Deep Learning–Based Digital Image Correlation for Fatigue Crack Characterization in Steel Structures

This proposal presents a strategic approach to improving transportation safety through the advancement of deep learning–based Digital Image Correlation (DIC) for fatigue crack characterization in steel structural components. With aging transportation infrastructure and increasing cumulative traffic loading, fatigue-related deterioration in steel bridges and related systems presents ongoing safety risks. Accurate measurement of crack-induced displacement fields is critical for reliable structural assessment and informed maintenance decisions. The primary objectives of this proposal are to advance artificial intelligence (AI)-driven DIC methods beyond the limitations of conventional correlation-based approaches by enabling sub-pixel displacement learning through synthetic data generation, incorporating physics-informed modeling of crack-induced displacement discontinuities, and supporting high-resolution analysis of large image regions without loss of spatial detail. The methodology involves grayscale synthetic speckle data generation for sub-pixel displacement learning, mechanics-based displacement field modeling using finite element simulations, and development of an attention-enhanced deep learning architecture for full-field displacement prediction. Experimental validation against commercial DIC systems will establish a transferable methodology supporting safer fatigue crack evaluation practices.

Language

• English

Project

• Status: Active
• Funding: $227,166.00
• Contract Numbers:

  69A3552348307

• 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:

  Mid-America Transportation Center

  University of Nebraska-Lincoln
  2200 Vine Street, PO Box 830851
  Lincoln, NE  United States  68583-0851
• Project Managers:

  Bruner, Britain

• Performing Organizations:

  University of Kansas, Lawrence

  Transportation Research Institute
  2117 Learned Hall, 1530 W 15th Street
  Lawrence, KS  United States  66045
• Principal Investigators:

  Li, Jian

• Start Date: 20260601
• Expected Completion Date: 20270531
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Deep learning; Fatigue cracking; Finite element method; Image analysis; Simulation; Steel structures; Structural health monitoring; Validation
• Subject Areas: Bridges and other structures; Data and Information Technology; Highways; Maintenance and Preservation;

Filing Info

• Accession Number: 01989528
• Record Type: Research project
• Source Agency: Mid-America Transportation Center
• Contract Numbers: 69A3552348307
• Files: UTC, RIP
• Created Date: May 19 2026 1:48PM