United States Department of Transportation
i
Optimal Selection of Upgrade and Maintenance Interventions To Minimize Life-Cycle Cost [Research Brief]
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2024-08-01
[PDF-446.93 KB]
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Edition:Research Brief
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Abstract:This study presents two novel models. The first focuses on bridges and uses machine learning techniques to predict the condition of concrete bridge elements based on the National Bridge Inventory (NBI) and National Bridge Elements (NBE) databases. The model uses binary linear programming to identify the optimal selection of maintenance interventions and their timing to maximize bridge performance. The model’s primary contributions are the development of a novel system that integrates machine learning techniques and linear programming, predicting bridge element conditions based on NBE’s health index metric, and generating long-term maintenance plans to maximize the performance of bridges within available budgets. The second model focuses on buildings and proposes a computationally efficient model for identifying optimal upgrade and maintenance interventions to minimize the equivalent annual operation and maintenance cost (EAOMC) while complying with specified annual budgets and building operational performance.
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