Optimal Selection of Upgrade and Maintenance Interventions To Minimize Life-Cycle Cost [Research Brief]

Details

• Creators:
  Abdallah, Moatassem ; Ozbek, Mehmet E. ; Ghafoori, Mahdi
• Corporate Creators:
  University of Colorado. Dept. of Civil Engineering ; Colorado State University. Dept. of Construction Management ; Mountain-Plains Consortium
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Bridges
  • Costs
  • Innovation
  • Life cycle costing
  • Maintenance
  • Mathematical models
  • Modernization
  • Optimization
  • Machine learning
• Publication/ Report Number:
  MPC-24-531
• Resource Type:
  Brief
• Geographical Coverage:
  United States
• Edition:
  Research Brief
• Corporate Publisher:
  Mountain-Plains Consortium
• 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.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/78472/dot_78472_DS1.pdf
• File Type:
  [PDF - 446.93 KB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:59a80e497e173816734739fe349be179f8eeae3f82a57bc2ad3f4a724bf8414767a75f7525c676051f194c5d1908576ddc1e3b2da7cd55370bdc56e3095930c7