Use of Linear Viscoelastic Theory to Predict Resilient Behavior of Unbound Granular MaterialsKSCE Journal of Civil Engineering (2015)
This paper presents a methodology to estimate the stress-strain relationship of an unbound aggregate base using linear viscoelastic theory. Current Mechanistic-Empirical (ME) pavement design procedure adopts the resilient modulus concept to explain the behavior of granular materials for flexible pavement design. The resilient modulus is a stress dependent material property of granular materials that is different from strength. Although California Bearing Ratio (CBR) test results (i.e., stress and strain) can be used to estimate the strength of a granular material, it is not possible to estimate the resilient modulus directly. Therefore, it is necessary to estimate stress along with strain changes. The convolution integral enables the stress to be estimated from the given strain changes only if the relaxation modulus is measured. Aggregate specimens prepared from two different sources in Georgia were subjected to the relaxation modulus test. From the test data, the time-dependent stress due to a known strain rate was computed as a convolution integral of the strain. The computed stress-strain relationship was compared with that from the resilient modulus (M R ) test. The results indicate that the stress-strain relationships from the M R test and the convolution integral are similar with nearly the same slopes when horizontal stress is assumed to be approximately 45% of vertical stress. This observation supports the use of the proposed methodology by state highway agencies to validate the M R test results for quality control and quality assurance of aggregate base material selection for pavement design and construction.
- aggregate base,
- artificial neural network,
- convolution integral
Citation InformationSung-Hee Kim, Kevin McFall, Jidong Yang and Jin-Hoon Jeong. "Use of Linear Viscoelastic Theory to Predict Resilient Behavior of Unbound Granular Materials" KSCE Journal of Civil Engineering (2015) ISSN: 1976-3808
Available at: http://works.bepress.com/kevin-mcfall/2/