Using Multi-Objective Optimization to Enhance Calibration of Performance Models in the Mechanistic Empirical Pavement Design Guide

This research study devised two scenarios for application of multi-objective optimization to enhance calibration of performance models in the American Association of State Highway and Transportation Officials (AASHTO) AASHTOWare® Pavement ME Design software. (1) In the primary scenario, mean and standard deviation of prediction error are simultaneously minimized to increase accuracy and precision at the same time. In the second scenario, model prediction error on data from Federal Highway Administration’s Long-Term Pavement Performance test sections and error on available accelerated pavement testing data are treated as independent objective functions to be minimized simultaneously. The multi-objective optimization results in a final pool of tradeoff solutions, where none of the viable sets of calibration factors are eliminated prematurely. Exploring the final front results in more reasonable calibration coefficients that could not be identified using single-objective approaches. This report demonstrates the application of engineering judgment and qualitative criteria to select reasonable calibration coefficients from the final pool of solutions that result from the multi-objective optimization. More reasonable calibration factors result in a more justifiable pavement design considering multiple aspects of pavement performance. This investigation revealed that simply evaluating the bias and standard error is not adequate for a comprehensive evaluation of performance prediction models.


  • English

Media Info

  • Media Type: Web
  • Edition: Final Report
  • Features: Figures; References; Tables;
  • Pagination: 156p

Subject/Index Terms

Filing Info

  • Accession Number: 01672674
  • Record Type: Publication
  • Report/Paper Numbers: FHWA-HRT-17-104
  • Contract Numbers: DTFH61-14-C-00025
  • Files: TRIS, ATRI, USDOT
  • Created Date: Jun 19 2018 9:50AM