A performance model and a reliability-based optimization model for flexible pavements
that accounts for the effects of rehabilitation actions are developed. The developed
performance model can be effectively implemented in all the applications that require
the reliability (performance) of pavements, before and after the rehabilitation actions.
The response surface methodology in conjunction with Monte Carlo simulation is used
to evaluate pavement fragilities. To provide more flexibility, the parametric regression
model that expresses fragilities in terms of decision variables is developed. Developed
fragilities are used as performance measures in a reliability-based optimization model.
Three decision policies for rehabilitation actions are formulated and evaluated using a
genetic algorithm. The multi-objective genetic algorithm is used for obtaining optimal
trade-off between performance and cost.
To illustrate the developed model, a numerical study is presented. The developed
performance model describes well the behavior of flexible pavement before as well as
after rehabilitation actions. The sensitivity measures suggest that the reliability of
flexible pavements before and after rehabilitation actions can effectively be improved by providing an asphalt layer as thick as possible in the initial design and improving the
subgrade stiffness. The importance measures suggest that the asphalt layer modulus at
the time of rehabilitation actions represent the principal uncertainty for the performance
after rehabilitation actions. Statistical validation of the developed response model shows
that the response surface methodology can be efficiently used to describe pavement
responses. The results for parametric regression model indicate that the developed
regression models are able to express the fragilities in terms of decision variables.
Numerical illustration for optimization shows that the cost minimization and reliability
maximization formulations can be efficiently used in determining optimal rehabilitation
policies. Pareto optimal solutions obtained from multi-objective genetic algorithm can be
used to obtain trade-off between cost and performance and avoid possible conflict
between two decision policies.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2921 |
| Date | 15 May 2009 |
| Creators | Deshpande, Vighnesh Prakash |
| Contributors | Damnjanovic, Ivan, Gardoni, Paolo |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | electronic, application/pdf, born digital |
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