This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 representative pavement sites across Iowa were selected. The selected pavement sites represent flexible, rigid, and composite pavement systems throughout Iowa. The req...

The AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement performance models and the associated AASHTOWare® Pavement ME Design software are nationally calibrated using design inputs and distress data largely from the national Long-Term Pavement Performance (LTPP). Further calibration and validation studies are necessary for local highway agencies’ implementation by taking into acc...

The new AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) provides pavement analysis and performance predictions for various what-if scenarios. MEPDG performance predictions for anticipated climatic and traffic conditions will depend on the values of the input parameters that characterize pavement materials, layers, design features, and condition. A comprehensive global sensitivity ana...

Initiatives are underway to implement the new Mechanistic-Empirical Pavement Design Guide (MEPDG) in Iowa. This paper focuses on the sensitivity study of Jointed Plain Concrete Pavements (JPCP) and Continuously Reinforced Concrete Pavements (CRCP) in Iowa using the MEPDG software. In this comprehensive study, the effect of MEPDG input parameters on the rigid pavement performance is evaluated us...

The performance models used in the Mechanistic-Empirical Pavement Design Guide (MEPDG) are nationally calibrated with design inputs and performance data obtained primarily from the national Long-Term Pavement Performance database. It is necessary to verify and calibrate MEPDG performance models for local highway agencies' implementation by taking into account local materials, traffic informatio...

The new AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) performance predictions for the anticipated climatic and traffic conditions depend on the values of the numerous input parameters that characterize the pavement materials, layers, design features, and condition. This paper proposes comprehensive local sensitivity analyses (LSA) and global sensitivity analyses (GSA) methodologies...

The new AASHTO Mechanistic–Empirical Pavement Design Guide (MEPDG) provides pavement analysis and performance predictions for various feasible design scenarios. The MEPDG performance predictions for the anticipated climatic and traffic conditions depends on the values of the input parameters that characterize the pavement materials, layers, design features, and condition. This paper focuses on ...

The new Mechanistic-Empirical Pavement Design Guide (MEPDG) uses the subgrade resilient modulus (MR) as key input parameter to represent soil behavior for pavement design. increases with an increase in confining pressure, whereas, deviatoric stress, it granular soils and decreases fine-grained soils. value of MR is highly stress dependent, state (i.e., bulk stress) a function position materials...

Many agencies use the AASHTO Guide for Design of Pavement Structures to design their pavement systems. The limitation inherent in this method is the empirical nature of the decision process, which was derived from a road test conducted almost 45 years ago in Ottawa, Illinois. The newly released Mechanistic-Empirical Pavement Design Guide (MEPDG), based on NCHRP Study 1-37A, has adopted a mechan...

15. Supplementary Notes Project performed in cooperation with the Texas Department of Transportation and the Federal Highway Administration. 16. Abstract TxDOT initiated the rigid pavement database project to collect information on the general performance of portland cement concrete (PCC) pavement as well as to collect project-level information on PCC pavement responses and performance. A total...