01553016

Component

The performance prediction models in the Pavement-ME design software are nationally calibrated using in-service pavement material properties, pavement structure, climate and truck loadings, and performance data obtained from the Long-Term Pavement Performance (LTPP) program. Generally, the nationally calibrated models may not perform well if the inputs and performance data used to calibrate those do not represent the local conditions and construction practices. Therefore, before implementing the new mechanistic-empirical (M-E) design procedure, each state highway agency (SHA) should evaluate how well the nationally calibrated performance models predict the measured field performance. The local calibrations of the Pavement-ME performance models are recommended to improve the performance prediction capabilities to reflect the unique conditions and design practices. During the local calibration process, the traditional calibration techniques (split sampling) may not necessarily provide adequate results when a limited number of pavement sections is available. Consequently, there is a need to employ statistical methodologies that are more efficient and robust for model calibrations given the data related challenges encountered by SHAs. The bootstrap is a nonparametric and robust resampling technique for estimating standard errors and confidence intervals of a statistic. The main advantage of bootstrapping is that model parameters estimation is possible without making distribution assumptions. This paper presents the use of bootstrapping to locally calibrate the performance models for rigid pavements. The results of the calibration show that the standard error of estimate (SEE) and bias are lower than the traditional statistical methods. In addition, the validation statistics are similar to that of the locally calibrated model, especially for the International Roughness Index (IRI) model, which indicates robustness of the local model coefficients.

This paper was sponsored by TRB committee AFD50 Rigid Pavement Design.

01550057

15-5748

English

18p

2015

Transportation Research Board 94th Annual Meeting

Digital/other

Figures; References; Tables

Calibration; Mathematical models; Mechanistic-empirical pavement design; Pavement performance; Rigid pavements; State departments of transportation; Statistical sampling

Bootstrapping

Michigan

Design; Highways; Pavements; I22: Design of Pavements, Railways and Guideways

Transportation Research Board Annual Meeting 2015 Paper #15-5748

TRIS, TRB, ATRI

Dec 30 2014 1:55PM