# Optimization of BP Neural Network Model for Rockburst Prediction under Multiple Influence Factors
## 6 Engineering Application  
To verify the engineering application effect of the best optimal model, the LM-BP neural network model based on the unified extreme value processing method to process data was applied to five engineering cases, namely, the Maluping mine (case 1) [40], the \(-730\) level of Dongguanshan copper mine (case 2) [41], the \(3+390\) working face of Jinping II hydropower station (case 3) [41], the Qinling tunnel (case 4) [41] and the highway tunnel of Zhongnan mountain (case 5) [42]. Rockburst occurred in these projects, and the values of \(\sigma_{\theta}/\sigma_{\mathrm{c}}\), \(\sigma_{\mathrm{c}}/\sigma_{\mathrm{t}}\), and \(W_{\mathrm{et}}\) are all worth recording. The prediction results are compared with those of the support vector machine (SVM) model and decision tree based on the same training samples. (1) Maluping mine  
The Maluping mine area of the Kailin group is located in the northern section of the east flank of Yangshui Anticline, with a stratigraphic inclination of \(110^{\circ}\)-\(165^{\circ}\) and a typical dip angle of \(20^{\circ}\)-\(35^{\circ}\). Additionally, the structure is dominated by faults, all of which are strike faults. The LM-BP neural network model is applied to predict the rockburst in the mining area with a \(750\) m middle section and red shale lithology. The prediction results are consistent with the SVM model, as shown in Table 6. The field records show that the roadway at this place has produced a sidewall and weak rockburst. The prediction results in this paper are consistent with the field situation [40]. (2) Dongguanshan copper mine  
Dongguanshan copper mine is the first typical deep-buried hard rock metal mine with rockburst proneness in China. Its main ore body is located at \(-680\) m-\(1000\) m, most of which are located below \(-730\) m above sea level. The maximum principal stress of the ore body is \(30\)-\(38\) MPa, which belongs to a high-stress region, and rockburst occur many times during the mining process. The LM-BP neural network model is used to predict the rockburst of the Dongguanshan copper mine. The prediction results are consistent with the DA-DNN model (based on the dropout and the improved [PERSON]'s deep neural network) and the cloud model, as shown in Table 7. There was a rockburst in the skarn at \(-790\) m in this mine. After supporting by an anchor net, the anchor rod was cut off, and a \(1.8\) m long floor have appeared at the boundary of the strata [41]. It can be seen that the prediction results in this paper are consistent with the field conditions and are better than the SVM model (misjudged case 2 from level III to level II). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**No.**} & \multicolumn{3}{c}{**Prediction Indexes**} & \multicolumn{5}{c}{**Prediction Results**} \\ \cline{2-7}  & \(\sigma_{\theta}/\sigma_{\mathrm{c}}\) & \(\sigma_{c}/\sigma_{\mathrm{t}}\) & \(W_{\mathrm{et}}\) & **LM-BP Model** & **Decision Tree** & **SVM Model** \\ \hline
1 & 0.45 & 11.20 & 2.03 & II & II & II & II \\ \hline \end{tabular}
\end{table} 
Table 6: Rockburst data and prediction results of Maluping mine. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**No.**} & \multicolumn{3}{c}{**Prediction Indexes**} & \multicolumn{5}{c}{**Prediction Results**} \\ \cline{2-10}  & \(\sigma_{\theta}/\sigma_{\mathrm{c}}\) & \(\sigma_{c}/\sigma_{\mathrm{t}}\) & \(W_{\mathrm{et}}\) & **LM-BP Model** & **Decision Tree** & **SVM Model** & **DA-DNN** & **Cloud Model** \\ \hline
2 & 0.46 & 11.10 & 3.97 & III & III & II & II & III & III \\ \hline \end{tabular}
\end{table} 
Table 7: Rockburst data and prediction results of Dongguanshan mine. (3) \(3+390\) working face of Jinping II hydropower station  
Jinping II hydropower station is located in a high-stress area of southwest China.