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Probabilistic modeling of flexible pavement overlay performance and performance-related pay adjustment
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Wang, Zilong. Probabilistic modeling of flexible pavement overlay performance and performance-related pay adjustment. Retrieved from https://doi.org/doi:10.7282/T32J6F6K

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TitleProbabilistic modeling of flexible pavement overlay performance and performance-related pay adjustment
NameWang, Zilong (author); WANG, HAO (chair); Rutgers University; Graduate School - New Brunswick
Date Created2016
Other Date2016-10 (degree)
SubjectCivil and Environmental Engineering, Pavements--Overlays, Pavement design
Extent1 online resource (xiii, 149 p. : ill.)
DescriptionThe performance of pavement plays a critical role in maintenance, rehabilitation and reconstruction (MR&R) for highway agencies. Reliable and accurate estimation of pavement performance can be instrumental in prioritization of the limited resources and funding in highway agencies. This dissertation presented a rational development of performance-related pay adjustments framework with deterministic and probabilistic models of pavement performance, with application to in-place air void contents and international roughness index (IRI) of asphalt pavements. The analyses were performed based on the quality assurance data collected from construction database and pavement performance data extracted from pavement management system. Performance-related pay adjustments were formulated using life-cycle cost analysis (LCCA) considering two different scenarios of maintenance strategy and the variations of pavement overlay life. Comparison was made between the calculated performance-related pay adjustments and the pay adjustments currently used by highway agency. The similarity and dissimilarity were discussed and recommendations were provided based on the analysis results. The results indicate that there are unneglectable variations in the model parameters for estimating the expected pavement life due to deviations in acceptance quality characteristics. This implies that addressing the variations in pavement performance modeling is a critical issue in deriving performance-related pay adjustments. Probabilistic results show that the Bayesian approach with Markov chain Monte Carlo (MCMC) methods can capture unobserved variations in pavement condition data and relate the quality measure to the expected pavement life with satisfactory goodness of fit. The probabilistic modeling results reflect the need to consider the level of reliability in decision making of pay adjustments. The pavement overlay performance after minor and major rehabilitation was evaluated considering the effect of pre-overlay condition. Through deterministic LCCA, optimal rehabilitation strategy can be recommended based on pre-overlay condition. Probability index, a risk related factor was proposed based on probabilistic LCCA and demonstrated its merit compared to the result from deterministic analysis. It can quantitatively show the risk of choosing inappropriate rehabilitation treatment under different scenarios for decision maker. The methodology can be implemented into PMS and reduce the failure risk of roadway network.
NotePh.D.
NoteIncludes bibliographical references
Noteby Zilong Wang
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T32J6F6K
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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