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 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.

Automated summary

This paper presents a novel Bayesian method for backcalculation of pavement properties using FWD data, which provides estimates and uncertainties for each property along with information about measurement error. The method achieves lower estimation errors compared to a state-of-the-art optimization algorithm. Confidence intervals are computed from simulated backcalculations to quantify uncertainty. A fully parallel transitional Markov chain Monte Carlo procedure is developed to cope with the computational expense of backcalculation, resulting in a significant reduction in computation time.