Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

Rock fragmentation in mining and construction industries is widely achieved using drilling blasting technique. The technique remains the most effective efficient means of breaking down rock mass into smaller pieces. However, apart from its intended purpose breakage, throw, heave, operations generate adverse impacts, such as ground vibration, airblast, flyrock, fumes, noise, that have significant operational environmental implications on activities. Consequently, blast impact studies are conducted to determine an optimum design can maximize desirable impacts minimize undesirable ones. To achieve this objective, several estimation empirical models been developed. despite being industry benchmark, model results based a limited number factors affecting outcomes blast. As result, modern-day researchers employing machine learning (ML) techniques for prediction. ML approach incorporate blast, therefore, it preferred over other statistical methods. This paper reviews various their prediction with focus details methods impacts—including applications, advantages, limitations—are discussed. literature reveals better predictors compared models. we observed presently these mainly applied academic research.