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Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation
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Determining the Fracture Process Zone Length and Mode I Stress Intensity Factor in Concrete Structures via
Seong-Kyum Kim1, Ho Geun Shin2, Suman Timilsina2
1Department of Civil Engineering, Kumoh National Institute of Technology, 61 Daehak-ro, Gumi-si, Gyeongsangbuk-do 39177, Korea.
Sensors (Basel, Switzerland)
|February 29, 2020
Summary
Mechanoluminescent (ML) technology visualizes concrete cracks and fracture zones for structural health monitoring. This novel approach measures crack length and calculates stress intensity factors, enhancing structural integrity assessment.
Area of Science:
- Materials Science
- Civil Engineering
- Structural Health Monitoring
Background:
- Structural Health Monitoring Systems (SHMS) require advanced techniques for assessing material integrity.
- Mechanoluminescent (ML) technology offers a novel approach using stress/strain sensing nanoparticles.
- Existing methods have limitations in visualizing and quantifying fracture phenomena in materials like concrete.
Purpose of the Study:
- To demonstrate the potential of ML technology for measuring the fracture process zone (FPZ) length in concrete.
- To evaluate ML technology's capability in calculating the stress intensity factor (SIF) for concrete structures.
- To apply ML technology for characterizing the structural integrity of concrete using SHMS.
Main Methods:
- Utilized a single-edge notched bend (SENB) test on concrete specimens.
- Employed ML technology comprising nanoparticles, a digital imaging system, and image processing.
- Developed image segmentation techniques (histogram-based and skeletonization) for ML image analysis.
Main Results:
- ML technology successfully visualized the FPZ and cracks in concrete.
- The length of the FPZ and cracks were accurately measured using ML imaging.
- ML technology demonstrated potential in determining fracture toughness and calculating SIF.
Conclusions:
- ML technology is a promising tool for non-destructive evaluation of concrete structures.
- The developed image processing methods facilitate precise measurement of fracture-related parameters.
- ML technology is applicable for advanced Structural Health Monitoring Systems (SHMS).
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