Human-AI Collaborative Bridge Inspection for Advanced Damage Detection and Defect Quantification

The aging concrete bridge infrastructure across the U.S. poses increasing risks to safety and reliability, amplifying the urgency to modernize inspection processes. Traditional manual inspection methods are labor-intensive, subjective, and inconsistent, creating barriers to effective infrastructure management. This research proposes an innovative human-AI collaborative framework designed to enhance the durability, resiliency, and technological capabilities of bridge inspections. The project integrates advanced artificial intelligence (AI) and computer vision technologies to automate damage detection and defect quantification. To ensure reliability and trustworthiness, the AI system incorporates rigorous uncertainty quantification methods, increasing transparency and inspector confidence. An active learning component strategically facilitates human-AI collaboration, enabling inspectors to focus on the most critical inspection cases identified by the AI, significantly reducing manual workloads. Additionally, engineering domain expertise, particularly in structural reliability and risk assessment, will inform and prioritize inspection processes within the AI framework. The outcomes of this project will substantially improve the accuracy, efficiency, and reliability of bridge inspections, promoting safer, longer-lasting, and more resilient transportation infrastructure. Anticipated benefits include reduced inspection costs, enhanced public safety, and a proactive approach to infrastructure management that significantly extends service life and supports sustainability.

Language

• English

Project

• Status: Active
• Funding: $301,194.00
• Contract Numbers:

  69A3552344813

  69A3552348318

• 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:

  National Center for Infrastructure Transformation

  Prairie View A&M University
  Prairie View, TX  United States  77446
• Performing Organizations:

  Texas A&M University, College Station

  Zachry Department of Civil Engineering
  3136 TAMU
  College Station, TX  United States  77843-3136
• Principal Investigators:

  Liang, Xiao

  Duan, Chang

• Start Date: 20260201
• Expected Completion Date: 20270131
• Actual Completion Date: 20270131
• USDOT Program: University Transportation Centers
• Source Data: 03-27-TAMU

Subject/Index Terms

• TRT Terms: Artificial intelligence; Bridge construction; Machine learning; Risk assessment; Structural health monitoring
• Subject Areas: Bridges and other structures; Data and Information Technology; Maintenance and Preservation; Safety and Human Factors;

Filing Info

• Accession Number: 02001920
• Record Type: Research project
• Source Agency: National Center for Infrastructure Transformation
• Contract Numbers: 69A3552344813, 69A3552348318
• Files: UTC, RIP
• Created Date: Sep 10 2026 9:04AM