# Instability Risk Assessment for Deep Excavation of Soil-Rock Combinations Containing Groundwater
## 3 Case Study
### Exavation Risk Assessment
#### 3.2.2 Risk Assessment Process Based on Improved Neural Networks
Twelve sets of training data were input into the neural network, with 1, 2, 3, and 4 used to indicate low risk, medium risk, high risk, and extra high risk, respectively, and the calculation results are shown in Table 8. In Table 9, compared with the actual risk level, the risk level calculated using the model corresponds to it, which indicates that the improved neural network had a high accuracy. After the neural network was trained, it was used for excavation risk assessment. The monitoring data of Anshan Road pit unit K on 10 January 2019 and 30 May 2019, which had a large cumulative variation, were selected to be used as samples for predicting the safety status of the excavation, as shown in Table 10. Meanwhile, to demonstrate the accuracy of the assessment results, the traditional RBF neural network model, BP neural network model, and fuzzy comprehensive assessment model were used for prediction based on the same data, and the results of the model comparison are shown in Table 11. It can be seen from Table 11 that the prediction results of the improved neural network model corresponded to the actual engineering grades, whose risk for deep excavation on 10 January 2019 and 30 May 2019 were grade II and grade III, respectively. In Table 11, the prediction results of the BP neural network are all class II, which is more conservative. This is because 12 sets of data as training samples are not enough for a BP neural network. This means that the improved RBF network could obtain good results with less information. On the other hand, the traditional RBF networks seemed to have better prediction results, with their risk levels all being 2. However, by comparing the mean square error of the models, the improved model had a smaller value. Incidentally, the BP model had the largest mean square error, with a value of 0.779. This indicates that the improved model had a greater ability to analyze nonlinear factors, and it had a stronger ability to resist interference when abnormal data were present. Overall, the improved model had a higher accuracy and stronger immunity to interference. Dynamic assessment of excavation risks and taking appropriate measures are significant for ensuring excavation safety. At this point, according to the management measures under the different risk levels listed in Table 6, the monitoring frequency of the pit itself as well as the surrounding environment should be increased during the excavation process. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multicolumn{2}{c}{**Horizontal**} & \multirow{2}{*}{ \begin{tabular}{c} **Pile** \\ **Displacement** \\ **of File Top** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Deep** \\ **Settlement** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Anchor Cable** \\ **Horizontal** \\ **Displacement** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **Surface** \\ **Stress** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Building** \\ **Settlement** \\ \end{tabular}  } & \multirow{2}{*}{**Water Table**} \\ \cline{1-1} \cline{6-10} \cline{7-10} \cline{7-  
An important fact to recognize is that the model presented in this paper has many shortcomings requiring improvement. The first point is that the library of potential risk factors needs to be supplemented with more risk factors. In actual engineering, the factors affecting excavation safety are numerous and complex. For example, human factors have a very significant impact on excavation safety but were not included in the factor library developed in this paper. Second, although we tried as far as possible to avoid the role of subjective factors in the factor selection process, their influence was still not completely negligible. The issue of how to solve this problem is an important direction that needs to be focused on in the future. Third, more training samples need to be collected. When the risk factor index increases, the LLE algorithm will no longer be applicable if the number of datasets is insufficient.