Abstract
Most bridge management systems have been developed to support either network- or project-level decisions. Network-level decisions include the selection of bridges for repair while repair strategies are considered project-level decisions. This article introduces an integrated model for bridge deck repairs with detailed life cycle costs of both network-level and project-level decisions. Two evolutionary-based optimization techniques that are capable of handling large-size problems, namely genetic algorithms and shuffled frog leaping, are then applied on the model to optimize maintenance and repair decisions. Ten trial runs with different numbers of bridges were used to compare the results of both techniques. The results indicate that both techniques can be equally suitable, and that the key issue is determining the set of parameters that optimize performance. The best optimization strategy for this type of problem appears to be a year-by-year strategy coupled with the use of a preprocessing function to allocate repair funds first to critical bridges.
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