Accounting for the effects of rehabilitation actions on the reliability of flexible pavements: performance modeling and optimization

摘要

A performance model and a reliability-based optimization model for flexible pavementsthat accounts for the effects of rehabilitation actions are developed. The developedperformance model can be effectively implemented in all the applications that requirethe reliability (performance) of pavements, before and after the rehabilitation actions.The response surface methodology in conjunction with Monte Carlo simulation is usedto evaluate pavement fragilities. To provide more flexibility, the parametric regressionmodel that expresses fragilities in terms of decision variables is developed. Developedfragilities are used as performance measures in a reliability-based optimization model.Three decision policies for rehabilitation actions are formulated and evaluated using agenetic algorithm. The multi-objective genetic algorithm is used for obtaining optimaltrade-off between performance and cost.To illustrate the developed model, a numerical study is presented. The developedperformance model describes well the behavior of flexible pavement before as well asafter rehabilitation actions. The sensitivity measures suggest that the reliability offlexible 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 thesubgrade stiffness. The importance measures suggest that the asphalt layer modulus atthe time of rehabilitation actions represent the principal uncertainty for the performanceafter rehabilitation actions. Statistical validation of the developed response model showsthat the response surface methodology can be efficiently used to describe pavementresponses. The results for parametric regression model indicate that the developedregression models are able to express the fragilities in terms of decision variables.Numerical illustration for optimization shows that the cost minimization and reliabilitymaximization formulations can be efficiently used in determining optimal rehabilitationpolicies. Pareto optimal solutions obtained from multi-objective genetic algorithm can beused to obtain trade-off between cost and performance and avoid possible conflictbetween two decision policies.