Intellibridge:AI-Powered Precision In Bridge Maintenance Optimization

Bridges perform the most important roles as part of transportation networks, growing opportunities for interconnection and economic development. Yet, due to limited budgets and the difficulties associated with determining how structures will decay over time, keeping these essential structures in tip-top shape poses significant challenges. These conventional maintenance strategies involve periodic inspection and query-based scheduling, which results in a lack of precision that can lead to delays, poor resource allocation, and wasted time. To overcome these issues, this project will develop an intelligent system called IntelliBridge to transform the way to plan bridge maintenance. The proposed system makes use of advanced machine learning (ML) algorithms that predict the future state of bridge elements, perform cost analysis and give the best maintenance interventions that are under budget, and identify any inefficiencies in the existing strategies. Utilizing historical data from the National Bridge Inventory (NBI) and National Bridge Elements (NBE), IntelliBridge will enable actionable insights that support data-driven decision-making to deliver concerted maintenance interventions that are cost-effective, timely, and performance-driven. Implementing this AI-based solution will provide transport agencies with an effective and strategic mechanism to increase bridges' life, safety, and efficiency.

• Record URL:
  • https://abc-utc.fiu.edu/intellibridgeai-powered-precision-in-bridge-maintenance-optimization/

Language

• English

Project

• Status: Active
• Funding: $87,000.00
• 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:

  Florida A&M University, Tallahassee

  404 Foote/Hilyer
  Tallahassee, FL  United States  32307
• Principal Investigators:

  Guo, Qianwen

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

Subject/Index Terms

• TRT Terms: Bridge management systems; Decision support systems; Machine learning
• Subject Areas: Bridges and other structures; Highways; Maintenance and Preservation; Planning and Forecasting;

Filing Info

• Accession Number: 01973317
• 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 4:42PM