input parameters such as rock property, geological property and
bench properties. A comprehensive dataset was collected from field measurements
and laboratory experiments to train the ANN models. The performance of the ANN models
such as Multi-layered Perceptron (MLP), Deep Neural Network (DNN), simple MLP and
Backpropagation Neural Network (BPNN) was evaluated based on performance metrics
R-squared (R)2, Mean Squared Error (MSE) and Mean Absolute Error (MAE). Among the
developed ANN models, the BPNN model was found to be the most accurate predictive
model for burden rock velocity, as evidenced by metrics R2(0.821), MSE (0.099) and
MAE (0.226).
The results indicate that the BPNN model effectively captures the complex relationships
between the predictors and burden rock velocity. Advanced neural network algorithms
such as recurrent neural networks and long short-term memory techniques can be used
to improve the accuracy of presented neural network models.
