The mechanistic-empirical pavement design guide (AASHTOWARE Pavement-ME) incorporates mechanistic models to estimate stresses, strains, and deformations in pavement layers using site-specific climatic, material, and traffic characteristics. These structural responses are used to predict pavement performance using empirical models (i.e., transfer functions). The transfer functions need to be calibrated to improve the accuracy of the performance predictions, reflecting the unique field conditions and design practices. The existing local calibrations of the performance models were performed by using version 2.0 of the Pavement-ME software. However, AASHTO has released versions 2.2 and 2.3 of the software since the completion of the last study. In the revised versions of the software, several bugs were fixed.Consequently, some performance models were modified in the newer software versions. As a result, the concrete pavement IRI predictions and the resulting PCC slab thicknesses have been impacted. The performance predictions varied significantly from the observed structural and function distresses, and hence, the performance models were recalibrated to enhance the confidence in pavement designs. Linear and nonlinear mixed-effects models were used for calibration to account for the non-independence among the data measured on the same sections over time. Also, climate data, material properties, and design parameters were used to develop a model for predicting permanent curl for each location to address some limitations of the Pavement-ME. This model can be used at the design stage to estimate permanent curl for a given location in Michigan.Pavement-ME also requires specific types of traffic data to design new or rehabilitated pavement structures. The traffic inputs include monthly adjustment factors (MAF), hourly distribution factors (HDF), vehicle class distributions (VCD), axle groups per vehicle (AGPV), and axle load distributions for different axle configurations. During the last seven years, new traffic data were collected, which reflect the recent economic growth, additional, and downgraded WIM sites. Hence it was appropriate to re-evaluate the current traffic inputs and incorporate any changes. Weight and classification data were obtained from 41 Weigh-in-Motion (WIM) sites located throughout the State of Michigan to develop Level 1 (site-specific) traffic inputs. Cluster analyses were conducted to group sites for the development of Level 2A inputs. Classification models such as decision trees, random forests, and Naive Bayes classifier were developed to assign a new site to these clusters; however, this proved difficult. An alternative simplified method to develop Level 2B inputs by grouping sites with similar attributes was also adopted. The optimal set of attributes for developing these Level 2B inputs were identified by using an algorithm developed in this study. The effects of the developed hierarchical traffic inputs on the predicted performance of rigid and flexible pavements were investigated using the Pavement-ME. Based on the statistical and practical significance of the life differences, appropriate levels were established for each traffic input. The methodology for developing traffic inputs is intuitive and practical for future updates. Also, there is a need to identify the change in traffic patterns to update the traffic inputs so that the pavement sections would not be overdesigned or under-designed. Models were developed where the short-term counts from the PTR sites can be used as inputs to check if the new traffic patterns cause any substantial differences in design life predictions.
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