Optimal ELM-Harris hawks optimization and ELM-grasshopper optimization models to forecast peak particle velocity resulting from mine blasting

Abstract

Most mining and tunneling projects usually require blasting operations to remove rock mass. Previous studies have mentioned that if the blasting operation is not properly designed, it may lead to several environmental issues, such as ground vibration. This study presents various machine learning (ML) techniques, i.e., hybrid extreme learning machines (ELMs) with the grasshopper optimization algorithm (GOA) and Harris hawks optimization (HHO) for controlling and predicting ground vibrations resulting from mine blasting. Actually, the GOA-ELM and HHO-ELM models are improved versions of a previously developed ELM model, and they are able to provide higher performance capacity. For the proposed ML modeling, a database was established consisting of 166 datasets collected from Malaysian quarries. The efficacy of the proposed ML techniques was observed in the training stage as well as in the testing stage, and the results were evaluated against five parameters constituting the fitness criteria. The results showed that the GOA-ELM model delivered more accurate ground vibration values compared to the HHO-ELM model. The system error values of the GOA-ELM model for the training and testing datasets were 2.0239 and 2.8551, respectively. The coefficients of determination of the GOA-ELM model for the training and testing datasets were 0.9410 and 0.9105, respectively. It was concluded that the new hybrid model is able to forecast ground vibration resulting from mine blasting with high level of accuracy. The capabilities of this hybrid model can be extended further to mitigate other environmental issues caused by mine blasting.