This report documents efforts of the Arizona Department of Transportation (ADOT) to implement the American Association of State Highway and Transportation Officials (AASHTO) DARWin-ME pavement design guide in Arizona. The research team also prepared a practical stand-alone user's guide that provides guidance for obtaining inputs, conducting design, and establishing the recommended pavement design. Implementation focused on identifying the desired pavement design application of flexible hot-mix asphalt (HMA) pavements, composite pavements (thin asphalt rubber friction course over jointed plain concrete pavement [JPCP] and continuously reinforced concrete pavement [CRCP]), JPCP, and HMA overlays of flexible pavement; characterizing materials and subgrades; determining traffic loadings (conducted under Darter et al. 2010); collecting and assembling DARWin-ME input data from 180 Long Term Pavement Performance and pavement management system sections of flexible, rigid, composite, and rehabilitated pavements; calibrating the DARWin-ME distress and International Roughness Index (IRI) prediction models to Arizona conditions; and training ADOT staff. Several biased distress and IRI models were corrected through the local calibration of Arizona pavements. Several key inputs were more accurately defined and Arizona defaults provided (e.g., subgrade resilient modulus). The calibration process improved these models through verification, validation, and calibration with Arizona data. Overall, the inputs and calibrated models will provide more accurate, reliable, and cost-effective pavement designs than designs created with global calibrations.--Abstract, Technical report documentation page.

This guide provides guidance to calibrate the Mechanistic-Empirical Pavement Design Guide (MEPDG) software to local conditions, policies, and materials. It provides the highway community with a state-of-the-practice tool for the design of new and rehabilitated pavement structures, based on mechanistic-empirical (M-E) principles. The design procedure calculates pavement responses (stresses, strains, and deflections) and uses those responses to compute incremental damage over time. The procedure empirically relates the cumulative damage to observed pavement distresses.

Introduction -- Mechanistic-Empirical Pavement Design Guide and AASHTOWare Pavement ME Design (TM) Software Overview -- Survey of Agency Pavement Design Practices -- Common Elements of Agency Implementation Plans -- Case Examples of Agency Implementation -- Conclusions.

The Kansas Department of Transportation is transitioning from adherence to the 1993 American Association of State Highway and Transportation Officials (AASHTO) Pavement Design Guide to implementation of the new AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) for flexible and rigid pavement design. This study was initiated to calibrate MEPDG distress models for Kansas. Twenty-seven newly constructed projects were selected for flexible pavement distress model calibration, 21 of which were used for calibration and six that were selected for validation. In addition, 22 newly constructed jointed plain concrete pavements (JPCPs) were selected to calibrate rigid models; 17 of those projects were selected for calibration and five were selected for validation. AASHTOWare Pavement ME Design (ver. 2.2) software was used for design analysis, and the traditional split sampling method was followed in calibration. MEPDG-predicted distresses of Kansas road segments were compared with those from Pavement Management Information System data. Statistical analysis was performed using the Microsoft Excel statistical toolbox. The rutting and roughness models for flexible pavement were successfully calibrated with reduced bias and accepted null hypothesis. Calibration of the top-down fatigue cracking model was not satisfactory due to variability in measured data, and the bottom-up fatigue cracking model was not calibrated because measured data was unavailable. AASHTOWare software did not predict transverse cracking for any projects with global values. Thus thermal cracking model was not calibrated. The JPCP transverse joint faulting model was calibrated using sensitivity analysis and iterative runs of AASHTOWare to determine optimal coefficients that minimize bias. The IRI model was calibrated using the generalized reduced gradient nonlinear optimization technique in Microsoft Excel Solver. The transverse slab cracking model could not be calibrated due to lack of measured cracking data.