Mechanistic-Empirical Rut Prediction Model for In-Service Pavements
Mechanistic-Empirical Rut Prediction Model for In-Service Pavements
复制标题
在用路面的机械经验车辙预测模型
DOI：
--
复制
发表时间：
2000
期刊：
影响因子：
--
通讯作者：
D. Park
中科院分区：
文献类型：
--
作者：
Hyung Bae Kim;N. Buch;D. Park
车辙是柔性路面的主要失效模式。开发准确的预测车辙性能模型是路面工程界不断追求的目标。这导致了过多的常规预测模型，从纯粹的机械模型到经验模型。介绍了机械经验车辙预测模型的开发，该模型使用来自密歇根州 39 个正在使用的柔性路面的数据。所提出的模型考虑了路基、底基层、基层和沥青混凝土层的车辙贡献。该模型解决了库存类型变量，如路面横截面、环境温度和沥青稠度特性。该模型的适用性通过使用 24 个长期路面性能 - 全球定位系统 (GPS) 站点的数据进行了验证。对于 24 个 GPS 站点中的 19 个，预测的车辙深度与测量的车辙深度相差在 5 毫米以内。
Rutting is a major mode of failure in flexible pavements. Development of accurate predictive rut performance models is an ongoing pursuit of the pavement engineering community. This has resulted in a plethora of rut prediction models ranging from purely mechanistic to empirical. Presented is the development of a mechanistic-empirical rut prediction model that uses data from 39 in-service flexible pavements from Michigan. The proposed model accounts for the rut contribution of the subgrade, subbase, base, and asphalt concrete layers. The model addresses inventory-type variables like pavement cross section, ambient temperature, and asphalt consistency properties. The applicability of the model was validated by using data from 24 Long-Term Pavement Performance–Global Positioning System (GPS) sites. For 19 of the 24 GPS sites, the predicted rut depth was within 5 mm of the measured rut depth.