摘要: |
Use of a deterministic, dynamic program for network-level pavement management optimization is applied. Dynamic programming, a mathematical programming technique, provides a systematic procedure for determining the decision or combination of decisions that increases the overall effectiveness of resources allocated to the pavement network. Deterministic dynamic programming is applied to the optimization of network-level pavement management, and Iowa segments of Interstate 80 are used as a case study. The network model is based on data provided by the Iowa Department of Transportation (Iowa DOT) and uses the Iowa DOT's pavement performance curves for predicting pavement condition. The model decision variables are the selection of a pavement section's treatment or rehabilitation strategy and the point in time when the treatment or rehabilitation strategy and the point in time when the treatment is to be applied to the section. Although the model is flexible and may consider several objectives and constraints, it is applied with a cost minimization objective while pavements are constrained to minimum performance levels. I-80 pavement data were provided by the Iowa DOT. In addition, a complete construction project listing for I-80 between 1987 and 1992 was used for comparing the treatment strategies selected by the model with those actually scheduled by Iowa DOT engineers. Although strategies selected by the model and the Iowa DOT engineers are likely to be different, a correlation between the two would tend to validate the results of the model. However, the model should make better decisions than the decisions made with engineering judgment. The dynamic program that performs the network optimization is written in FORTRAN 77. When the results of the computer model were compared with the actual construction project data, in almost 35% of the pavement sections, the treatment or rehabilitation strategy and implementation time selected by the optimization model match the Iowa DOT strategy and time. For 40% of the pavement sections, the treatment strategy selected by the optimization matched the one selected by Iowa DOT engineers, but the timing was different. The remaining sections showed some inconsistency in the data and the decision-making process. The network optimization model, if implemented, adds to the Iowa DOT's flexibility, consistency, speed in decision making, and ability to forecast the implications of specific decisions, changes in cost structure, changes in assumptions, or changes in resource limitations. |