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Review of Machine Learning Application in Mine Blasting by Ahmed Abd Elwahab; Erkan Topal; Hyong Doo Jang is a Earth and Planetary Sciences article available to read on EtoBox.

What is Review of Machine Learning Application in Mine Blasting about?

Abstract Mine blasting has adopted machine learning (ML) into its practices with the aims of performance optimization, better decision-making process, and work safety. This study is aimed at reviewing the status of ML method applications to mine blasting issues. One of the most important observations of this research highlights the developed ML methods such as hybrids/ensembles, outperforming the other methods at 61% of the sample of case studies. The first section provides a background on the application of ML methods in mining. Two sections of the review provide the trends in the application of ML methods and the utilization of input parameters in surface and underground blasting problems. The appraisal reveals an increase of hybrid/ensemble or highly developed ML methods for the top four blast issues on the surface (72%) and underground (45%). The sample of studies reviewed indicated through graphical/statistical means a continuing increase in hybrids/ensembles’ use mirrored by high research output for the top four surface blast issues. This is contrasted by a low rate of research in underground blasting, under the encountered operational conditions applied. Regarding the input

Who reads Review of Machine Learning Application in Mine Blasting?

Author

Ahmed Abd Elwahab; Erkan Topal; Hyong Doo Jang

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Earth and Planetary Sciences (Physical Sciences)