Blasting operations involve some undesirable environmental issues that may cause damage to equipment and surrounding areas. One of them, and probably the most important one, is flyrock induced by blasting, where its accurate estimation before the operation is essential to identify the blasting zone’s safety zone. This study introduces several tree-based solutions for an accurate prediction of flyrock. This has been done using four techniques, i.e., decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost). The modelling of tree-based techniques was conducted with in-depth knowledge and understanding of their most influential factors. The mentioned factors were designed through the use of several parametric investigations, which can also be utilized in other engineering fields. As a result, all four tree-based models are capable enough for blasting-induced flyrock prediction. However, the most accurate predicted flyrock values were obtained using the AdaBoost technique. Observed and forecasted flyrock by AdaBoost for the training and testing phases received coefficients of determination (R²) of 0.99 and 0.99, respectively, which confirm the power of this technique in estimating flyrock. Additionally, according to the results of the input parameters, the powder factor had the highest influence on flyrock, whereas burden and spacing had the lowest impact on flyrock.

Air overpressure (AOp) is an undesirable environmental effect of blasting. To date, a variety of empirical equations have been developed to forecast this phenomenon and prevent its negative impacts with accuracy. However, the accuracy of these methods is not sufficient. In addition, they are resource-consuming. This study employed support vector regression (SVR) optimized with the grasshopper optimizer (GO) algorithm to forecast AOp resulting from blasting. Additionally, a novel input selection technique, the Boruta algorithm (BFS), was applied. A new algorithm, the SVR-GA-BFS₇, was developed by combining the models mentioned above. The findings showed that the SVR-GO-BFS₇ model was the best technique (R² = 0.983, RMSE = 1.332). The superiority of this model means that using the seven most important inputs was enough to forecast the AOp in the present investigation. Furthermore, the performance of SVR-GO-BFS₇ was compared with various machine learning techniques, and the model outperformed the base models. The GO was compared with some other optimization techniques, and the superiority of this algorithm over the others was confirmed. Therefore, the suggested method presents a framework for accurate AOp prediction that supports the resource-saving forecasting methods.

Blasting is one of the most important operations in the mining projects. Implementation of improper blasting pattern may result in some undesirable phenomena such as poor fragmentation, back break, flyrock etc., which in turn increase the cost of operation. A pattern can be evaluated as suitable once it satisfies the objectives (including economical, technical and safety indexes) of the operation that are defined by the designer. In this way, selection of a blasting pattern from the field experienced patterns can be considered as a Multi Attribute Decision Making (MADM) problem. In this paper, blasting patterns of the Sungun copper mine are ranked using AHP-TOPSIS to find out the most suitable pattern covering the required level of the concerned attributes. As a result, the blasting pattern with burden of 3 m, spacing of 4 m and stemming of 3.2 m was determined as the most suitable blasting pattern.