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首页> 外文期刊>Journal of materials in civil engineering >Analysis of Representative Volume Element for Asphalt Concrete Laboratory Shear Testing
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Analysis of Representative Volume Element for Asphalt Concrete Laboratory Shear Testing

机译:沥青混凝土实验室剪切试验代表性体积元分析

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The primary purposes of this paper are to develop a method of quantifying the precision and bias in repeated simple shear test at constant height (RSST-CH) laboratory test results for different-sized specimens and to determine the effects of this precision and bias on predicted rutting performance. The effect of RSST-CH variability was quantified by using a statistical sampling method called bootstrapping. The contribution of test variability to variability in predicted in situ rutting performance was determined by performing Monte Carlo simulations that used a shear-based incremental-recursive rutting analysis model. Results indicated that significant bias exists between the predicted rut depths of different specimen sizes. Increasing the specimen size decreased the test variability. Specimen size requirements for two different mix types are proposed on the basis of the analysis. The effect of test temperature on test results variability was also determined. In addition, analyzing various rutting performance parameters was used to determine the parameter that contained minimum size-related bias. Permanent shear strain at 5,000 repetitions appears to be an unbiased rutting performance evaluation parameter, when compared with other parameters, because it is not significantly affected by specimen size-related bias when three or more replicate tests are conducted. Analyses were performed by using RSST-CH results and a specific rutting model; however, the general procedure can be used to identify specimen size-related bias and precision for any type of laboratory test and distress model.
机译:本文的主要目的是开发一种定量方法,用于量化不同尺寸样本在恒定高度重复简单剪切试验(RSST-CH)的实验室测试结果中的精度和偏差,并确定此精度和偏差对预测值的影响车辙性能。 RSST-CH变异性的影响通过使用称为自举的统计采样方法进行量化。通过使用基于剪切的增量递归车辙分析模型的蒙特卡罗模拟,可以确定测试变异性对预测的原车辙性能的贡献。结果表明,在不同样品尺寸的预测车辙深度之间存在显着偏差。增大样品尺寸会降低测试变异性。在分析的基础上,提出了两种不同混合物类型的试样尺寸要求。还确定了测试温度对测试结果变异性的影响。另外,通过分析各种车辙性能参数来确定包含最小尺寸相关偏差的参数。与其他参数相比,在5,000次重复下的永久剪切应变似乎是无偏析的车辙性能评估参数,因为当进行三个或更多重复测试时,它不受样品尺寸相关偏差的显着影响。使用RSST-CH结果和特定的车辙模型进行分析。但是,对于任何类型的实验室测试和遇险模型,都可以使用通用程序来确定与样本大小相关的偏差和精度。

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