Maintenance Optimization System to Maximize Performance of Bridges within Available Budget

This research focuses on developing an advanced Maintenance Optimization System to enhance the operational efficiency of bridges within available budget. The research is vital due to the poor performance of bridges, as reflected in infrastructure reports by the American Society of Civil Engineers and aims to address the delays and inadequacies in current bridge maintenance strategies through a data-driven approach. The proposed system will leverage machine learning (ML) to predict the conditions and maintenance costs of bridge components, forming the basis for a novel optimization model that schedules maintenance tasks effectively. This dual approach ensures that limited resources are utilized in the most impactful way, extending the lifespan of bridge infrastructure while adhering to budget limitations. Outcomes include the creation of ML models capable of forecasting bridge conditions accurately and an optimization model that strategically schedules maintenance. These tools are expected to transform maintenance planning from a reactive to a proactive process, enhancing safety and extending the operational life of bridges.

• Record URL:
  https://www.ctips.org/projects/details.php?id=627

Language

• English

Project

• Status: Active
• Funding: $120000
• Contract Numbers:

  69A3552348308

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Center for Transformative Infrastructure Preservation and Sustainability

  North Dakota State University
  Fargo, ND  United States  58108
• Project Managers:

  Tolliver, Denver

• Performing Organizations:

  University of Colorado Denver

  Department of Civil Engineering
  1200 Larimer Street, Room 3037
  Denver, CO  United States  80217
• Principal Investigators:

  Abdallah, Moatassem

• Start Date: 20240817
• Expected Completion Date: 20260816
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program
• Source Data: CTIPS-029

Subject/Index Terms

• TRT Terms: Bridge management systems; Cost effectiveness; Machine learning; Maintenance management; Optimization
• Subject Areas: Bridges and other structures; Data and Information Technology; Highways; Maintenance and Preservation; Planning and Forecasting;

Filing Info

• Accession Number: 01929382
• Record Type: Research project
• Source Agency: Center for Transformative Infrastructure Preservation and Sustainability
• Contract Numbers: 69A3552348308
• Files: UTC, RIP
• Created Date: Aug 31 2024 11:15AM