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Random Field Modeling of Track Irregularity of Beijing-Guangzhou High-Speed Railway with Karhunen-Loève Expansion

机译:Karhunen-Loève扩展的京广高速铁路轨道不平顺的随机场建模

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As one of the largest civil engineering systems in China, the network of high-speed railway expanded fast in the last decade. The safety of railway operation is of great concern for the whole country. Railway track irregularity is a potential threat to safety of operation and comfort of passengers, and remains a challenging issue for researchers and engineers. Currently, track irregularity data are recorded by various sensors in the comprehensive inspection cars in China. To reveal relations between the operation of high-speed trains and the track geometry, the data mining and random modeling of track irregularity are needed. In this paper, different methods to evaluate the track irregularities are presented at first. A case study of a section in Beijing-Guangzhou high-speed railway is studied using the Karhunen-Loève expansion. Track geometries that are a representation of this railway network are generated along with statistical and frequency validations. As an application based on generated random track geometries, accelerations of train body under different traveling velocities are calculated and analyzed using a simulation model.
机译:作为中国最大的土木工程系统之一,高铁网络在过去十年中发展迅速。铁路运行的安全性是整个国家的高度关注。铁路轨道不规则是对操作安全和乘客舒适度的潜在威胁,并且对于研究人员和工程师而言仍然是一个充满挑战的问题。目前,在中国的综合检查车中,各种传感器记录了轨道不规则数据。为了揭示高速列车的运行与轨道几何形状之间的关系,需要对轨道不规则性进行数据挖掘和随机建模。本文首先介绍了评估轨道不规则性的不同方法。使用Karhunen-Loève展开法研究了京广高铁一段的案例。代表此铁路网络的轨道几何图形与统计和频率验证一起生成。作为基于生成的随机轨道几何形状的应用程序,使用仿真模型来计算和分析列车在不同行驶速度下的加速度。

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