Integrating ANN Prediction with Honeybee Optimisation for Flyrock Minimisation in Open-Pit Mining
DOI:
https://doi.org/10.17794/rgn.2025.5.11Keywords:
Flyrock prediction, Optimisation algorithm, Blasting pattern, Honeybee algorithm, Metaheuristic algorithmsAbstract
Flyrock is an undesirable phenomenon resulting from blasting in open-pit mines, posing significant risks to both environmental and human safety. Given these risks, a comprehensive study of flyrock is essential to mitigate its adverse effects. This study presents a novel hybrid intelligent model designed to predict and minimize flyrock distance by integrating an Artificial Neural Network (ANN) with a Honeybee Optimization Algorithm. Utilizing a dataset of 334 blast records collected from the Sungun copper mine, various ANN models were developed and evaluated. After assessing multiple models through a formal scoring system, the most effective one was selected for optimization. The chosen ANN model demonstrated strong predictive performance, achieving coefficients of determination (R²) of 0.8930 and 0.8874, as well as root mean square error (RMSE) values of 0.2486 and 0.2512 for the training and testing phases, respectively, outperforming conventional empirical models. To further refine the blast pattern for safety, the Honeybee Optimization Algorithm was employed to minimize the predicted flyrock distance. The optimal flyrock distance was determined to be 7.25 meters, reflecting a 27.5% reduction compared to the lowest observed value in the collected data. This demonstrates the superiority of the proposed hybrid approach in enhancing blasting safety and efficiency.
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Copyright (c) 2025 Mojtaba Rezakhah, Erfan Nemati, Amir Batarbiat, Manoj Khandelwal
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