01660208

Component

Sponsored by the Federal Highway Administration’s (FHWA's) Long-Term Pavement Performance (LTPP) program, this study devised two scenarios for application of multi-objective optimization to enhance calibration of performance models in the AASHTOWare® Pavement ME Design Software. 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 LTPP data, and error on available accelerated pavement testing (APT) 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 study 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. The shape of the final non-dominated front indicated that the selected objective functions conflicted with one another and therefore, the application of a multi-objective optimization approach was justified. The first scenario resulted in calibrated models that had higher precision and higher generalization capability, compared to the conventional single-objective calibration. The second scenario has significantly improved the prediction accuracy, precision, and generalization capability of the calibrated model. This study provides a framework for multi-objective calibration and comprehensive evaluation of performance prediction models.

This paper was sponsored by TRB committee AFD60 Standing Committee on Design and Rehabilitation of Asphalt Pavements. Alternate title: Multi-Objective Calibration of Mechanistic-Empirical Pavement Performance Models.

18-03649

English

19p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; References

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

AASHTOWare (Software); Long-Term Pavement Performance Program; Mechanistic-Empirical Pavement Design Guide

Multiobjective optimization

Highways; Pavements

Transportation Research Board Annual Meeting 2018 Paper #18-03649

TRIS, TRB, ATRI

Jan 8 2018 10:54AM