Stochastic Identification of Linear-Viscoelastic Models of Aged and Unaged Asphalt Mixtures

Mehrez, Loujaine; Kassem, Emad; Masad, Eyad; Little, Dallas N.

doi:10.1061/(asce)mt.1943-5533.0001103

Cited by 22 publications

(15 citation statements)

References 8 publications

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“…Alavi et al (2013) proposed a new model to quantify the effects of binder aging on the visco-elastic behaviour of the asphalt mixture by evaluating carbonyl functional groups of recovered binder and continuous relaxation spectrum of the asphalt mixtures. Mehrez et al (2015) used stochastic model to recognize the linear visco-elastic master curve of asphalt mixtures subjected to different aging durations. Glover et al (2014) developed a model to predict the oxidation rate based on binder kinetics data, temperature profile of the pavement, and mixture characteristics.…”

Section: Introductionmentioning

confidence: 99%

Effects of extended short-term aging duration on asphalt binder behaviour at high temperatures

Hamzah

Omranian

2016

BJRBE

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Many factors affecting pavement performance include variations in binder composition and environmental conditions during asphalt mixture production. Hence, predicting pavement performance is a difficult task. This paper aims to investigate the effects of short term aging on binder viscosity at high temperature. In order to predict the effects of short term aging on the asphalt binder viscosity at high temperatures, a Response Surface Method was performed on the Rotational Viscometer test results. An experimental matrix was planned based on the central composite design for aging duration and test temperature. The test results showed that aging increased the binder viscosity, while increasing test temperature decreased the corresponding value. However, aging effects differ and depend on binder types, test temperatures and aging conditions. It was also found that the Response Surface Method is a fast, effective and reliable method to predict the effects of aging on binder viscosity behaviour at high temperatures.

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Section: Introductionmentioning

confidence: 99%

Effects of extended short-term aging duration on asphalt binder behaviour at high temperatures

Hamzah

Omranian

2016

BJRBE

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“…Fitzenz et al [25], Most [26], and Sarkar et al [27] investigated elastoplastic materials and thermodynamical material models to identify their model parameters. Other studies on viscoelastic models are carried out by Zhang et al [28], Mehrez et al [29], and Miles et al [30]. Further investigations on viscoelastic models to estimate a higher number of model parameters are studied by Zhao and Pelegri [31], and by Kenz et al [32].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage

Adeli

Rosić

Matthies

et al. 2020

Metals

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The state of materials and accordingly the properties of structures are changing over the period of use, which may influence the reliability and quality of the structure during its life-time. Therefore, identification of the model parameters of the system is a topic which has attracted attention in the content of structural health monitoring. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, the measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the focus is on the identification of material parameters of a viscoplastic damaging material using a stochastic simulation technique to generate artificial data which exhibit the same stochastic behavior as experimental data. It is proposed to use Bayesian inverse methods for parameter identification and therefore the model and damage parameters are identified by applying the Transitional Markov Chain Monte Carlo Method (TMCMC) and Gauss-Markov-Kalman filter (GMKF) approach. Identified parameters by using these two Bayesian approaches are compared with the true parameters in the simulation and with each other, and the efficiency of the identification methods is discussed. The aim of this study is to observe which one of the mentioned methods is more suitable and efficient to identify the model and damage parameters of a material model, as a highly non-linear model, using a limited surface displacement measurement vector and see how much information is indeed needed to estimate the parameters accurately.

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“…Fitzenz et al [24], Most [25], and Sarkar et al [26] investigated elastoplastic materials and thermodynamical material models to identify their model parameters. Other studies on viscoelastic models are carried out by Zhang et al [27], Mehrez et al [28], and Miles et al [29]. Further investigations on viscoelastic models to estimate a higher number of model parameters are studied by Zhao and Pelegri [30], and by Kenz et al [31].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Parameter Determination of a CT-Test Described by a Viscoplastic-Damage Model Considering the Model Error

Adeli

Rosić

Matthies

et al. 2020

Metals

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The state of materials and accordingly the properties of structures are changing over the period of use, which may influence the reliability and quality of the structure during its life-time. Therefore identification of the model parameters of the system is a topic which has attracted attention in the content of structural health monitoring. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, the measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the Choboche model with a damage is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behavior as experimental data. Then the model and damage parameters are identified by applying the sequential Gauss-Markov-Kalman filter (SGMKF) approach as this method is determined as the most efficient method for time consuming finite element model updating problems among filtering and random walk approaches. The parameters identified using this Bayesian approach are compared with the true parameters in the simulation, and further, the efficiency of the identification method is discussed. The aim of this study is to observe whether the mentioned method is suitable and efficient to identify the model and damage parameters of a material model, as a highly non-linear model, for a real structural specimen using a limited surface displacement measurement vector gained by Digital Image Correlation (DIC) and to see how much information is indeed needed to estimate the parameters accurately even by considering the model error and whether this approach can also practically be used for health monitoring purposes before the occurrence of severe damage and collapse.

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Stochastic Identification of Linear-Viscoelastic Models of Aged and Unaged Asphalt Mixtures

Cited by 22 publications

References 8 publications

Effects of extended short-term aging duration on asphalt binder behaviour at high temperatures

Effects of extended short-term aging duration on asphalt binder behaviour at high temperatures

Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage

Bayesian Parameter Determination of a CT-Test Described by a Viscoplastic-Damage Model Considering the Model Error

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