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Local Calibration of MEPDG for Flexible Pavements in New Mexico

机译:新墨西哥州柔性路面的MEPDG本地校准

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摘要

Local calibration of the mechanistic-empirical pavement design guide (MEPDG) is performed by determining the pavement-performance model coefficients to minimize the difference between the measured and predicted distresses of New Mexico department of transportation (NMDOT) pavements. A total of 24 New Mexico pavement sections, which have all the MEPDG inputs and quantitative-distress values required for MEPDG calibration, were used for calibration. Pavement-performance models such as rutting, alligator cracking, longitudinal cracking, and roughness models were calibrated by an error-minimization algorithm. In the calibration methodology, the target was fixed to reduce the sum of squared errors, defined by the square of the difference between predicted and measured distress, so that any bias was eliminated and precision was increased. The optimized calibration coefficients are: β_(r1) = 1.1, β_(r2) = 1.1, β_(r3) = 0.8, β_(GB)= 0.8, and β_(SG) = 1.2 for the rutting model; C_1 = 0.625, C_2 = 0.25, and C_3 = 6,000 for alligator cracking; C_1 = 3, C_2 = 0.3, and C_3 = 1,000 for longitudinal cracking; and site factor = 0.015 for roughness. The results show that these calibration coefficients reduce error in the MEPDG prediction and assist better design of flexible pavements using MEPDG in New Mexico.
机译:通过确定路面性能模型系数来最小化新墨西哥州交通部(NMDOT)路面的实测和预测的遇险之间的差异,从而进行机械-经验路面设计指南(MEPDG)的局部校准。总共使用了新墨西哥州的24个路面部分,其中包含所有MEPDG输入和MEPDG校准所需的定量遇险值,用于校准。路面性能模型(例如车辙,鳄鱼裂纹,纵向裂纹和粗糙度模型)通过误差最小化算法进行了校准。在校准方法中,固定目标是为了减少平方误差的总和,误差的平方由预测的和测量的困扰之间的差异的平方确定,从而消除了任何偏差并提高了精度。对于车辙模型,优化的校准系数为:β_(r1)= 1.1,β_(r2)= 1.1,β_(r3)= 0.8,β_(GB)= 0.8和β_(SG)= 1.2;对于鳄鱼裂纹,C_1 = 0.625,C_2 = 0.25和C_3 = 6,000;对于纵向裂纹,C_1 = 3,C_2 = 0.3和C_3 = 1,000;粗糙度的位置系数= 0.015。结果表明,这些校准系数减少了MEPDG预测中的误差,并有助于在新墨西哥州使用MEPDG更好地设计柔性路面。

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