01590179

Component

The performance prediction models in the Pavement-ME are nationally calibrated using in-service pavement material properties, pavement structure, climate and truck loading conditions, and performance data obtained from the Long Term Pavement Performance (LTPP) program. Generally, the nationally calibrated models may not be accurate if the inputs and performance data used to calibrate do not represent a state local conditions and practices. Therefore, each state highway agency (SHA) should evaluate the nationally calibrated performance models to determine the adequacy of predicted field performance before implementing the new M-E design procedure. If the predictions are not satisfactory, local calibration of the Pavement-ME performance models is recommended to improve the performance prediction capabilities reflecting the unique field conditions and design practices. The commonly used calibration technique such as split sampling does not necessarily provide adequate results, especially with small sample sizes. 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 resampling methodologies like bootstrapping includes estimation of parameter without making distribution assumptions. This paper presents the use of resampling techniques to locally calibrate the flexible pavement performance models. The locally calibrated Pavement-ME models improved the performance prediction accuracy. The results of the local calibration show that the validation standard error (SEE) and bias obtained from bootstrapping were much lower than other resampling techniques. In addition, the validation statistics were similar to that of the calibrated model, which indicates robustness of the local model coefficients. The reliability equations after local calibration are a better representation of measured pavement performance in Michigan.

This paper was sponsored by TRB committee AFD60 Standing Committee on Flexible Pavement Design.

01584066

16-3651

English

22p

2016

Transportation Research Board 95th Annual Meeting

Digital/other

Figures; References; Tables

Calibration; Flexible pavements; Mathematical models; Mechanistic-empirical pavement design; Pavement performance

Long-Term Pavement Performance Program

Bootstrapping; Local conditions

Michigan

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

Transportation Research Board Annual Meeting 2016 Paper #16-3651

TRIS, TRB, ATRI

Jan 12 2016 5:35PM