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Finite Element Analysis of a New Test Specimen for Investigating Mixed Mode Cracks in Asphalt Overlays

  • M. R. M. Aliha
  • M. Ameri
  • A. Mansourian
  • M. R. Ayatollahi
Part of the RILEM Bookseries book series (RILEM, volume 4)

Abstract

Cracking is a common mode of deterioration in asphalt pavements. In general, cracks in the asphalt pavements experience a combination of opening and sliding deformation due to thermal and traffic loads. In this research, a new test specimen called ASCB is proposed for mixed mode I/II fracture toughness study of asphalt materials. The ASCB specimen is a semi-circular specimen containing a crack normal to the specimen edge and subjected to asymmetric three-point bend loading. Simple geometry and convenience of testing set up are two primary advantages of the ASCB specimen. In addition, the disc shape of specimen facilitates its preparation using the conventional gyratory compactor machines or using the asphalt field coring devices. The stress intensity factors (K I and K II) are fundamental parameters in order to characterize the load bearing capacity of asphalt failure due to brittle fracture or fatigue crack growth. Hence, in this paper the stress intensity factors of the ASCB specimen are calculated from several finite element analyses and for different mixed mode loading conditions. The numerical results show that the complete mode mixities ranging from pure mode I (opening mode) to pure mode II (in-plane sliding) can be achieved from the ASCB specimen by changing the loading support positions relative to the crack plane. It is also shown that the suggested laboratory specimen is also very suitable for simulating the stress and deformation fields of real cracked pavements which are subjected to the loads induced by the wheels of the moving vehicles.

Keywords

Stress Intensity Factor Fatigue Crack Growth Mixed Mode Asphalt Mixture Asphalt Pavement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© RILEM 2012 2012

Authors and Affiliations

  • M. R. M. Aliha
    • 1
  • M. Ameri
    • 1
  • A. Mansourian
    • 2
  • M. R. Ayatollahi
    • 1
  1. 1.Iran University of Science and Technology, NarmakTehranIran
  2. 2.Iran Ministry of Road and TransportationTransportation Research InstituteTehranIran

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