Evaluation of project selection techniques for pavement network maintenance and repair.
Wood, Thomas L.
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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.
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