HMA s型预测模型的动态模量评价及用AFNOR和LC筛网优化美国筛网方法

Mouhamed Lamine Chérif Aidara, Makhaly Bâ, A. Carter
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摘要

路面设计工具不是通用的。事实上,在美国的路面尺寸计算中,用于计算HMA动态模量的预测模型并不适合用法国方法设计的HMA混合料的矿物骨架特征。本文旨在评估力学经验设计中使用的动态模量预测模型在沥青路面设计中的应用,并考虑到筛系列LC和AFNOR标准,开发新的预测模型。在温度范围(5)和频率(5)的数据下,对六种混合物进行了直接拉伸压缩测试(26-700 LC)。研究了动态模量预测模型(Witczak model 1999和Witczak model 2006)。在不考虑AFNOR或LC筛网的情况下,分别用AFNOR滤网P0.08 (0.08 mm)、R5 (5 mm)、R10 (10 mm)和R14 (14 mm)代替美国筛网的ρ200 (0.075 mm)、ρ4 (4.76 mm)、ρ38 (9.5 mm)和ρ34 (19 mm)。通过与实验室测得的模量|E*|的相关性来评价两个模型的结果是令人满意的(R2 = 0.83,分别为R2 = 0.71和p值= 0.00)。对这些近似模型进行优化,得到的新模型与原模型具有相同的框架,并且与实验室观测数据具有更好的相关性(R2 = 0)。p值= 0.00)。
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Evaluation of Dynamic Modulus of HMA Sigmoidal Prediction Models and Optimization by Approach of U.S. Mesh Sieve by AFNOR and LC Mesh Sieve
Pavement design tools are not universal. Indeed, in the sizing of pavements in the USA, the prediction models used in the calculation of the dynamic modulus of HMA are not adapted to the characterization of the mineral skeleton of the HMA mix designed with the French method. This article aims to assess the predictive models of the dynamic modulus used in the mechanistic-empirical design for their use in the design of bituminous pavements, and to develop new predictive models taking into account the sieve series LC and AFNOR standards. A total of six types of mixtures were subjected to the determination of complex modulus testing by direct tensile-compression on cylindrical specimens (26-700 LC) over a temperature range (5) and frequency (5) data. Dynamic modulus prediction models |E*| are studied Witczak model 1999 and model Witczak 2006. These models do not take into account the AFNOR or LC mesh sieve, an approach was made in relation to the US mesh sieve to replace ρ200 (0.075 mm), ρ4 (4.76 mm), ρ38 (9.5 mm) and ρ34 (19 mm) respectively by the AFNOR mesh P0.08 (0.08 mm), R5 (5 mm), R10 (10 mm) and R14 (14 mm). The result is the production of two models whose are evaluated by correlation with the values |E*| of modulus measured in the laboratory is satisfactory (R2 = 0.83 respectively R2 = 0.71 and p-value = 0.00). The optimization of these approximate models gave new models with the same frame as the original models and a better correlation with the data observed in the laboratory (respectively R2 = 0. 95 and R2 = 0.91 p-value = 0.00).
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