基于智能颗粒传感和CT扫描的快速重载铁路有砟道床动力响应微细观机理分析研究

随着我国高速客运和快捷重载货运需求的不断增长以及“一带一路”战略的实施，客货一体快速重载铁路发展前景可期。高频/高幅值循环列车动载及其交替作用对有砟轨道病害发展有推动作用，本项目针对有砟道床荷载特征及传播规律和动力响应演化规律关键难题展开研究。首先利用智能颗粒传感和CT扫描技术构建散体道砟微细观尺度分析和表征方法，构建反映颗粒真实形状的离散元-有限元耦合动力学数值分析方法，获取快速重载铁路道砟层荷载谱。然后，结合室内静动三轴和足尺模型试验分析循环动载下道砟颗粒运动、粒间结构和力链演化规律及其与强度和累积变形之间的微细观机制。基于多工况的“道砟-路基土”复合试样模拟翻浆冒泥现象，从微细观角度分析道砟迁移、夹层变化与翻浆冒泥孕育过程，揭示有砟道床翻浆冒泥孕育条件和微细观机理，并利用现场典型工点的实时监测数据进行验证分析。为快速重载铁路有砟道床的科学设计和建养提供理论依据与技术指导。

With the increasing demands of high-speed passenger and heavy-axle freight trains and the progressing "One Belt One Road" strategy, the construction of high-speed shared railroad corridors is booming. The application of passenger and freight train loads would accelerate the deterioration of ballasted trackbed. This research study is scoped for the challenging problems of determining load spectra in ballasted trackbed and disclosing mechanisms of evolving dynamic responses. First, the smart particulate sensor and micro-CT technologies are employed to quantify the micro- and meso-scale internal structure characteristics, followed by developing the coupled DEM-FEM numerical method for dynamic analysis of the complex rail system. The load spectra in ballasted tracked is therefore realistically determined based on this numerical framework. Second, laboratory static and cyclic triaxial tests and full-scale model tests are conducted to relate strength and deformation characteristics with micro-mechanical and meso-scale particle movement patterns, fabric and force chain characteristics. The inherent mechanisms governing strength and deformation failures are thus disclosed. Finally, the mud-pumping phenomena are simulated by laboratory specimens made of ballast particles underlain by embankment soils and loaded under different physical conditions. The migration of ballast particles, the inter-layer formation between ballast and soils, and the occurrence of mud-pumping are analyzed from micro- and meso-scale perspectives. The mechanisms of mud-pumping are disclosed accordingly and further validated using field data collected by smart sensors in the typical mud-pumping sites. The findings of this study are helpful for optimal design and maintenance of high-speed shared railroad corridors.