# Genetic Algorithm-Based Intelligent Selection Method of Universal Shield Segment Assembly Points
## 1 Introduction
[24] analyzed the influencing factors of segment assembly point selection and designed an objective function; one step further is that the weights of the objective function can be adaptively changed, but the method of taking the weights has no theoretical support. It can be noted that establishing the objective function of the selection of a segment assembly point has become the major research method for selecting the assembly point. There are two key steps in the establishment of a reasonable objective function. One is the selection of the influencing factors, and the other is taking the weights of the influencing factors. It is clear that the most important influencing factors are thrust cylinder stroke difference and shield tail gap. The proportion of the curve-fitting deviation in the objective function is small. For example, in the paper [24], the authors take an initial weight of 5%. In addition, the curve-fitting deviation cannot be accurately calculated due to the drift phenomenon of the newly assembled segment. Meanwhile, engineering has agreed that considering the thrust cylinder stroke difference and the shield tail gap are sufficient parameters to select the assembly point. On the other hand, the weights of the influencing factors have a crucial influence on the rationality of the selection of assembly points. However, in the current study, the weight of each parameter is determined by the drivers through experience. This weighting method is arbitrary and subjective, making it impossible to guarantee the reasonableness of weights and resulting in the research results not reaching the ideal application in engineering. At the same time, the weights reflect the importance degree of the parameters at that moment, and the importance degree of each parameter changes constantly under different working conditions. Therefore, the weights should also be adjusted with the working conditions all the time. However, how to adjust the weights scientifically and reasonably is also a gap in the current research. The purpose of this paper is to establish an objective function of the selection of the segment assembly point with thrust cylinder stroke difference and shield tail gap and use a scientific method to objectively and adaptively take the weights. The thrust cylinder stroke difference and shield tail gap represent the working condition, so they are collectively referred to as working condition parameters (WCPs) in this paper. Essentially, the problem of segment assembly point selection is a multi-objective optimization problem. The solution of multi-objective optimization problems is mostly calculated by intelligent optimization algorithms, such as ant colony algorithm [25; 26], particle swarm optimization [27; 28], a genetic algorithm [29; 30; 31; 32], etc. Among them, the genetic algorithm has been widely used in solving multi-objective optimization problems because of its good robustness and strong global search ability, and we will use genetic algorithms to optimize the weights in this paper. To achieve adaptive taking of weights with WCPs, we divide the working conditions into 81 intervals. The working conditions are considered to be consistent within each interval and share the same set of weights, and the genetic algorithm optimizes the weights within each interval. We use the correct rate of assembly point selection for example data as an evaluation index of the chromosome goodness of the genetic algorithm. However, actual data from the project are not only slow to obtain, but also difficult to reasonably reflect all working conditions. So, we also propose a method to calculate WCPs and combine them with a manual labeling method to obtain a sufficient number of example data. Figure 1 shows the pipeline of the work in this paper. The remainder of this paper is organized as follows. Section 2 establishes the assembly point selection objective function. Section 3 presents the calculation method of the WCPs and the establishment of an example dataset.