A Data-Driven Probabilistic Framework Using Computational Fluid Dynamics, Artificial Intelligence, and Underwater Robotics for Predicting Bridge Scour

Scour is the leading cause of bridge failure in the U.S. Traditional methods of scour prediction rely on empirical formulas that require flow information at bridge location, which is scarce and hard to obtain, and scour inspections often rely on human divers which is costly, involve safety risks, and often lack the precision and adaptability needed for the complex coastal and estuarine environments. This project will develop a novel approach for scour prediction and mapping that addresses these shortcomings by integrating computational fluid dynamics (CFD) and machine learning (ML) for real-time prediction of flow velocity and scour, and underwater robots powered by first-principles and machine learning-driven perception to provide high fidelity maps of the scour beyond capabilities of human divers. Project tasks include: (1) identify bridges vulnerable to scour and characterize their environmental conditions and structural features, (2) development of a CFD model for scour of a vulnerable bridge, and deployment of a current meter on the channel bed close to the bridge to measure currents that drive scour, and use of its data to validate the CFD model, (3) run the CFD model for a variety water level conditions, spanning regular tides to intense storms to generate training data for a ML model that will calculate scour in real time given real-time current measurements at operational gauges, (4) Develop a probabilistic framework for scour prediction using the trained ML model, (5) deployment of low-cost underwater autonomous vehicles to map a scour patch pre- and post-storm, and using the data to validate the scour models. The framework in this proof-of-concept project can be scaled up to numerous bridges across any region in future studies. By combining novel simulation and in-situ data acquisition techniques, this project aims to enable risk-informed decision making for management of bridge infrastructure.

• Record URL:
  • https://abc-utc.fiu.edu/a-data-driven-probabilistic-framework-using-computational-fluid-dynamics-artificial-intelligence-and-underwater-robotics-for-predicting-bridge-scour/

Language

• English

Project

• Status: Active
• Funding: $70,000.00
• 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 International University

  Civil and Environmental Engineering
  10555 W. Flagler Street, EC 3680
  Miami, FL  United States  33174
• Principal Investigators:

  Tahvildari, Navid

• Start Date: 20260101
• Expected Completion Date: 20270630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Artificial intelligence; Bridges; Fluid dynamics; Machine learning; Predictive models; Robots; Scour
• Subject Areas: Bridges and other structures; Data and Information Technology; Highways; Maintenance and Preservation; Planning and Forecasting;

Filing Info

• Accession Number: 01998615
• 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: Aug 7 2026 8:37AM