贫混凝土基层混凝土路面结构设计研究

【作者】 刘伟；

【导师】 王秉纲；

【作者基本信息】 长安大学 ， 道路与铁道工程， 2003， 硕士

【摘要】 贫混凝土基层混凝土路面是一种适用于承受重、超重交通的路面结构，由于其种种优点，具有很大的发展潜力。为了推广应用，深入研究其力学特性和结构设计方法。 本文通过疲劳试验对贫混凝土的抗疲劳特性进行了研究，表明贫混凝土具有比常用的半刚性基层材料更加优良的疲劳性能。并提出了贫混凝土两种形式的疲劳方程，据此推导了混凝土路面结构贫混凝土基层的疲劳应力系数和沥青路面结构贫混凝土基层的抗弯拉强度结构系数。 对贫混凝土基层混凝土路面现有结构设计方法的应力计算进行了分析，发现等刚度法用于双层板路面的荷载应力计算是可行的，但用于温度应力计算会引起较大的误差。 利用三维有限元方法对贫混凝土基层混凝土路面进行了荷载应力和温度应力的大量计算，通过回归，得出了贫混凝土基层混凝土路面荷载应力及温度应力实用计算公式。针对规范中轴载换算存在的问题，分别根据面层和基层的荷载应力公式和疲劳方程，提出轴载换算公式以及疲劳应力系数。 论文还提出了贫混凝土基层混凝土路面的概率型结构设计方法。更多还原

【Abstract】 The concrete pavement with lean concrete base (LCB) is a suited kind of pavement structure for heavy and over-heavy traffic. Because of its excellence, it has good future. To popularize it, its mechanical characteristics and method of structure design must be studied.First, the fatigue feature of lean concrete is studied by fatigue test in this paper. It is proved that lean concrete has better fatigue feature than semi-rigid materials. And two types of fatigue equations are put forward. According to them the fatigue stress coefficient of LCB for the concrete pavement structure and the structure coefficient of tension strength of LCB for the asphalt pavement structure are developed.The existing methods of structure design of concrete pavement with lean concrete base are analyzed, proving that the equivalent stiffness method is proper for calculating load stress but not for thermal stress.The load stress and the thermal stress of concrete pavement with lean concrete base are analyzed by three-dimensional finite element method, and the practical formulas of load stress under different axle load models and of thermal stress are get by regression method. The formula of axle load conversion in the standard has some problems, so respectively according to the corresponding formulas of load stress and fatigue equations of concrete pavement and LCB, the different formulas of axle load conversion and fatigue stress coefficients are put forward.At last a probabilistic method of structure design of concrete pavement with LCB is advanced.更多还原