Process and Data Needs for Local Calibration of Performance Models in the AASHTOWARE Pavement ME Software. Issue 1 (January 2015)

• Record Type: Journal Article
• Authors: Haider, Syed Waqar; Brink, Wouter C.; Buch, Neeraj; Chatti, Karim
• Is Part Of: Transportation research record. Volume 2523:Issue 1(2015)
• Page Start: 80
• Page End: 93
• Publication Date: 2015-01
• Subjects: Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.3141/2523-10
• Languages: English
• ISSNs: 0361-1981

Abstract

Local calibration of the performance models in the AASHTOWARE Pavement ME software (Pavement ME) is a challenging task, especially if the data are limited. This paper summarizes the local calibration process for flexible and rigid pavements in Michigan. Other agencies can learn from the steps needed to accomplish a more streamlined local calibration. The local calibration process includes several sequential steps. An adequate number of pavement sections needs to be identified from the pavement management system (PMS) database on the basis of pavement type, age, geographical location, and number of collection cycles for performance data. The selection of the final set of pavement sections is based on distress magnitude over time. The selected pavement sections must be categorized on the basis of measured distresses because the local calibrated models are typically used to predict normal pavement performance at the design stage. For the selected pavement sections, the as-constructed input variables are collected from construction records. However, when such input information is unavailable, the best estimates are used to represent the pavement design and construction practices of the Michigan Department of Transportation. Finally, the typical steps for local calibration by using various resampling techniques are demonstrated for the rutting (flexible) and transverse cracking (rigid) models. The techniques are compared through use of the standard error of the estimate (SEE). The SEE of a technique shows how much variance is explained by the model. The main advantage of using resampling is to quantify the variability associated with the model predictions and parameters. The quantification of the variability will also help in determining more robust design reliability in the Pavement ME.