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Fiber Reinforced Concrete01:22

Fiber Reinforced Concrete

Fiber-reinforced concrete significantly enhances the structural and nonstructural properties of traditional concrete by incorporating fibers like steel, glass, and polymers. These fibers, varying from natural ones such as sisal and cellulose to manufactured ones like polypropylene and Kevlar, are mixed into hydraulic cement with aggregates. Steel fibers, often preferred for their robustness, contribute to improved ductility, toughness, and post-cracking performance. The concrete is classified...
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A New Multi-Modal Data Fusion Framework for Delamination Detection in Concrete Bridge Decks.

Maria Rashidi1,2, Shayan Ghazimoghadam3, Vahid Mousavi1,2

  • 1Centre for Infrastructure Engineering (CIE), Western Sydney University, Penrith, NSW 2751, Australia.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

A new Physics-Enhanced Multi-Modal Fusion (PE-MMF) framework effectively combines Ground-Penetrating Radar (GPR) and Infrared Thermography (IRT) data. This advanced sensor fusion significantly improves the detection of delamination in concrete bridge decks, enhancing structural safety.

Keywords:
bridge deck delaminationdefect detectionground-penetrating radar (GPR)infrared thermography (IRT)multi-modal data fusionsensor fusionstructural health monitoring (SHM)

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Area of Science:

  • Civil Engineering
  • Structural Health Monitoring
  • Non-Destructive Testing

Background:

  • Bridge decks degrade due to environmental exposure, traffic, and aging, leading to delamination.
  • Subsurface concrete delamination and steel corrosion compromise structural integrity and public safety.
  • Existing non-destructive evaluation techniques like Ground-Penetrating Radar (GPR) and Infrared Thermography (IRT) have limitations in data fusion.

Purpose of the Study:

  • To introduce a Physics-Enhanced Multi-Modal Fusion (PE-MMF) framework for improved delamination detection in reinforced concrete bridge decks.
  • To address the challenges of integrating GPR and IRT data due to differences in sensing principles and resolution.
  • To enhance the generalization capability of delamination detection across different bridge structures.

Main Methods:

  • Developed a PE-MMF framework utilizing transfer learning, cross-modal attention, and gated fusion.
  • Integrated a systematic feature selection protocol for identifying physically consistent indicators.
  • Trained and validated the framework on the publicly available SDNET2021 dataset with co-registered GPR and IRT measurements.

Main Results:

  • Achieved substantial performance improvements in delamination detection.
  • Demonstrated average F1-score gains of up to 55% over IRT-based methods and 25% over GPR-based methods.
  • Confirmed superior generalization capability of the multi-modal approach compared to single-modality methods.

Conclusions:

  • The PE-MMF framework offers a scalable and data-efficient decision-support tool for infrastructure monitoring.
  • Deep learning-based sensor fusion can effectively prioritize areas for detailed physical investigation.
  • The study highlights the potential of integrated GPR and IRT for robust delamination detection in bridge decks.