Methodology for Existing Railway Reconstruction with Constrained Optimization Based on Point Cloud Data

Abstract

The reconstruction of an existing railway is important for railway reformation or double-track design. Obtaining the curve parameters of the railway and the location of the main stake accurately and rapidly is the key issue for existing railway reconstruction. A new method based on point cloud data is proposed in this paper. The issue of reconstruction was transformed into an optimization problem by constructing the objective function and introducing the constraint. With consideration of the slope of the curves’ chord, the robust local weighted moving average method was used for de-noising. The time complexity was reduced greatly after separating the curve unit. The proposed method can obtain the coordinates of the main stake and the parameters of the railway by particle swarm optimization using a full direction search, combining the design requirements and geometric relations of the railway. Finally, some experiments on the design data and measured data were conducted to verify the validity of the proposed method. The results also show that the proposed method is very effective and useful for existing railway reconstruction.

A smoothness optimization method for horizontal alignment considering ballasted track maintenance

“…Camacho-Torregrosa et al (2015) did not set a recognition threshold but instead selected a heuristic algorithm to optimize the theoretical heading direction of the measurement data and searched for the boundary points of the alignment element. In addition to the above methods, Higuera de Frutos and Castro (2017), Higuera de Frutos et al (2015), and F. Li et al (2018) applied the slope of the centerline to identify the geometric features of the horizontal and vertical alignments and constructed a filter to eliminate the interference. Yoshimura and Naganuma (2013) and Bera et al (2010) identified the characteristics of geometric alignment elements with the help of the versine.…”

“…Based on the consistency between the measurement points and the track alignment, W. Li et al (2019) and Pu et al (2019) imposed constraints in the process of swing iterative fitting and refined the fitting results by using a genetic algorithm. According to point cloud data, F. Li et al (2018) searched in all directions with the particle swarm optimization algorithm to obtain the boundary point coordinates and parameters of alignment elements.…”

“…(2015), and F. Li et al. (2018) applied the slope of the centerline to identify the geometric features of the horizontal and vertical alignments and constructed a filter to eliminate the interference. Yoshimura and Naganuma (2013) and Bera et al.…”

“…According to point cloud data, F. Li et al. (2018) searched in all directions with the particle swarm optimization algorithm to obtain the boundary point coordinates and parameters of alignment elements.…”

Power Transformer’s Electrostatic Ring Optimization Based on ANSYS Parametric Design Language and Response Surface Methodology

“…When the analytical expression for the relationship between the optimization variables and optimization objectives cannot be found, the other type of methods, the global optimization methods, or the response surface methodology(RSM)combined with FEM can be used for the optimization design. Global optimization methods such as the genetic algorithm [12][13][14][15][16] and the particle swarm optimization algorithm [8,[17][18][19] need to call a large number of modeling and optimization programs repeatedly, which significantly affect the efficiency of optimization. Most importantly, without appropriate fitness functions the local search ability may become worse and the search efficiency may be reduced.…”