## 5 Conclusions  
This paper proposed an optimized BPNN model using genetic algorithm. Four other machine learning methods were used for comparison. In view of the fact that there is little research on the prediction of the tunneling parameters of the shield tunneling in the mixed ground, this paper proposed five strategies to deal with the multi-geological parameters of the mixed ground, selecting different forms of geological parameters for prediction separately. Two indicators _MAE_ and _RMSE_ were used to evaluate the performance of different models. In the strategy selection, the statistical \(R^{2}\) was used to evaluate the pros and cons of each strategy. Finally, the field data of a section of Nanjing Rail Transit S6 line were used for verification. The main conclusions from this study are as follows:  
1. It was feasible to combine genetic algorithm with back-propagation neural network. By optimizing the combination of hyperparameters, it can quickly find the best network structure and help the neural network achieve the best prediction effect.
2. Compared with the other four models, the BPNN model provided the best performances. With the help of genetic algorithm, the optimal combination of model hyperparameters can be found in a short time.
3. Shield tunneling parameter data of 1188 rings, combined with the geological parameter data obtained from the geological exploration, were used to train and test the models. Four error indicators were employed and the results showed that the model  
Figure 11: Predicted and actual AR values by the BPNN model for the test datasets.  
Figure 12: Performance of BPNN on all data.  
obtained by MLR had the worst fitting effect, while BPNN had a strong learning ability. The performance of the BPNN model was significantly better than the other four models, so the BPNN model was determined as the optimal advance rate prediction model in this study.
4. Five strategies were proposed to solve the mixed ground engineering puzzle. The only difference between them was the treatment of the geological parameters of the mixed ground. After comparison, the best prediction was achieved by Strategy B, which took the geological parameters of the upper and lower layer as input, and introduced the parameter of the composite ratio at the same time. This provided a reference for the selection of input parameters for our subsequent model establishment of shield tunneling parameter prediction in mixed ground.
5. A sample of 238 rings of data was randomly selected to test the performance of the model. The results showed that the BPNN obtained in scheme B can effectively predict the AR, and the \(R^{2}\) reached 0.920, which can be used for prediction and control of construction tunneling parameters.  
The models and conclusions in this study present some references for the shied tunneling parameters prediction in the mixed ground. However, there are still some key steps that are worth continuing to study, and the accuracy of the prediction of the AR needs to be further improved. Furthermore, since the relationship between advance rate and the geological parameters is a more complex nonlinear relationship, other models--such as the LSTM model--can also be used for further analysis and comparison. Therefore, further study needs to be conducted to find a more suitable solution.  
Conceptualization, X.F. and Q.G.; Methodology, X.F. and Y.W.; Software, X.F.; Resources, Q.G.; Data curation, Y.W. and H.L.; Writing--original draft preparation, X.F.; Writing--review and editing, Y.W. and Y.Z.; Funding acquisition, Q.G. All authors have read and agreed to the published version of the manuscript.  
The authors are grateful for the support from the National Natural Science Foundation of China (51978523).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.