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The cost-efficacy control of maintenance operations in developing countries has become critical to the infrastructure asset management after highway construction. To effectively manage numerous projects annually with limited resources, it is necessary to reasonably estimate costs during the process of making maintenance project selection decisions. This study outlines the modeling of case-based reasoning (CBR) estimation that compares and retrieves the most similar instance across the case library. Four CBR approaches were presented and assessed in terms of their mean absolute prediction error rates. The resulting model demonstrates the ability of estimating the pavement maintenance project costs with the satisfactory accuracy at the early stages.