Flexible Pavement Distresses Prediction Models using AASHTOWare

Abstract

This study mainly focuses on the implementation of the mechanistic-empirical (M-E) analysis method using AASHTOWare for flexible pavement distresses prediction-models generation. To achieve that four steps were followed. First, the most accurate assessment that shows the combined impact of the most important parameters that affect flexible pavement performance was considered in berforming the AASHTOware runs. In which, 378 design combinations of (3 traffic speed levels × 3 traffic load levels ×3 climatic zones × 7 Surface HMA mixes widely used in Egypt) at two input levels of the MEPDG hierarchy (levels 1 &2) that typically are required for binders and hot-mix-asphalt (HMA) were used. Second, a sensitivity analysis to study the combined effect and impact of the investigated parameters on MEPDG-predicted performance (cracking, rutting, and roughness) was conducted at the two input levels. Third, a Multiple Linear Regression (MLR) as a modeling approach to develop five performance prediction models for flexible pavements based on the MEPDG software results was implemented. The proposed MLR models predicted each distress as a function of climatic factors, the surface HMA properties, different regions' speed levels, and traffic volume levels. Finally, a validation process of the proposed MLR prediction models was conducted. Results indicated that the proposed models yield an overall good prediction, asserting the robustness of the proposed process. This study provides a procedure to develop the performance prediction models of flexible pavements based on the MEPDG approach and in accordance with different regions’ input levels on pavement performance

Keywords

• pavement management systems (pms)
• aashtoware pavement me design
• sensitivity analysis
• prediction distress models
• performance models