# Genetic Algorithm-Based Intelligent Selection Method of Universal Shield Segment Assembly Points
## 6 Conclusions  
This paper presents a method to select universal shield segment assembly points. Through the study of example data, this method can automatically select the best assembly point of the next segment ring based on WCPs and previous assembly points. This method  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ring**} & \multicolumn{2}{c}{**Drivers Checked Points**} & \multicolumn{2}{c}{**Actual Points**} & \multicolumn{2}{c}{**81-GA Selected Points**} \\ \cline{2-7}  & **Optional** & **Optional** & **Selected** & **Is an Optional** & **Selected** & **Is an Optional** \\  & **Point 1** & **Point 2** & **Points** & **Point** & **Points** & **Point** \\ \hline
1 & 5 & & 1 & & 5 & \(\surd\) \\
2 & 9 & & 9 & \(\surd\) & 9 & \(\surd\) \\
3 & 7 & 11 & 11 & \(\surd\) & 11 & \(\surd\) \\
4 & 6 & & 3 & & 6 & \(\surd\)  
eliminates the need for manual selection of assembly points and provides a higher accuracy rate. The following conclusions can be drawn in this study.
* To establish the intelligent selection method of assembly points, the factors influencing the selection of the assembly points were analyzed, and an objective function for the selection of the universal shield segment assembly point was designed. The working conditions were divided into 81 intervals, and the genetic algorithm was used to optimize the weights of the objective function.
* Because it is difficult to obtain sufficient example data to optimize the weights, this paper also proposed a calculation method of WCPs based on homogeneous coordinate transformation and spatial analytic geometry methods. The Monte Carlo method is used to generate any required amount of data.
* The genetic algorithm obtained 92.6% accuracy on the training set when optimizing the weights. The genetic algorithm assigned larger weights to worse WCPs, reflecting the tendency to adjust the worse parameters as much as possible, and overall assigned larger weights to the shield tail gap, reflecting the tendency to preferentially adjust the shield tail gap. Moreover, the preference is more obvious when the shield tail gap is poor.
* The results on the test data show that the proposed method of assembly point selection can reach a 90.1% correct rate. In addition, the intelligent selection method of assembly point was applied on Line 8 of the Zhengzhou rail transit in China, and the correct rate of assembly points selection reached 90.6%, which is 12.5% higher than the correct rate of actual assembly point selection in the site, indicating that the proposed method has higher accuracy than the actual assembly point selection on site.  
Furthermore, as future work, the empirically selected interval boundary values can also be optimized, and the case of more subdivisions of the working condition will be explored.  
Conceptualization, R.L. and G.Z.; methodology, R.L. and J.H.; software, R.L.; validation, R.L. and J.H.; investigation, R.L. and D.Z.; data curation, D.P.; writing--original draft preparation, R.L. and J.H.; writing--review and editing, R.L. and D.Z.; supervision, G.Z.; funding acquisition, G.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Plan of China, grant number 2020 YFB173900.  
Not applicable.  
The authors declare no conflict of interest.