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Praneeth_Nimmatoori MSThesis 2009.pdf (10.23 MB)

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Comparison of Several Project Level Pavement Condition Prediction Models

Nimmatoori, Praneeth
http://rave.ohiolink.edu/etdc/view?acc_num=toledo1578491583921183

Abstract Details

2009, Master of Science, University of Toledo, Civil Engineering.
Prediction of future pavement conditions is one of the important functions of pavement management systems. They are helpful in determining the rate of roadway network deterioration both at the network-level and project-level management, which forms a major part of engineering decision making and reporting. Network-level management focuses on determination and allocation of funds to maintain the pavement network above a specified operational standard and does not give importance to how the individual pavement sections deteriorate. Therefore, a survival time analysis is determined to predict the remaining service life. At the project-level, engineers make decisions on which pavement to repair, when and how to repair. Therefore, it requires more condition accuracy than network-level. The two adjustment methods proposed by Shahin (1994) and Cook and Kazakov (1987) are often used to obtain more condition prediction at the project-level. Both the Shahin and the Cook and Kazakov models take into account a family average curve in predicting deterioration of individual pavement sections. This prediction is done through the latest available condition-age point of an individual pavement section and does not consider all available data points. This study considers the most commonly used pavement condition prediction models viz. linear regression, polynomial constrained least squares, S-shape and power curve. The prediction accuracy of these four models is compared. Further the prediction accuracy of each of the four models is compared with their respective the Shahin's and the Cook's models to determine whether is it possible to further improve the prediction accuracy error for each of the four models.
Eddie Y. Chou, PhD (Committee Chair)
George J. Murnen, PhD (Committee Member)
Andrew G. Heydinger, PhD (Committee Member)
104 p.
Civil Engineering; Engineering; Transportation
Pavement Condition,Deterioration Models, Statistical Analysis, Prediction, Condition, Age, Network-level, Project-level, Pavement Condition Rating, Pavement Management System, Linear Regression, Polynomial, S-shape, Power Curve

Recommended Citations

Citations

  • Nimmatoori, P. (2009). Comparison of Several Project Level Pavement Condition Prediction Models [Master's thesis, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1578491583921183

    APA Style (7th edition)

  • Nimmatoori, Praneeth. Comparison of Several Project Level Pavement Condition Prediction Models. 2009. University of Toledo, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1578491583921183.

    MLA Style (8th edition)

  • Nimmatoori, Praneeth. "Comparison of Several Project Level Pavement Condition Prediction Models." Master's thesis, University of Toledo, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1578491583921183

    Chicago Manual of Style (17th edition)

toledo1578491583921183
159
© 2009, all rights reserved.
This open access ETD is published by University of Toledo and OhioLINK.