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Using a Non-local Elastic Damage Model to Predict the Fatigue Life of Asphalt Pavement Structure

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Proceedings of the International Conference on Advances in Computational Mechanics 2017 (ACOME 2017)

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Abstract

Asphalt concrete is a composite material comprising aggregate, sand, mineral filler and bitumen as a binder. Although good compaction is performed during the construction, there is still relatively large discontinuity inside the material, and this will favour the appearance of micro-cracks, which decreases the performance of the material. Structural cracking resulted from repeated loading, or fatigue cracking, is a common failure mode of asphalt pavement structure, reducing the serviceability of the pavement. Owing to the present of micro-cracking, the fatigue cracking of asphalt pavement is generally modelled by using damage theory. In this paper, the authors aim to illustrate the application of an isotropic non-local elastic damage model in predicting the fatigue life of a pavement structure. A scalar D, called damage variable, is used to define the damage state at a point of the material, and the evolution of this variable at a point depends on the historic damage state as well as the present strain tensor at that point. The model parameters are determined on the basis of fatigue test results—namely, 4-point bending test. Numerical examples are presented to illustrate the ability of using damage theory to predict the damage evolution of a pavement structure as well as its service life.

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Correspondence to H. T. Tai Nguyen .

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Nguyen, H.T.T., Nguyen, N.H. (2018). Using a Non-local Elastic Damage Model to Predict the Fatigue Life of Asphalt Pavement Structure. In: Nguyen-Xuan, H., Phung-Van, P., Rabczuk, T. (eds) Proceedings of the International Conference on Advances in Computational Mechanics 2017. ACOME 2017. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-10-7149-2_4

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  • DOI: https://doi.org/10.1007/978-981-10-7149-2_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7148-5

  • Online ISBN: 978-981-10-7149-2

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