# Open-Pit Bench Blasting Fragmentation Prediction Based on Stacking Integrated Strategy
## 4 Results and Discussion
### Engineering Validation Results of the Model  
In this study, field data from five blasting operations in a northeastern mining area were collected to validate the performance of the constructed model. The rocks in this mining area are mainly granite, with moderate hardness, making it a relatively explosive region. Based on the model's required input parameters, the specific values of the collected relevant parameters are shown in Table 4.  
The fragmentation size \(X_{50}\) of the blasted rock was obtained using the fragmentation recognition software Split Desktop v4.0.0.42 [56]. Figure 11 illustrates the software's recognition of the blasting results on-site. The software effectively maps the boundaries of rock fragmentation blocks and calculates the distribution of block sizes for this blasting, using a 1-meter-long ruler placed on-site as a reference. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **B** & **S** & **H** & **D** & **T** & **Pf** & **XB** & **E** & **X50** \\  & **(m)** & **(m)** & **(m)** & **(m)** & **(kg/m3)** & **(m)** & **(GPa)** & **(m)** \\ \hline
1 & 7 & 8.5 & 15 & 250 & 7.5 & 0.62 & 1.11 & 5.6 & 0.1456 \\
2 & 7 & 9 & 15 & 250 & 7.5 & 0.64 & 1.11 & 5.6 & 0.163 \\
3 & 7 & 10 & 15 & 250 & 7.5 & 0.61 & 1.11 & 5.6 & 0.1962 \\
4 & 7 & 9.5 & 15 & 250 & 7.5 & 0.57 & 1.11 & 5.6 & 0.1997 \\
5 & 7 & 9.5 & 15 & 250 & 7.5 & 0.62 & 1.11 & 5.6 & 0.1786 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: blasting parameter statistics. To maintain consistency with the input parameters used during model training, the validation still uses the value of the ratio between the parameters as the model's input parameters. Table 5 shows the input parameters and their values for the validation process. Figure 12 shows the comparison between the model-predicted results and the actual results of rock fragmentation. To make it easier to compare the difference between the true and predicted values, a purple color legend representing the true values has been added to the figure. The model's predicted values closely match the actual values, with prediction errors within 0.03 m. This indicates that the prediction model developed in this study can accurately predict rock fragmentation in open blast projects, which is of practical significance. However, the cross-validation method commonly adopted by most scholars was not employed in constructing the stacking model. This may be due to the small dataset size in this study, which resulted in lower prediction accuracy when using cross-validation methods. Additionally, during the engineering verification process, Split-Desktop software v4.0.0.42 inevitably has some errors in recognizing the block size. Therefore, in future research, it is advisable to consider more diverse datasets and increase the number of base learners. In practical engineering applications, blasting outcomes can be predicted in advance based on various design parameters. This enables iterative adjustments to parameter values, ensuring that the desired fragmentation index is achieved for the project. Based on the results of the engineering verification mentioned above, the model can be used to predict the fragmentation size of the blast in advance, according to the blasting parameters, before production blasting. The parameters of on-site blasting operations can be further optimized based on the predicted results to meet the required fragmentation size for production. Similarly, under the guidance of a large amount of actual on-site parameters, the predictive accuracy of the stacking model established in this study can be further improved, providing the potential to develop an intelligent prediction system for on-site blasting. Figure 11: Block size recognition results from Split-Desktop software.