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Inherent Variability in the Parameters Describing the Linear Viscoelastic Response of Asphalt Concrete

机译：描述沥青混凝土线性粘弹性响应的参数的固有变异性

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Mechanistic methods are introduced to provide a realistic prediction of the performance of asphalt pavements. Despite the advantage of these methods in providing accurate and advanced material characterization, they should take into account the uncertainties associated with material properties and models. Such material properties include the linear viscoelastic response functions that characterizes the behavior of asphalt concrete such as relaxation modulus E(t). Typically, complex modulus E* data is obtained by lab testing and then used to determine E(t) through mathematical interconversion methods. The objective of this study is to propagate the inherent variability in E* data for various reduced times into E(t). This was achieved by analyzing the data of E* testing and interconverted E(t) from three different mixes that include at least eight replicates with a probabilistic framework that allows for a preliminary quantification of uncertainty. Monte Carlo simulations were used to propagate the uncertainties of the Prony series coefficients to determine the mean, coefficient of variation (COV), and probability distribution in E(t) as a function of reduced time.
机译：引入了机械方法以提供对沥青路面性能的现实预测。尽管这些方法在提供准确和高级的材料表征方面具有优势，但它们应考虑与材料特性和模型相关的不确定性。这样的材料特性包括表征沥青混凝土性能的线性粘弹性响应函数，例如松弛模量E（t）。通常，复数模量E *数据是通过实验室测试获得的，然后通过数学互转换方法用于确定E（t）。这项研究的目的是将E *数据的固有可变性在各种减少的时间内传播到E（t）中。这是通过分析E *测试数据和来自三种不同混合物的相互转换的E（t）来实现的，这些混合物包括至少八个重复样品，并具有一个概率框架，可以对不确定性进行初步量化。蒙特卡罗模拟用于传播Prony级数系数的不确定性，以确定均值，变异系数（COV）和E（t）中的概率分布作为减少时间的函数。

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