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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| Vydáno v: | Computer-aided civil and infrastructure engineering Ročník 39; číslo 13; s. 1911 - 1927 |
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| Jazyk: | angličtina |
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Hoboken
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01.07.2024
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| ISSN: | 1093-9687, 1467-8667 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Coletti, Keaton Davis, R. Benjamin Romeo, Ryan C. |
| Author_xml | – sequence: 1 givenname: Keaton surname: Coletti fullname: Coletti, Keaton organization: University of Georgia – sequence: 2 givenname: Ryan C. surname: Romeo fullname: Romeo, Ryan C. organization: The MathWorks, Inc – sequence: 3 givenname: R. Benjamin surname: Davis fullname: Davis, R. Benjamin email: ben.davis@uga.edu organization: University of Georgia |
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| CitedBy_id | crossref_primary_10_3390_en18174516 crossref_primary_10_1134_S1061830925700111 crossref_primary_10_3390_coatings14080922 crossref_primary_10_1080_10298436_2025_2487617 crossref_primary_10_1016_j_trgeo_2025_101715 crossref_primary_10_1007_s12206_025_0807_z crossref_primary_10_1111_mice_13325 crossref_primary_10_1111_mice_13346 |
| Cites_doi | 10.1016/j.compstruc.2022.106935 10.3390/su13168831 10.1111/mice.12696 10.1007/s11440-019-00847-1 10.1111/mice.12898 10.1007/978-1-84882-310-5 10.1016/j.advengsoft.2005.10.001 10.1080/10298436.2013.782401 10.1111/j.1467-8667.2012.00762.x 10.3141/2005-10 10.1139/L10-127 10.1016/j.cma.2015.01.015 10.3850/978-981-14-8593-0_4374-cd 10.1111/mice.12987 10.1061/JPEODX.0000044 10.1080/1029843021000067836 10.1061/(ASCE)0733-947X(2006)132:1(76) 10.1177/0361198118821337 10.1145/321941.321951 10.1177/0361198118823500 10.1061/(ASCE)EM.1943-7889.0001066 10.1080/10298436.2021.1883016 10.1111/mice.13014 10.1016/j.conbuildmat.2010.04.040 10.1016/j.conbuildmat.2017.07.034 10.1061/(ASCE)0733-9399(2007)133:7(816) 10.1139/L07-083 10.1093/biomet/57.1.97 10.1080/10298430500150981 10.1016/j.ymssp.2021.108471 10.1111/mice.12624 10.1111/mice.13010 10.1080/17415977.2016.1191073 10.1002/9780470824566 10.3390/app13021192 10.1214/aoap/1034625254 10.1016/j.conbuildmat.2019.117792 10.23919/USNC/URSI49741.2020.9321602 |
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| Snippet | This paper presents a novel Bayesian method for backcalculation of pavement dynamic modulus, stiffness, thickness, and damping using falling weight... |
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| StartPage | 1911 |
| SubjectTerms | Algorithms Bayesian analysis Computation Confidence intervals Damping Error analysis Markov chains Microprocessors Pavements Uncertainty |
| Title | Bayesian backcalculation of pavement properties using parallel transitional Markov chain Monte Carlo |
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| Volume | 39 |
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