Preparation of the Implementation Plan of AASHTO Mechanistic-Empirical Pavement Design Guide (M-E PDG) in Connecticut

Federal Highway Administration2014PDFManualYut, IliyaMahoney, JamesZinke, ScottUniversity of Connecticut. Connecticut Transportation InstituteConnecticut Advanced Pavement LaboratoryConnecticut. Department of TransportationUnited States. Department of Transportation. Federal Highway AdministrationConnecticut. Department of TransportationConnecticutCalibrationImplementationMechanistic-empirical pavement designPavement distressSensitivity analysisMechanistic-Empirical Pavement Design GuideCT-2274-F-13-15CAPLAB XX-2014SPR-22742002 Mechanistic-Empirical Pavement Design Guide is based on mechanistic-empirical (M-E) principles that provide a uniform platform for the design of flexible, rigid, and composite pavements. It considers design parameters for traffic, structure conditions, environment, and allows the user to specify a reliability Level of the predictions. The distress prediction models were originally calibrated to national averages using data from the Long-Term Pavement Performance (LTPP) effort. The distress models need to be recalibrated with data obtained locally in order to be applicable for the particular materials, construction practices, and environmental conditions encountered in Connecticut. Longitudinal (top-down fatigue), alligator (bottom-up fatigue), thermal cracking, asphalt rutting and total rutting prediction models were analyzed for all pavement designs. Statistical sensitivity analyses were conducted for all of the input ranges identified as pertinent for Connecticut including mix properties, environmental factors, underlying structures etc. All of the inputs were then ranked according to their significance in order to establish target levels of detail as necessary for each input. Because this study only provides analyses based on a limited dataset, it is recommended that all of the M-EPDG models should be calibrated for use throughout the state. An implementation plan is presented along with course/training materials and recommendations for further analysis.132