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Calibration of Pavement ME Design and Mechanistic-Empirical Pavement Design Guide Performance Prediction Models for Iowa Pavement Systems

机译:路面ME设计的标定和力学-经验路面设计指南爱荷华州路面系统的性能预测模型

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The AASHTO mechanistic-empirical pavement design guide (MEPDG) pavement performance models and the associated AASHTOWare pavement ME design software are nationally calibrated using design inputs and distress data largely from the national long-term pavement performance (LTPP). Further calibration and validation studies are necessary for local highway agencies' implementation by taking into account local materials, traffic information, and environmental conditions. This study aims to improve the accuracy of MEPDG/pavement ME design pavement performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 70 sites from Iowa representing both jointed plain concrete pavements (JPCPs) and hot mix asphalt (HMA) pavements were selected. The accuracy of the nationally calibrated MEPDG prediction models for Iowa conditions was evaluated. The local calibration factors of MEPDG performance prediction models were identified using both linear and nonlinear optimization approaches. Local calibration of the MEPDG performance prediction models seems to have improved the accuracy of JPCP performance predictions and HMA rutting predictions. A comparison of MEPDG predictions was also performed between two software programs to assess if the local calibration coefficients determined from one software program is acceptable with the use of another software program, which has not been addressed before. Few differences are observed between one software program and MEPDG predictions with nationally and locally calibrated models for: (1) faulting and transverse cracking predictions for JPCP; and (2) rutting, alligator cracking, and smoothness predictions for HMA. With the use of locally calibrated JPCP smoothness (IRI) prediction model for Iowa conditions, the prediction differences between the two software programs are reduced. Finally, recommendations are presented on the use of identified local calibration coefficients with the two software programs for Iowa pavement systems.
机译:AASHTO机械-经验路面设计指南(MEPDG)路面性能模型和相关的AASHTOWare路面ME设计软件在全国范围内使用设计输入和遇险数据进行了校准,这些数据主要来自国家长期路面性能(LTPP)。考虑到当地材料,交通信息和环境条件,进一步的校准和验证研究对于地方公路机构的实施是必要的。本研究旨在通过对MEPDG预测模型进行局部校准来提高爱荷华州路面系统的MEPDG /路面ME设计路面性能预测的准确性。从爱荷华州总共选择了70个地点,分别代表了普通水泥混凝土路面(JPCP)和热拌沥青路面(HMA)。评估了针对爱荷华州条件的全国校准MEPDG预测模型的准确性。使用线性和非线性优化方法确定了MEPDG性能预测模型的局部校准因子。 MEPDG性能预测模型的本地校准似乎已经提高了JPCP性能预测和HMA车辙预测的准确性。在两个软件程序之间也进行了MEPDG预测的比较,以评估从一个软件程序确定的局部校准系数是否可以与另一软件程序一起使用,这一点之前尚未解决。在一个软件程序与MEPDG预测与国家和本地校准模型之间几乎没有发现差异:(1)JPCP的断层和横向裂缝预测; (2)HMA的车辙,鳄鱼裂纹和光滑度预测。通过针对爱荷华州条件使用本地校准的JPCP平滑度(IRI)预测模型,减少了两个软件程序之间的预测差异。最后,提出了有关在爱荷华州路面系统的两个软件程序中使用已识别的局部校准系数的建议。

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