Abstract
The solution methods of multiobjective optimization have undergone constant development over the past three decades. However, the methods available to date are not particularly robust. Because of the complicated relationship between the rehabilitation cost and deterioration degree of infrastructure systems, it is difficult to find a near‐optimal solution using common optimization methods. Since genetic algorithms work with a population of points, they can capture a number of solutions simultaneously and easily incorporate the concept of Pareto optimality. In this paper a simple genetic algorithm with two additional techniques, Pareto optimality ranking and fitness sharing, is implemented for the deck rehabilitation plan of network‐level bridges, aiming to minimize the total rehabilitation cost and deterioration degree. This approach is illustrated by a simple example and then applied to a practical bridge system with a large number of bridges.
Published Version
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