Evaluation of project selection techniques for pavement network maintenance and repair.
Abstract
Different approaches have been suggested for determining the optimal
mix of repair projects for a pavement network. These methods range from
random selection to sophisticated mathematical optimization models.
This paper presents an analysis of several questions regarding the
effectiveness of three possible selection methods.
First, the performance of three separate single year project selection
methods on different size networks is assessed over a broad funding spectrum.
The results indicate that as funding levels increase, the benefit obtained by
different selection methods converge. In addition, as the size of the network
increases, the convergence tends to occur at progressively lower funding levels.
Second, the effect of the performance prediction models on these same
selection methods is assessed by altering the coefficients of the models to predict both faster and slower deterioration of the network. The "select sets" of
projects created by priority ranking selection and Knapsack IP selection at three
separate funding levels are compared to determine how much variation is introduced by the changes in the performance prediction. With a 30%
acceleration and deceleration of the deterioration curves, there was little change
in the optimal project set created by either method.
Finally, a modified Monte Carlo model is used to assess the general
shape of the solution space. The results suggest that the solution space is relatively flat except in the immediate vicinity of the optimum. This, in turn,
suggests that a Monte Carlo approach to this problem would require a large
number of trials to approximate the optimum. This finding conceptually supports
findings in this study and others, as well as the intuitive observation, that random
maintenance and repair strategies perform poorly compared to more rational
approaches. Since only a few sets of repair projects are near the optimum, the
chances of a random selection matching one of these near optimal project sets are relatively small.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.Collections
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