Comparison of two evolutionary algorithms for optimization of bridge deck repairs

Prioritization of bridge decks for maintenance and repair purposes involves decisions related to project-level repair strategies as well as network-level selection of bridges that bring the highest return on the repair budget. These decisions, however, represent complex optimization problems that traditional optimization techniques are often unable to solve. This article introduces an integrated model for bridge deck repairs with detailed life cycle costs of 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. Results of both techniques are compared on case study problems with different numbers of bridges. Based on the results, the benefits of the bridge deck management system are illustrated along with various strategies to improve optimization performance.