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    Evaluation of the hirsch model for dynamic modulus estimation of asphalt mixtures

    Access Status
    Fulltext not available
    Authors
    Malekzehtab, H.
    Nikraz, Hamid
    Date
    2017
    Type
    Conference Paper
    
    Metadata
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    Citation
    Malekzehtab, H. and Nikraz, H. 2017. Evaluation of the hirsch model for dynamic modulus estimation of asphalt mixtures.
    Source Title
    ISEC 2017 - 9th International Structural Engineering and Construction Conference: Resilient Structures and Sustainable Construction
    ISBN
    9780996043748
    School
    Department of Civil Engineering
    URI
    http://hdl.handle.net/20.500.11937/57011
    Collection
    • Curtin Research Publications
    Abstract

    Copyright © 2017 ISEC Press. The dynamic modulus of the asphalt mixtures is an important factor in designing or analyzing an asphalt concrete pavement, but it is expensive and time consuming to measure. Therefore, it is important to develop a model to predict this value. In this regard, the Hirsch model is a popular model, however, it is developed based on a range of U.S. asphalt mixtures and standards. Therefore, it is not certain that it can be used for asphalt mixtures based on materials and codes other than U.S. This article investigated whether this model performs satisfactorily with two typical asphalt mixtures in Western Australia (WA) containing 0, 10, 20, and 30% of recycled asphalt pavement. To do so, cylindrical samples were made with materials and locally established standards in Western Australia and then tested in Asphalt Mixture Performance Tester (AMPT) machine to acquire their dynamic modulus and phase angle values in different loading frequencies (0.01 to 10 Hz) and temperatures (4 to 40°C). Meanwhile, the results are estimated by the Hirsch model using some properties of the mixture and binder. The properties of the binder in different test conditions are obtained using a dynamic shear rheometer. The comparison of the results showed that the dynamic modulus underestimation or overestimation error can reach to 50 and 280% respectively. Generally, this model did not perform well in this study.

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