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Development of Assessment and optimization methods for vibration reduction in subway track.

地铁轨道减振评估和优化方法的开发。

基本信息

批准号：
13650517
负责人：
ABE Kazuhisa
金额：
$ 1.54万
依托单位：
Niigata University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2001
资助国家：
日本
起止时间：
2001 至 2003
项目状态：
已结题

项目摘要

The objective of this project is to develop the following three numerical methods associated with the vibration reduction of subway track. Through numerical applications the validity of the methods has been discussed.1.Construction of three-dimensional analysis methodIn the dynamic interaction problem of subway track, ballast, tunnel and ground were modeled as a three-dimensional wave field. The three-dimensional problem was transformed to a quasi two-dimensional one through Fourier transform with respect to the tunnel axis. It was found that the developed three-dimensional model enables us to attain the good estimation for the track dynamic response.2.Development of an identification method for unknown parametersIn this study the damping constants of rail and sleeper pads and the shear modulus of ballast were considered as unknown parameters in the numerical model. The identification method was constructed by means of the generic algorithm(GA). In order to reduce the computational work, a neural network was employed for the evaluation of fitness function in GA. The performance of the method was verified through the application to numerical model and in situ measurements.3.Development of an optimization method for subway track structureA multi-objective problem was defined within the framework of the optimization concerning the vibration reduction, construction cost and life of rail. The searching of Pareto solutions was carried out by multi-objective GA. The feasibility of the method was proved by numerical examples.

本项目的目标是开发与地铁轨道减振相关的以下三种数值方法。通过数值算例验证了该方法的有效性。1.三维分析方法的构建在地铁轨道、道碴、隧道和地面的动力相互作用问题中，将轨道、道碴、隧道和地面作为三维波场来模拟。通过傅立叶变换，将三维问题转化为关于隧道轴线的准二维问题。研究结果表明，所建立的三维模型能够较好地估计轨道动力响应。2.未知参数识别方法的研究在本文中，钢轨和轨枕垫的阻尼常数以及道碴的剪切模量被认为是数值模型中的未知参数。辨识方法是通过遗传算法（GA）。为了减少计算量，在遗传算法中采用神经网络来评估适应度函数。通过数值模型和现场实测验证了该方法的有效性。3.地铁轨道结构优化方法的研究在轨道结构优化的框架内，定义了一个以减振、造价和寿命为目标的多目标优化问题。采用多目标遗传算法进行Pareto解的搜索。数值算例验证了该方法的可行性。