Review of machine learning application in mine blasting

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 status ML method applications to mine issues. One most important observations this research highlights developed methods such as hybrids/ensembles, outperforming other 61% sample case studies. The first section provides a background on application in mining. Two sections review provide trends utilization input parameters surface underground problems. appraisal reveals an increase hybrid/ensemble or highly for top four blast issues (72%) (45%). studies reviewed indicated through graphical/statistical means continuing hybrids/ensembles’ use mirrored by high output contrasted low rate blasting, under encountered operational conditions applied. Regarding parameters, controllable (blast design geometry) were recognized be steadily used issues, along less involvement from uncontrollable (geological geotechnical parameters). On contrary, slight more than In final paper, offers discussion current state limits where efforts should focused concerning applied, involved, challenges faced. Such levels performances are demand complex mining environment. Persistent research, development employees’ technological skills alongside increased awareness among industry benefits techniques, greatly needed stage. would establish role improving both process overall management.