Predictive models for storage modulus and loss modulus of asphalt mixtures

Veena Venudharan, Anush K. Chandrappa, Krishna P. Biligiri, Kamil Kaloush

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Complex modulus of an asphalt mixture constitutes two components: E' representing the ability of the mixture to store energy (elastic behavior), and E" reflecting the capacity of the material to dissipate energy (viscous behavior). The main objective of this study was to develop predictive equations for the two components, E' and E", to better quantify and assess the performance of conventional and modified mixtures alternate to standard laboratory testing. The dataset used in this effort encompassed 163 conventional dense graded asphalt concrete (DGAC), 13 asphalt-rubber asphalt concrete (ARAC) gap-graded, and 9 asphalt-rubber friction course (ARFC) open-graded mixes covering 5,550 data points. Aggregate gradation, binder, and volumetric property parameters were used as predictor variables. Squared-error optimization mathematical techniques were employed in developing predictive models. The statistical goodness of fit measures of E' and E" predictive models were very good to excellent. Validation results of the predictive models reflected effectiveness in reproducing observed values with goodness-of-fit measures in the domain of fair to excellent. Sensitivity performance analyses were also carried out to demonstrate the performance of asphalt mixtures with respect to different material properties.

Original languageEnglish (US)
Article number04016038
JournalJournal of Materials in Civil Engineering
Volume28
Issue number7
DOIs
StatePublished - Jul 1 2016

Fingerprint

asphalt
Asphalt mixtures
Asphalt concrete
Elastic moduli
Rubber
Asphalt
Binders
Materials properties
Friction
Testing

Keywords

  • Asphalt-rubber gap graded
  • Beta distribution
  • Conventional dense graded asphalt
  • Loss modulus
  • Predictive models
  • Sigmoidal distribution
  • Storage modulus

ASJC Scopus subject areas

  • Building and Construction
  • Civil and Structural Engineering
  • Materials Science(all)
  • Mechanics of Materials

Cite this

Predictive models for storage modulus and loss modulus of asphalt mixtures. / Venudharan, Veena; Chandrappa, Anush K.; Biligiri, Krishna P.; Kaloush, Kamil.

In: Journal of Materials in Civil Engineering, Vol. 28, No. 7, 04016038, 01.07.2016.

Research output: Contribution to journalArticle

Venudharan, Veena ; Chandrappa, Anush K. ; Biligiri, Krishna P. ; Kaloush, Kamil. / Predictive models for storage modulus and loss modulus of asphalt mixtures. In: Journal of Materials in Civil Engineering. 2016 ; Vol. 28, No. 7.
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