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Probabilistic modeling of flexible pavement overlay performance and performance-related pay adjustment
Descriptive
TitleInfo
Title
Probabilistic modeling of flexible pavement overlay performance and performance-related pay adjustment
Name
(type = personal)
NamePart
(type = family)
Wang
NamePart
(type = given)
Zilong
NamePart
(type = date)
1988-
DisplayForm
Zilong Wang
Role
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(authority = RULIB)
author
Name
(type = personal)
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WANG
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HAO
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HAO WANG
Affiliation
Advisory Committee
Role
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chair
Name
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NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
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Graduate School - New Brunswick
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school
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Text
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theses
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DateCreated
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2016
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2016-10
Place
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xx
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2016
Language
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eng
Abstract
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The 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.
Subject
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Topic
Civil and Environmental Engineering
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Rutgers University Electronic Theses and Dissertations
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ETD_7431
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electronic resource
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application/pdf
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text/xml
Extent
1 online resource (xiii, 149 p. : ill.)
Note
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Ph.D.
Note
(type = bibliography)
Includes bibliographical references
Subject
(authority = ETD-LCSH)
Topic
Pavements--Overlays
Subject
(authority = ETD-LCSH)
Topic
Pavement design
Note
(type = statement of responsibility)
by Zilong Wang
RelatedItem
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TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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NjNbRU
Identifier
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doi:10.7282/T32J6F6K
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ETD doctoral
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Rights
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The author owns the copyright to this work.
RightsHolder
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Name
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Wang
GivenName
Zilong
Role
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Type
Permission or license
DateTime
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2016-07-12 09:42:43
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Name
Zilong Wang
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Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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2016-10-31
DateTime
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2017-05-02
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after May 2nd, 2017.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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