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Microcracking in concrete refers to the tiny cracks that can form within the material even before any external load is applied. These microcracks typically occur at the interface between the coarse aggregate and the hydrated cement paste, often as a result of differential volume changes prompted by variations in stress-strain behavior, as well as thermal and moisture movement. Initially, these microcracks remain stable and do not grow substantially until the concrete is stressed to about 30...
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Detecting Internal Defects in FRP-Reinforced Concrete Structures through the Integration of Infrared Thermography and

Pengfei Pan1,2, Rongpeng Zhang3, Yi Zhang4

  • 1Xinhua College, Ningxia University, Yinchuan 750021, China.

Materials (Basel, Switzerland)
|July 13, 2024
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Summary

This study uses infrared thermography and Mask R-CNN deep learning for detecting hidden defects in Fiber-Reinforced Polymer concrete structures. The combined approach achieves over 96% accuracy in identifying damage, enhancing structural health monitoring.

Keywords:
FRPMask RCNNinfrared thermographyinterfacial damage detection

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Area of Science:

  • Materials Science
  • Civil Engineering
  • Computer Science

Background:

  • Structural health monitoring (SHM) is crucial for infrastructure.
  • Identifying hidden defects in Fiber-Reinforced Polymer (FRP)-reinforced concrete is challenging.
  • Traditional methods often lack precision and automation.

Purpose of the Study:

  • To develop an automated method for detecting and segmenting hidden defects in FRP-concrete structures.
  • To integrate infrared thermography (IRT) with deep learning for enhanced defect detection.
  • To evaluate the performance of the Mask R-CNN model for this application.

Main Methods:

  • Utilized a dual RGB and thermal camera system for data acquisition.
  • Performed semantic segmentation annotation on captured image data.
  • Trained a Mask R-CNN deep learning model using fused RGB and thermal images.
  • Conducted 5-fold cross-validation to assess model performance.

Main Results:

  • Achieved an average accuracy of 96.28% in defect detection and segmentation.
  • Demonstrated high performance metrics: specificity (96.78%), precision (96.42%), and recall (96.91%).
  • Obtained an average F1-score of 96.78%, indicating effective damage assessment.

Conclusions:

  • The synergistic integration of IRT and deep learning (Mask R-CNN) offers a powerful tool for SHM.
  • This automated approach significantly enhances the precision and efficiency of inspecting critical infrastructure.
  • The developed method is vital for ensuring the integrity and longevity of FRP-concrete structures.