Modelling of combined physical–mechanical moisture-induced damage in asphaltic mixes, Part 1: governing processes and formulations

Kringos, Nicole; Scarpas, A.; Kasbergen, Cor; Selvadurai, A. P. S.

doi:10.1080/10298430701792185

Cited by 79 publications

(34 citation statements)

References 8 publications

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“…While conditioning, water diffuses through the mastic, aggregate and masticaggregate interface. In field, mastic -aggregate interface can be affected by moisture diffusion through mastic material to aggregate or though aggregate to mastic material (Kringos et al 2008a). In reality, when water diffuses through mastic material and infiltrates into interface and saturate aggregate, then the mastic, aggregate and mastic -aggregate interface degraded its strength.…”

Section: Damage Model Parametersmentioning

confidence: 97%

Quantifying moisture damage at mastic–aggregate interface

Tarefder

2013

International Journal of Pavement Engineering

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This study quantifies moisture damage at the asphalt mastic -aggregate interfaces using finite element method modelling technique in ABAQUS. A model aggregate surrounded by a layer of mastic is subjected to static loads of varying magnitudes and patterns. Using dynamic shear and elastic moduli of wet and dry mastic and aggregate as model inputs, moisture-induced damage is quantified through parameters such as contact stresses at interface, load to initiate damage and de-bonding at the interface. Results show that contact stresses are significantly higher in dry samples than wet samples. It is revealed that damage initiates at a smaller magnitude of deformation (0.0508 mm) in the wet sample than that (0.508 mm) in the dry sample. That is, a stiffer dry sample carries more loads and deforms less before damage initiation than a softer wet sample. In addition, approximately 6.8% interface de-bonding occurs in the dry sample, whereas 49.1% de-bonding occurs in the wet sample.

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Section: Damage Model Parametersmentioning

confidence: 97%

Quantifying moisture damage at mastic–aggregate interface

Tarefder

2013

International Journal of Pavement Engineering

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“…More details on the computational scheme can be found in Kringos (2007) and Kringos et al (2007bKringos et al ( , 2008b.…”

Section: Moisture Flow and Molecular Diffusionmentioning

confidence: 99%

Physical and mechanical moisture susceptibility of asphaltic mixtures

Kringos

Scarpas

2008

International Journal of Solids and Structures

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Moisture has for a long time been recognized as a serious contributor to premature degradation of asphaltic pavements. Many studies have been performed to collect, describe and measure the moisture susceptibility of asphaltic mixes. Most of these aimed at a comparative measure of moisture damage, either via visual observations from field data or comparative laboratory tests. The research presented in this paper is part of an ongoing research effort to move away from such comparative or empirical measures of moisture induced damage of asphaltic materials and develop a fundamental approach via a comprehensive energy based computational framework. Such a framework would enable realistic predictions and time assessment of the failure pattern occurring in an asphaltic pavement under the given environmental and traffic loading, which could be rutting, cracking, raveling or any combination or manifestation thereof. The paper discusses the fundamental moisture induced damage parameters and demonstrates the developed model.

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“…Adhesive damage was accounted for via the cohesive zone model, where cohesive elements were inserted along the aggregate-mastic interface. A study in 2008 [24,23] incorporated both physical and mechanical damage mechanisms associated with moisture transport in a finite element code called RoAM (Raveling of Asphalt Mixes). The models developed in the works of Caro was extended to a continuum damage mechanics-based framework to model the detrimental effect of moisture on the response of particulate composite materials [37,36].…”

Section: Moisture Damage Micromechanical Modelsmentioning

confidence: 99%