Quantification of Uncertainty in the Master Curves of Viscoelastic Properties of Asphalt Concrete

Rema, Aswathy; Swamy, Aravind Krishna

doi:10.1520/acem20170049

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…According to Van Dijk et al’s research, fatigue life mainly depends on the dissipation modulus and energy consumption during the stress–strain cycle [ 34 ]. The mechanical properties of the asphalt mixture depend on how long the load is applied and the temperature when the pressure is applied [ 35 ]. Its complex modulus is composed of a storage modulus and loss modulus.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Strength and Fatigue Life of Rubber Asphalt Mixture

Yuan

Peng

et al. 2020

Materials

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Strength and fatigue life are essential parameters of pavement structure design. To accurately determine the pavement structure resistance of rubber asphalt mixture, the strength tests at various temperatures, loading rate, and fatigue tests at different stress levels were conducted in this research. Based on the proposed experiments, the change law of rubber asphalt mixture strength with different temperatures and loading rates was revealed. The phenomenological fatigue equation of rubber asphalt mixture was established. The genetic algorithm optimized backpropagation neural network (GA-BPNN) is highly reliable for optimizing production processes in civil engineering, and it has a remarkable application effect. A GA-BPNN strength and fatigue life prediction model was created in this study. The reliability of the prediction model was verified through experiments. The results showed that the rubber asphalt mixture strength decreases and increases with the increase of temperature and loading rate, respectively. The goodness of fit of the rubber asphalt mixture strength and fatigue life prediction model based on the GA-BPNN could reach 0.989 and 0.998, respectively. The indicators of the fatigue life prediction model are superior to the conventional phenomenological fatigue equation model. The GA-BPNN provides an effective method for predicting the rubber asphalt mixture strength and fatigue life, which significantly improves the accuracy of the resistance design of the rubber asphalt pavement structure.

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Section: Introductionmentioning

confidence: 99%

Investigation of Strength and Fatigue Life of Rubber Asphalt Mixture

Yuan

Peng

et al. 2020

Materials

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“…Slowik characterized the measurement uncertainty for the stiffness modulus of the asphalt mixture using the indirect tension method ( 9 ). Rema discussed the uncertainty in interconversion methods from dynamic modulus test results to the creep compliance and relaxation modulus ( 10 ). Gudipudi studied the reliability of fatigue predictions in the simplified viscoelastic continuum damage model (S-VECD) given the variation in the input parameters ( 11 ).…”

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confidence: 99%

Uncertainty Quantification of Simplified Viscoelastic Continuum Damage Fatigue Model using the Bayesian Inference-Based Markov Chain Monte Carlo Method

Ding

Wang

Gulzar

et al. 2020

Transportation Research Record: Journal of the Transportation R

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The simplified viscoelastic continuum damage model (S-VECD) has been widely accepted as a computationally efficient and a rigorous mechanistic model to predict the fatigue resistance of asphalt concrete. It operates in a deterministic framework, but in actual practice, there are multiple sources of uncertainty such as specimen preparation errors and measurement errors which need to be probabilistically characterized. In this study, a Bayesian inference-based Markov Chain Monte Carlo method is used to quantify the uncertainty in the S-VECD model. The dynamic modulus and cyclic fatigue test data from 32 specimens are used for parameter estimation and predictive envelope calculation of the dynamic modulus, damage characterization and failure criterion model. These parameter distributions are then propagated to quantify the uncertainty in fatigue prediction. The predictive envelope for each model is further used to analyze the decrease in variance with the increase in the number of replicates. Finally, the proposed methodology is implemented to compare three asphalt concrete mixtures from standard testing. The major findings of this study are: (1) the parameters in the dynamic modulus and damage characterization model have relatively strong correlation which indicates the necessity of Bayesian techniques; (2) the uncertainty of the damage characteristic curve for a single specimen propagated from parameter uncertainties of the dynamic modulus model is negligible compared to the difference in the replicates; (3) four replicates of the cyclic fatigue test are recommended considering the balance between the uncertainty of fatigue prediction and the testing efficiency; and (4) more replicates are needed to confidently detect the difference between different mixtures if their fatigue performance is close.

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“…This uncertainty increases significantly as the loading rate becomes slower or as the temperature increases. Rema and Swamy studied various quantification methods to address the scatter in D ( t ) and E ( t ) ( 10 ). It was shown that AC can possess a significant scatter which is dependent on temperature and testing frequency.…”

mentioning

confidence: 99%

Effect of Asphalt Mixture Components on the Uncertainty in Dynamic Modulus Mastercurves

Kassem

Chehab

Najjar

2020

Transportation Research Record: Journal of the Transportation R

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Practitioners and researchers in the paving industry have highlighted the importance of the adoption of reliability-based pavement design. The goal of developing reliable pavements with optimum performance over their design life has become a key factor to be considered during both pavement design and construction processes. This requires the adoption of statistical and probabilistic-based analyses for the formulation of the properties and behavior of pavement materials. Thus, many researchers worked on the quantification and modeling of the uncertainty caused by the inherent variability in pavement materials in general and that of asphalt concrete (AC) in particular. The dynamic modulus (| E*|), a fundamental property for mechanistic-empirical and purely mechanistic pavement designs, has been proven to have a significant level of uncertainty that is dependent on climatic and traffic loading conditions. The main objective of this study is to investigate the effect of the AC mixture properties and components on the uncertainty in the | E*| mastercurve. This objective is achieved by conducting an experimental program incorporating four different mixtures having the same material sources but different binder types and gradations. Monte Carlo simulations are used to model the uncertainty of | E*| for each of these mixtures. The paper shows that the uncertainty is dependent on mixture type, as the presence of larger nominal maximum aggregate size, modified binder, or additive can increase the uncertainty in the | E*| mastercurve, especially at high temperatures or slow loading rates. The uncertainty is proven to be material related and not imposed by the testing instrumentation.

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Quantification of Uncertainty in the Master Curves of Viscoelastic Properties of Asphalt Concrete

Cited by 8 publications

References 24 publications

Investigation of Strength and Fatigue Life of Rubber Asphalt Mixture

Investigation of Strength and Fatigue Life of Rubber Asphalt Mixture

Uncertainty Quantification of Simplified Viscoelastic Continuum Damage Fatigue Model using the Bayesian Inference-Based Markov Chain Monte Carlo Method

Effect of Asphalt Mixture Components on the Uncertainty in Dynamic Modulus Mastercurves

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