Machine learning based prediction of flyrock distance in rock blasting: A safe and sustainable mining approach

Flyrock is a significant environmental and safety concern in mining and construction. It arises from various geological and blast design factors, posing risks to workers, machinery, and nearby structures. This study examined how these factors affect the rate and distance of flyrock projections caused by blasts. To address this issue, advanced machine learning (ML) models were used to predict flyrock distances in the Akoko Edo dolomite quarries. The models examined included bidirectional recurrent neural networks (BRNNs), support vector regression (SVR) with different kernels (SVR-S, SVR-RBF, SVR-L, SVR-P), long short-term memory (LSTM) networks, and random forest (RF) algorithms. A case study was conducted using 258 blasting data samples to develop these models. Key factors influencing flyrock were identified: blast hole burden distance, maximum instantaneous charge, and rock brittleness index. Using these factors, a flyrock possibility assessment chart was created to enhance the safety of small-scale mining operations. The model’s prediction accuracy was evaluated using correlation coefficients and four performance metrics. The LSTM model stood out, achieving the highest coefficient of correlation ( R 2 = 0.99) for both training and testing datasets. This indicates that the LSTM model accurately predicts blast-induced flyrock distance. The study also revealed that the Gaussian-RBF kernel SVR has high prediction accuracy when compared to other SVR variants (SVR-S, SVR-L, and SVR-P). In conclusion, the study compared various ML models for flyrock reduction and found that the LSTM model was the most effective in estimating blast-induced flyrock distances.