Optimizing Strategies in Bridge Asset Management Through Generating Interactive Reinforcement Learning (GI-RL) Methods

The proposed study aims to create a Generating Interactive Reinforcement Learning (GI-RL) framework to optimize bridge maintenance strategies. Bridge asset management is a critical aspect of infrastructure maintenance, particularly for bridge owners responsible for ensuring the safety and functionality of numerous bridges. Traditional methods often involve reactive maintenance strategies, which can lead to suboptimal outcomes. This study proposes exploring the application of reinforcement learning (RL) to optimize bridge management strategies, focusing on strategic decision-making under imperfect information. RL is a subfield of artificial intelligence (AI) that focuses on training agents or stakeholders to make sequences of decisions. It rewards bridge owners for making beneficial choices, such as performing preventive maintenance and/or reducing user/driver time, thereby promoting Accelerated Bridge Construction (ABC).

• Record URL:
  • https://abc-utc.fiu.edu/optimizing-strategies-in-bridge-asset-management-through-generating-interactive-reinforcement-learning-gi-rl-methods/

Language

• English

Project

• Status: Active
• Funding: $69,000.00
• Contract Numbers:

  69A3552348322

• Sponsor Organizations:

  Innovative Bridge Technologies/Accelerated Bridge Construction University Transportation Center (IBT/ABC-UTC)

  Florida International University
  Miami, FL  United States 

  Office of the Assistant Secretary for Research and Technology

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

  University of Georgia Research Foundation (UGARF)

  310 E. Campus Rd.,
  Athens, GA  United States  30602
• Principal Investigators:

  Chorzepa, Mi

• Start Date: 20250101
• Expected Completion Date: 0
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Asset management; Bridge management systems; Machine learning
• Subject Areas: Bridges and other structures; Highways; Maintenance and Preservation;

Filing Info

• Accession Number: 01973314
• Record Type: Research project
• Source Agency: Innovative Bridge Technologies/Accelerated Bridge Construction University Transportation Center (IBT/ABC-UTC)
• Contract Numbers: 69A3552348322
• Files: UTC, RIP
• Created Date: Dec 2 2025 4:33PM