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Calibration of the AASHTOWare Pavement ME Design Software for PCC Pavements in Idaho
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2019-08-01
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[PDF-4.25 MB]
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Edition:Final Report 09/01/2017 - 8/31/2019
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Abstract:The performance prediction models incorporated in the AASHTOWare Pavement ME Design (PMED) software are calibrated using data obtained from the Long-Term Pavement Performance (LTPP) program. The nationally calibrated models are to be evaluated to determine whether they accurately predict field performance for local conditions. Otherwise, some pavements will be overdesigned and others under designed, turning to either excessive costs or premature failure. This study aims to evaluate and improve the accuracy of the prediction of the performance of rigid pavements in Idaho using the PMED software. The work done in this project is based on the PMED software version 2.5.3, which was the latest release by AASHTO at the time of this project. The calibration of the performance models of Portland Cement Concrete (PCC) pavements addressed in this study is performed for the transverse slab cracking, joint faulting and International roughness Index (IRI) models for the jointed plain concrete pavements (JPCP). Performance models of the continuous reinforced concrete pavements (CRCP) include Punchout and IRI. It was not possible to evaluate and calibrate the CRCP models due to lack of sites in Idaho. However, few LTPP sites in adjacent states were used to evaluate the CRCP models, but there was not sufficient data to conduct the calibration. For the JPCP calibration, a total of 40 PCC pavement sites were selected across the state of Idaho. These sites represent different climate zones, traffic levels, pavement structures, and materials. The required PMED inputs and the historical performance data for the selected sites were extracted from a variety of sources including project construction data, material testing records, and the ITD Transportation Asset Management System (TAMS). The accuracy of the nationally-calibrated PMED prediction models for Idaho conditions was statistically evaluated. The results showed a difference between the distress prediction and field observation. Therefore, calibrating the PMED models was recommended and the local calibration coefficients was developed. The calibrated models provide better accuracy with less bias and errors.
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