摘要：AbstractFlexible pavements comprise about 93 percent of paved roads in the United States. Although flexible pavements are widely used for reasons such as cost, constructability and consistent performance, they are often subject to severe rutting. To gain an insight of flexible pavement rutting under actual vehicular traffic and environmental conditions, a 305-meter long test section was constructed on I-35 (Southbound) in McClain County, Oklahoma, USA and instrumented for field data collection. Field rut measurements were conducted periodically to monitor performance of the test section using a straight edge/rut gauge combination and a Face Dipstick®. The loose mixes were collected from the field and dynamic modulus testing was conducted in the laboratory at different temperatures (i.e., 4, 21, 40, 55°C) and frequencies (i.e., 25, 10, 5, 1, 0.5, 0.1Hz) in accordance with AASHTO TP62. The mechanistic-empirical pavement design guide (MEPDG) recommends dynamic modulus as a key input parameter to predict distresses (rutting and fatigue cracking) of a flexible pavement. Therefore, dynamic modulus based approach is used in the present paper to develop a rut prediction model. Dynamic modulus test data, along with the actual vehicular traffic and environmental data from the test section were used as inputs in multilayered linearelastic analysis software, WinJULEA, to model the test section and determine rutting. A total of approximately 18-million accumulated axles and four years of environmental data were used to develop the field rut prediction model. A vertical strain- based (VSB) rut prediction model was developed using the measured rut on test section and relating it to vertical strain on the top of the aggregate base layer due to passing of each vehicle. The correlation coefficient (R2value) for this model was around 0.78, based on the comparison of field measured and predicted ruts. The results from this study are expected to be useful in predicting rutting of state highway pavements under similar traffic and environmental conditions.
