Local Calibration of Rigid Pavement Cracking Model in the New Mechanistic-Empirical Pavement Design Guide using Bootstrapping

The local calibration of the performance models in the new mechanistic-empirical pavement design guide is a challenging task, especially due to the lack of needed data. For the selected set of pavement sections for local calibration, the data requirements include: (a) a wide range of inputs related to traffic, climate, design and material characterization, and (b) a reasonable extent and occurrence of observed performance data over time. In addition, to data limitations, the conventional statistical methods of split sampling for model calibration and validation further add to these complications. In the traditional approach about 70% of the data set is used for calibration and remaining 30% is utilized for validation. However, most of the States have a limited number of identified pavement sections for local calibration. Therefore, there is a need to employ statistical methodologies that are more efficient and robust for model calibrations given the data related challenges encountered by state highway agencies. In this paper, the rigid pavement cracking model was calibrated using the traditional and advanced statistical resampling approaches like jackknifing and bootstrapping. Jackknifing and bootstrapping methods provide more reliable assessment of the model prediction accuracy than the alternative methods. While traditional split sample approach uses a two-step process for calibration and validation, advance approaches can simultaneously consider both steps. Moreover, the goodness-of-fit statistics are based on predictions rather than on data used for fitting the model parameters. The efficiency and robustness of such approaches become more important when the sample size is small. The results in the paper show the effect of different approaches on model parameter estimations and compare the role of such parameters in reducing the bias and the standard error of the cracking model.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: pp 100-110
  • Monograph Title: T&DI Congress 2014: Planes, Trains, and Automobiles

Subject/Index Terms

Filing Info

  • Accession Number: 01531780
  • Record Type: Publication
  • ISBN: 9780784413586
  • Files: TRIS, ASCE
  • Created Date: Jun 2 2014 3:01PM