Modeling of viscoplastic rate-dependent hardening-softening behavior of hot mix asphalt in compression

Yun, Taeyoung; Kim, Y. Richard

doi:10.1007/s11043-010-9116-7

Cited by 19 publications

(4 citation statements)

References 12 publications

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“…The Perzyna form of the viscoplastic model resolves this issue and is widely used by researchers (21,(23)(24)(25)(26)(27). The researchers also propose viscoplastic models in hereditary integral forms (28)(29)(30). However, in the classical sense of plasticity theory, its stress state should vary consistently for a material to undergo hardening deformation by continuously evolving yield surface.…”

Section: Literature Reviewmentioning

confidence: 99%

System Identification Framework for Modeling of Static Creep and Dynamic Creep Behavior of Dense-Graded Asphalt Mixtures

2023

Transportation Research Record: Journal of the Transportation R

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The response of two dense-graded asphalt concrete (AC) mixtures under static and dynamic creep tests conducted in compression is modeled using the principles of the system identification framework in this study. The AC materials under static and dynamic creep loading conditions exhibit complex behavior patterns such as history dependency, plasticity, and temperature dependency. It is desired to formulate a constitutive model describing these complex phenomena. The empirical approach is outside the scope of this study. A viscoelastic continuum damage model requires individual experimentation to characterize linear and damage behavior. A viscoplastic model requires incremental deviator stress for plastic deformation. Both static and dynamic creep tests do not satisfy this condition. Therefore, the “system identification” approach is used in this study. Two methods of system identification approach—that is, black box and grey box modeling—are adopted to characterize static and dynamic creep behavior of AC. Of these two models under consideration, the black box models were versatile in modeling complex loading situations with a high degree of accuracy but lacked physical interpretation. While the grey box models have a physical interpretation, they are not as versatile as black box models.

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Section: Literature Reviewmentioning

confidence: 99%

System Identification Framework for Modeling of Static Creep and Dynamic Creep Behavior of Dense-Graded Asphalt Mixtures

2023

Transportation Research Record: Journal of the Transportation R

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“…However, until recently, few attempts have been made in the literature to model this distinct behavior of asphalt concrete during the cyclic loading. Recently, Yun and Kim called this effect the VP softening and attempted to model this effect by introducing another function in the yield surface that has a different evolution function during the rest period (20). However, this model is only applicable to the uniaxial compression loading scenarios and cannot effectively be applied to model the rutting performance of pavements subjected to a three-dimensional state of stress.…”

Section: Vp Hardening Relaxation Mechanismmentioning

confidence: 99%

Constitutive Modeling of Cyclic Viscoplastic Response of Asphalt Concrete

Darabi

Al‐Rub

Masad

et al. 2013

Transportation Research Record

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Rutting (permanent deformation) is the most important distress that asphalt concrete pavements are prone to at high temperatures. However, available classical viscoplastic (VP) theories are incapable of properly predicting permanent deformation of asphalt concrete materials at high temperatures subjected to cyclic loading conditions. A physically based VP hardening relaxation model is proposed to overcome this issue. This model considers the changes in the microstructure that occur during the rest period and cause the induced hardening stress to relax and recover. This mechanism affects the VP properties of asphalt concrete before and after the rest period is applied. A memory surface is introduced as the general criterion for the evolution of the hardening relaxation mechanism. The memory surface possesses a state variable memorizing the history of the VP deformation during the loading history. The proposed VP hardening relaxation model is coupled with Schapery's viscoelastic model and the classical Perzyna's VP model and is validated against extensive experimental data, including repeated creep and recovery tests at different stress levels, loading times, and rest periods. It is shown that the proposed model reasonably predicts the cyclic VP response of asphalt concrete materials at high temperatures.

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“…It has been demonstrated that the regression coefficients of the phenomenological model can be expressed as functions of viscoelastic and damage characteristics of asphalt concretes. More recently, the modeling approach has been modified to consider the viscoplastic rate-dependent hardening-softening response of bituminous mixtures under compression [18][19][20][21]. A viscoelastoplastic formulation of the constitutive approach has also been developed [22,23].…”

Section: Introductionmentioning

confidence: 99%

Stiffness Modulus and Marshall Parameters of Hot Mix Asphalts: Laboratory Data Modeling by Artificial Neural Networks Characterized by Cross-Validation

2019

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The present paper discusses the analysis and modeling of laboratory data regarding the mechanical characterization of hot mix asphalt (HMA) mixtures for road pavements, by means of artificial neural networks (ANNs). The HMAs investigated were produced using aggregate and bitumen of different types. Stiffness modulus (ITSM) and Marshall stability (MS) and quotient (MQ) were assumed as mechanical parameters to analyze and predict. The ANN modeling approach was characterized by multiple layers, the k-fold cross validation (CV) method, and the positive linear transfer function. The effectiveness of such an approach was verified in terms of the coefficients of correlation ( R ) and mean square errors; in particular, R values were within the range 0.965 – 0.919 in the training phase and 0.881 – 0.834 in the CV testing phase, depending on the predicted parameters.

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Modeling of viscoplastic rate-dependent hardening-softening behavior of hot mix asphalt in compression

Cited by 19 publications

References 12 publications

System Identification Framework for Modeling of Static Creep and Dynamic Creep Behavior of Dense-Graded Asphalt Mixtures

System Identification Framework for Modeling of Static Creep and Dynamic Creep Behavior of Dense-Graded Asphalt Mixtures

Constitutive Modeling of Cyclic Viscoplastic Response of Asphalt Concrete

Stiffness Modulus and Marshall Parameters of Hot Mix Asphalts: Laboratory Data Modeling by Artificial Neural Networks Characterized by Cross-Validation

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