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2 MEPDG Model Reflection Cracking Model Material Traffic Properties Interlayer INPUT Climate Pavement Existing Pavement EICM Structure Conditions Pavement Response Model Pavement Response (, ) MODELS Stress Intensity Factor (SIF) Model: Multi-layer elastic Artificial Neural Network (ANN) Pavement Distress Pavement Distress Model Reflection Cracking Models (Thermal, Shearing, Bending) OUTPUT Pavement Performance Prediction: Pavement Performance Reflection Cracking Extent Predictions and Severity Figure 1. Compatibility of the MEPDG software with the reflection cracking model. This project has produced two programs: the Design Pro- an accurate overview of the approach and results of this project. gram and a Calibration Program. The Design Program is writ- Much more detailed discussions of the subjects in this report ten as a subprogram to the existing MEPDG software but is are contained in Appendices A through T. These appendices capable of being run separately. The Calibration Program is are not published herein but they are available on the NCHRP set up to allow the user to generate a new set of calibration Report 669 summary web page at coefficients for any other combination of pavement structure- Blurbs/163988.aspx. overlay-climatic zone of special interest to that user. A nar- rated instructional video has been provided for each program Research Approach to take the user through the steps of entering the data, running A single model that uses fracture mechanics as its basis is suf- each program, and displaying the output. An interface has ficient for the analysis of reflection cracking for different over- been provided so that the calibration coefficients that the user lay types. This is appropriate because the model will consider may develop with the Calibration Program can be inserted the difference in the mechanisms of crack propagation and into the Design Program. The Users' Guides to the Design and the field calibration coefficients. Three separate mechanisms of Calibration Programs are in Appendices O and P, respectively. crack propagation, thermal, traffic bending, and traffic shear- The third chapter presents interpretations, appraisals, and ing, were modeled using Artificial Neural Network (ANN) applications of the model that was developed and calibrated. algorithms. This chapter illustrates how well the calibrated predictions of the S-shaped accumulating distress curves fit the observed dis- Material Properties tress patterns. It also discusses the level of detail and amount of input data needed for the user of the Calibration Program The material properties of the overlay are calculated from to develop a new set of calibration coefficients. the design of the asphalt mix of that overlay using a set of The fourth chapter presents conclusions and suggested fur- ANN algorithms that replicate Witczak's 1999 (2) and 2006 ther research. The main body of this report is written to give (3) models of asphalt mixture complex moduli. The input