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.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2020-03-03




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Bayesian Uncertainty Quantification for Planar Impact Crashes via Markov Chain Monte Carlo Simulation

    Davis, Gary A. / Mudgal, Abhisek | British Library Conference Proceedings | 2016



    Bayesian uncertainty quantification for planar impact crashes via Markov Chain Monte Carlo simulation

    Davis,G.A. / Mudgal,A. / Univ.of Minnesota,US et al. | Automotive engineering | 2016



    Simplified Viscoelastic Continuum Damage Model as Platform for Asphalt Concrete Fatigue Analysis

    Underwood, B. Shane / Baek, Cheolmin / Kim, Y. Richard | Transportation Research Record | 2012