The AASHTO Guide for Design of Pavement Structures is the primary document used by the state highway agencies to design new and rehabilitated highway pavements. Currently the Kansas Department of Transportation (KDOT) uses the 1993 edition of the AASHTO pavement design guide, based on empirical performance equations, for the design of Jointed Plain Concrete Pavements (JPCP). However, the newly released Mechanistic-Empirical Pavement Design Guide (MEPDG) provides methodologies for mechanistic-empirical pavement design while accounting for local materials, environmental conditions, and actual highway traffic load distribution by means of axle load spectra. The major objective of this study was to predict pavement distresses from the MEPDG design analysis for selected in-service JPCP projects in Kansas. Five roadway sections designed by KDOT and three long term pavement performance (LTPP) sections in Kansas were analyzed. Project-specific construction, materials, climatic, and traffic data were also generated in the study. Typical examples of axle load spectra calculations from the existing Weigh-in-Motion (WIM) data were provided. Vehicle class and hourly truck traffic distributions were also derived from Automatic Vehicle Classification (AVC) data provided by KDOT. The predicted output variables, IRI, percent slabs cracked, and faulting values, were compared with those obtained during annual pavement management system (PMS) condition survey done by KDOT. A sensitivity analysis was also performed to determine the sensitivity of the output variables due to variations in the key input parameters used in the design process. Finally, the interaction of selected significant factors through statistical analysis was identified to find the effect on current KDOT specifications for rigid pavement construction. The results showed that IRI was the most sensitive output. For most projects in this study, the predicted IRI was similar to the measured values. MEPDG analysis showed minimal or no faulting and was confirmed by visual observation. Only a few projects showed some cracking. It was also observed that the MEPDG outputs were very sensitive to some specific traffic, material, and construction input parameters such as, average daily truck traffic, truck percentages, dowel diameter, tied concrete shoulder, widened lane, slab thickness, coefficient of thermal expansion, compressive strength, base type, etc. Statistical analysis results showed that the current KDOT Percent Within Limits (PWL) specifications for concrete pavement construction are more sensitive to the concrete strength than to the slab thickness. Concrete slab thickness, strength, and truck traffic significantly influence the distresses predicted by MEPDG in most cases. The interactions among these factors are also almost always evident.

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.

The Kansas Department of Transportation (KDOT) is moving toward the implementation of the new American Association of State Highway and Transportation Officials (AASHTO) Mechanistic-Empirical Pavement Design Guide (MEPDG) for pavement design. The MEPDG provides a rational pavement design framework based on mechanistic-empirical principles to characterize the effects of climate, traffic, and material properties on the pavement performance, as compared with the 1993 AASHTO Guide for Design of Pavement Structures. Before moving to the MEPDG, the nationally calibrated MEPDG distress prediction models need to be further validated and calibrated to the local condition. The objective of this research was to improve the accuracy of the MEPDG to predict the pavement performance in Kansas. This objective was achieved by evaluating the MEPDG-predicted performance of Kansas projects, as compared with the pavement performance data from the pavement management system (PMS), and calibrating the MEPDG models based on the pavement performance data. In this study, 28 flexible pavement projects and 32 rigid pavement projects with different material properties, traffic volumes, and climate conditions were strategically selected throughout Kansas. The AASHTO ME Design software (Version 1.3) was used in this study. The comparisons between the MEPDG-predicted pavement performance using the nationally calibrated models and the measured pavement performance indicated the need for the calibration of the MEPDG models to the Kansas conditions. For new flexible pavements, the MEPDG using the nationally calibrated models overestimated the rutting due to the overprediction of the deformation of the subgrade layer. Biases also existed between the predicted top-down cracking, thermal cracking, and International Roughness Index (IRI) and the measured data. The relationship between the measured and the predicted IRIs was more obvious than that for the cracking. Using the coefficients determined through local calibration in this study, the biases and the standard errors were minimized for all the models based on the statistical analysis. For new rigid pavements, very low mean joint faulting was measured in actual projects as compared with the default threshold of the MEPDG. The type of base course had a minor effect on the pavement performance. The traditional splitting data method was adopted in the process of local calibration. After the local calibration, the biases between the predicted pavement performance (mean joint faulting and IRI) and the measured pavement performance were minimized, and the standard errors were reduced.

This 2020 3rd edition includes the following revisions and updates from the previous 2015 2nd edition: new fracture mechanics-based model for reflective cracking in AC overlays over flexible, semi-rigid, and rigid pavements; new mechanistic-empirical model for short jointed plain concrete pavement (SJPCP) overlays of flexible pavements; new flexible and semi-rigid pavement global calibration coefficients; addition of non-structural preventative maintenance treatment consideration for flexible and rigid pavements; addition of five level 3 default distributions for normalized axle load spectra (NALS); updated climate discussion for Modern Era Retrospective Reanalysis (MERRA) and North American Regional Reanalysis (NARR) data; incorporation of crack load transfer efficiency (LTE) for flexible pavements; expanded guidance for creep compliance and indirect tensile strength inputs for asphalt wearing surface layers; and updated standards references.

As AASH is expected to eventually adopt the MEPDG at its primary pavement design method, it is critical that the SDDOT become familiar with the MEPGD documentation and associated design software. The research conducted under this project was a first step toward achieving this goal.