Development of Seasonal Adjustment Models for Flexible Pavements (05-1367)**
Sameh Zaghloul, International Transportation Asset Management Specialists (ITAMSs)
Amr Ayed, Stantec Consulting
Halim Omar Abd El Halim, Carleton University, Canada
Nicholas P. Vitillo, Center for Advanced Infrastructure and Transportation (CAIT)
Nenad Gucunski, Rutgers University

Pavement design and performance are highly influenced by environmental factors such as temperature and moisture. Since temperature and moisture conditions vary with time (daily, seasonal and longer cycles), adjustment models are required to account for these variations and to bring pavement response parameters measured at different periods to the same standard conditions. A study funded by the New Jersey Department of Transportation (NJDOT) and the Federal Highway Administration (FHWA) is underway to develop temperature and seasonal adjustment models that suit New Jersey conditions. These models will be used in the network- and project-level pavement evaluation, analysis and design. Twenty-four test sections were instrumented to continuously measure environmental and climatic parameters. Deflection testing is being performed on a monthly basis (and bi-monthly during the recovery periods) for the last two years. In addition, two 24-hour testing cycles, in which tests are repeated every 2 hours for a 24-hour period, were performed on selected sections. Comprehensive analyses are performed on the collected data; which include backcalculation to determine the in-situ structural capacity under different environmental conditions, correlation to correlate weather data and sub-surface parameters, and Analysis Of Variance (ANOVA) to study the significance of different environmental parameters on pavements. Regression analysis is then performed to develop models that can be used to predict the impact of environmental factors on pavement performance. Also, statistical and empirical temperature and seasonal correction models are developed. This paper presents the developed models and some of the findings of this study.