Bayesian backcalculation of pavement properties using parallel transitional Markov chain Monte Carlo

This paper presents a novel Bayesian method for backcalculation of pavement dynamic modulus, stiffness, thickness, and damping using falling weight deflectometer (FWD) data. The backcalculation procedure yields estimates and uncertainties for each pavement property of interest. As a by‐product of th...

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Veröffentlicht in:Computer-aided civil and infrastructure engineering Jg. 39; H. 13; S. 1911 - 1927
Hauptverfasser: Coletti, Keaton, Romeo, Ryan C., Davis, R. Benjamin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken Wiley Subscription Services, Inc 01.07.2024
Schlagworte:
Algorithms
Bayesian analysis
Computation
Confidence intervals
Damping
Error analysis
Markov chains
Microprocessors
Pavements
Uncertainty
ISSN:1093-9687, 1467-8667
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Zusammenfassung:This paper presents a novel Bayesian method for backcalculation of pavement dynamic modulus, stiffness, thickness, and damping using falling weight deflectometer (FWD) data. The backcalculation procedure yields estimates and uncertainties for each pavement property of interest. As a by‐product of the Bayesian procedure, information about measurement error is recovered. The Bayesian method is tested on simulated FWD backcalculations and compared with a state‐of‐the‐art trust‐region optimization algorithm, and it achieves estimation errors that are nearly an order of magnitude lower than the trust‐region solver. Confidence intervals are computed from thousands of simulated backcalculations and are shown to quantify uncertainty in estimated pavement properties. To cope with the computational expense of backcalculation, a fully parallel transitional Markov chain Monte Carlo procedure is developed. The fully parallel algorithm scales well to computation with many processor cores, and it yields up to a 50% reduction in computation time when compared to existing parallel implementations.
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ISSN:1093-9687
1467-8667
DOI:10.1111/mice.13123