|dc.description.abstract||Optimization of transportation facilities for capacity and pavement condition could be achieved with mechanistic analysis of pavement structures. This report is focused on using the American Association of State Highway and Transportation Officials (AASHTO) M-E Design Guide (MEPDG) to show the results of quantitative sensitivity analyses of typical pavement structures (rigid and flexible pavements) to highlight the main factors that affect pavement performance in terms of critical distresses and smoothness. The sensitivity analyses were conducted using the Mechanistic-Empirical Pavement Design Guide software (version 8.1). Pavement performance included specifically faulting, transverse cracking, and smoothness for rigid pavements. It also included smoothness, longitudinal cracking, alligator cracking, transverse cracking, and permanent deformation for flexible pavements. The input parameters that were varied included traffic variables [Average Annual Daily Truck Traffic (AADTT), speed, and wander] and pavement structure for selected rigid and flexible pavements. In addition, the binder grade was varied for the flexible pavements. Based on the sensitivity results, the input parameters were ranked and categorized from those to which pavement performance is most sensitive to least sensitive (or insensitive). The ranking should help pavement designers identify the level of importance for each input parameter and also identify the input parameters that can be modified to satisfy the predetermined pavement performance criteria. It is expected that ranking could also help planners to determine how traffic of heavy vehicles could be directed to enhance the service life of various sections of pavement network and to develop better maintenance strategies.
This project identified two important calibration factors for a Midwest implementation of the Mechanistic-Empirical Pavement Design Guide (M-E PDG). The calibration factors are for the fatigue damage model in flexible pavements in Wisconsin. Pavement performance data was collected from Michigan, Ohio, Iowa and Wisconsin state transportation agencies using uniform data structures as spreadsheet templates specifically designed to manage the calibration data. Spreadsheets were developed for both flexible and rigid pavements. Calibration factors were derived by minimizing differences between observed and predicted pavement performance. The gathering of data required for calibration is labor intensive because the data resides in various and incongruent data sets. Furthermore, some pavement performance observations include temporary effects of maintenance and those observations must be removed through a tedious data cleaning process. The scope of calibration factors are limited by these data impediments. For each state, the observed and predicted performances are compared for both flexible and rigid pavements. The predicted performance is computed using default and derived calibration factors. The project includes a case study design as an example for quantifying the benefits of the M-E PDG.||en