## 6 Conclusions  
Flyrock generation remains a persisting safety concern in surface-mine blasting. It poses significant operational risks to people, machinery, materials, and structures nearby. Prevention of the occurrence of flyrock needs careful planning of the blast design parameters for efficient utilization of the explosive energy. In this research paper, a modified approach of delimiting the danger zone for bench blasting has been proposed. The method integrates a high-speed motion analysis and machine learning regression to help optimize the blast design variables, thereby reducing the chances of flyrock generation and risks associated with it. The study investigated the factors influencing flyrock launch velocity (\(v_{f}\)) using high-speed motion analysis of 79 full-scale production blasts conducted with a total of 4110 blastholes at five distinct surface mines in India. Out of these, 231 blastholes generated flyrock. The data acquired through high-speed motion analysis were utilized to develop ten Bayesian optimized machine learning regression models. All the models were optimized using five-fold cross validation and their accuracy was rigorously examined. The conclusions drawn from the experiment are as follows:  
1. Out of the ten Bayesian optimized machine learning regression models, the optimized Extremely Randomized Trees Regression model (ERTR-BO) demonstrated the best predictive accuracy, exhibiting higher R\({}^{2}\), IA, and KGE scores and the lowest RMSE, MAE, and MAPE;
2. An in-depth analysis of the achieved accuracies of the white box models unveiled their inability to effectively capture the intricate and complex non-linear dependencies  
Figure 17: Simulated trajectories of flyrock for fifteen different combinations of fragment mass and launch height for a blast from Mine D.  
existing between the input features and the target variable. Furthermore, these models displayed evidence of overfitting, as indicated by a substantial disparity between the training and test R\({}^{2}\) scores;
3. Each of the ensemble black box models exhibited notably superior predictive capability as compared to the black box single model SVR-BO;
4. Through the application of Bayesian optimization, the R\({}^{2}\) score of SVR demonstrated notable enhancement with an increase of 15.04% for the training set and 14.12% for the test set, followed by improvements in the ENR (13.09%-training, 12.20%-testing), LR (3.85%-training, 6.26%-testing), KNN (2.08%-training, 2.76%-testing), and ABR (1.36% training, 1.38%-testing). Certain other black box models performed well with their respective default hyperparameters. Notably, there were no discernable improvements in the RR scores even after hyperparameter tuning;
5. The SHAP analysis, undertaken to elucidate the marginal contribution of input features in predicting \(v_{f}\) by the ERTR-BO model, unveiled \(\rho_{r}\) as the most influential feature, followed by \(W\), \(B\), \(B\)/\(d\), \(l_{s}\)/\(B\), and \(l_{s}\)/\(l_{bh}\). The analysis further substantiated the tangible significance and intricate interactions among these features that collectively contributed to the generation of flyrock in bench-blasting operations;
6. In order to improve the practicality of the ERTR-BO model for real-world flyrock prediction, a simple GUI was designed. The interface may allow users to input predictor variables and obtain predicted flyrock velocity. This velocity may then be used for simulating flight paths and estimating the potential range of flyrock events, as demonstrated in Section 5.4. The generation of flyrock is influenced not only by blast design variables, but also by uncontrollable factors such as site-specific geo-mining conditions and the physicochemical properties of the rock mass to be blasted. As a result, the unpredictability of flyrock persists until the occurrence of the actual blast, which is a contributing factor to the limited data availability for this study.