Research On The Modeling Of Subway Line Adjustment And Slope Adjustment Based On Evolutionary Algorithm

We live in a society where knowledge is updating rapidly,and data is being complexed.Useful information can be extracted from complex data such as consumption habits and account passwords.The optimal solution is selected from the massive decision-making schemes to improve production efficiency and reduce costs in all aspects.People usually use some efficient data mining tools and optimization methods,and then use computers instead of the human brain to do many very complex calculations.How to induce useful information from massive data is high-tech and intensive work.To reduce people's workload and save social costs,people have designed some convenient methods and machine hardware that can replace human brain work.These methods are called intelligent algorithms.With the advent of the information age,the powerful computing power and various intelligent algorithms of the computer gradually get the attention of the industry,and the industry gradually uses the computer and intelligent algorithm to optimize complex problems in many fields.Based on the point cloud data of the subway tunnel,the evolutionary mechanism is applied to solve the problem of the fine-tuning of line and slope.This paper mainly includes the following three parts:In the first part,a denoising method of the point cloud in subway circular tunnel is proposed.When using laser scanning to get the subway tunnel point cloud,due to the influence of various factors,there are some noise points in the final tunnel point cloud data,which leads to the designers unable to correctly judge the overall contour of the tunnel,affecting their adjustment of the track laying line.In this part,we propose a method to filter the noise of point cloud in the circular tunnel.The experimental results show that the proposed point cloud data filtering method can effectively filter the noise in the point cloud data.In the second part,a mathematical model of the actual tunnel is constructed based on the point cloud data.In engineering projects,the overall contour of the tunnel is represented by the 3D point cloud data obtained by laser scanning.However,in the actual project,the scale of point cloud data is very large,so it takes a lot of time to scan and calculate the point cloud data to extract a certain section or cross-section of the tunnel.To speed up the extraction of point cloud data and simplify the representation of the actual tunnel.In this part,based on the point cloud data of the actual tunnel,a mathematical model analytic expression is built,which is used to represent the actual tunnel contour instead of the point cloud data.The experimental results show that using our analytical formula can greatly reduce the extraction time of tunnel contour,and greatly reduce the time complexity of the third part.In the third part,an evolutionary-based line and slope adjustment method is proposed.To achieve this,an evolutionary algorithm is used to optimize the track route to be laid and find an optimal design tunnel with the minimum deviation from the actual tunnel.The experimental results show that the proposed line and slope adjustment scheme can perform better results as compared with the scheme designed by the experts.