Kinetics-based aging prediction of asphalt mixtures using field deflection data Academic Article uri icon

abstract

  • © 2017, © 2017 Informa UK Limited, trading as Taylor & Francis Group. Asphalt pavements suffer from field aging that accelerates distresses and reduces service life of pavements under the complex in-service environment. The majority of the current methods to study aging are performed in an indirect manner, designed in the laboratory to simulate the field. This study targets the field pavement aging directly and aims at characterising and predicting field aging using the kinetics-based modelling, which contains the field mixture moduli and field aging temperatures. The Falling Weight Deflectometer (FWD) data from the Long-Term Pavement Performance (LTPP) database are employed to provide the field moduli of asphalt mixtures over a long aging period. A pavement temperature model that considers solar radiation, air temperature and wind speed is utilised to determine the temperature fluctuations over the entire aging period and then appropriate field aging temperatures. Eight pavement sections from the four different climate zones are selected and the modulus, temperature, mixture property and climate data are collected. For each pavement section, the rheological properties are first determined to characterise the temperature-dependency of field asphalt mixtures. Then based on the kinetics-based aging prediction models for asphalt pavements, the aging properties are determined to characterise and predict aging of field asphalt mixtures. In the determination of aging properties, it is critical to divide the modulus vs. aging time curve into different segments according to the features of the constant-rate period and fast-rate period. The proposed approaches are validated by comparing the predicted moduli at specified ages to the field measurements by the FWD. It is expected that with more calibration and validation work accomplished in the future, the number of FWD measurements required for nondestructive evaluations could be reduced with the aid of the approaches proposed in this study.

author list (cited authors)

  • Luo, X., Gu, F., & Lytton, R. L.

citation count

  • 16

publication date

  • February 2017