Development of a precise model for prediction of blast-induced flyrock using regression tree technique

Drilling and blasting is the predominant rock fragmentation method in open-cast mines and civil construction works. Flyrock is one of the most hazardous effects caused by blasting operation. Therefore, the ability to make accurate predictions of the blast-induced flyrock is essential to reduce the environmental problems. This paper aimed to develop a precise and applicable model based on regression tree (RT) to predict blast-produced flyrock distance in Ulu Tiram quarry, Malaysia. In this regard, 65 blasting operations were investigated and the most influential factors on the flyrock, i.e. blast-hole length, spacing, burden, stemming, maximum charge used per delay and powder factor, were measured. Also, the flyrock distance values for the considered blasting events were carefully measured. In order to check the precision of the proposed RT model, multiple linear regression (MLR) model was also developed and both of the predictive models were compared. For this work, some statistical functions, i.e. median absolute error, coefficient of determination (R2) and root mean squared error, were used and computed. The results revealed that the RT can be introduced as a powerful technique to predict flyrock distance and the proposed RT model can estimate flyrock distance better than MLR model. Also, sensitivity analysis was performed and it was found that the powder factor is the most influential parameter on the flyrock in the studied case.