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Global Sensitivity Analysis of Jointed Plain Concrete Pavement Mechanistic-Empirical Performance Predictions

机译:节理素混凝土路面力学-经验性能预测的整体敏感性分析

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摘要

The new AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) provides pavement analysis and performance predictions for various what-if scenarios. MEPDG performance predictions for anticipated climatic and traffic conditions will depend on the values of the input parameters that characterize pavement materials, layers, design features, and condition. A comprehensive global sensitivity analysis methodology is proposed for evaluating performance predictions for jointed plain concrete pavement to MEPDG inputs for five climatic conditions and three traffic levels. MEPDG inputs evaluated in the analysis include traffic volume, layer thicknesses, material properties, groundwater depth, and geometric parameters. Correlations between MEPDG inputs were considered as appropriate. The global sensitivity analysis varied all inputs simultaneously across the problem domain for each of the 15 base cases (five climates × three traffic levels). Two response surface modeling approaches, multivariate linear regressions and artificial neural networks, were developed for evaluation of MEPDG input sensitivities across the problem domain. The response surface modeling approaches based on artificial neural networks not only provided robust and accurate representations of the complex relationships between MEPDG inputs and distress outputs but also captured the variation in sensitivity across the problem domain. The normalized sensitivity index for the design limit proposed in the study provides practical interpretation of sensitivity by relating a given percentage change in an MEPDG design input to the corresponding percentage change in predicted distress relative to its design limit value.
机译:新的AASHTO机械-经验性路面设计指南(MEPDG)提供了各种假设情况下的路面分析和性能预测。 MEPDG对预期的气候和交通状况的性能预测将取决于表征路面材料,层,设计特征和状况的输入参数的值。提出了一种综合的全球敏感性分析方法,用于评估五种气候条件和三种交通水平下,MEPDG输入接缝的普通混凝土路面的性能预测。分析中评估的MEPDG输入包括交通量,层厚度,材料特性,地下水深度和几何参数。 MEPDG输入之间的相关性被认为是适当的。全局敏感性分析针对15个基本案例(五个气候x三种流量水平)中的每一个,在问题域中同时更改所有输入。开发了两种响应面建模方法,即多元线性回归和人工神经网络,用于评估问题域内的MEPDG输入灵敏度。基于人工神经网络的响应面建模方法不仅提供了MEPDG输入和遇险输出之间复杂关系的鲁棒且准确的表示,而且还捕获了整个问题域中灵敏度的变化。通过将MEPDG设计输入中给定的百分比变化与预测的遇险相对于其设计极限值的相应百分比变化相关联,研究中提出的设计极限的标准化灵敏度指标为灵敏度提供了实用的解释。

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