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Change to Poster Presentation at the Conference, with Full Paper in Conference Proceedings

Pavement Design, Evaluation, Preservation, Maintenance, and Rehabilitation

Session: Meet the Author - Poster Session PS-5

1374299 - Impact of Climate Data Sources on MEPDG Pavement Distress Predictions

Friday, June 16, 2023
1:30 PM – 3:00 PM CST
Room: Governor's Grand Ballroom
Salon ABC
TRACK: Poster Presentation with a Paper in the Proceedings
In the Pavement Mechanistic-Empirical Design (PMED) approach, several climatic factors as air temperature, precipitation, wind speed, percent sunshine, and humidity are considered for predicting different types of pavement distresses. The current version of AASHTOWare (version 2.6.22) is integrated with Modern-Era Retrospective Analysis for Research and Application (MERRA v2.0) database to retrieve these climatic data inputs for the prediction of distresses in flexible pavements. This paper compares the predicted distresses of flexible pavements at different locations in the state of New Jersey using four different climate data sources: (1) Ground Based Weather Station (GBWS) data, (2) MERRA-v2.0 database from the National Aeronautical and Space Administration (NASA), (3) North American Regional Reanalysis (NARR), (4) New Jersey weather and climatic database maintained by Rutgers agricultural experiment station. Climatic data from six different regions of New Jersey are selected for the data collection and the distress predictions are evaluated for a specific pavement structure and traffic level. The results from the study evaluate the impact of climatic data sources on distress predictions and comparison with predictions using existing data sources (MERRA-v2.0) in the AASHTOWare software. Consequently, this could lead to assessing the requirement for recalibration of the performance prediction models for the state of New Jersey.

Learning Objectives:

Attendees can expect to learn the following from this session:
  • Identify a suitable and accurate climatic data source for the state of New Jersey
  • Demonstrates the difference in the distress prediction using various climatic data sources
  • Assess the requirement for recalibration of the performance prediction models for the state of New Jersey