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Published on: September 29, 2019
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Assessment of Cracking Development in Concrete Precast Crane Beams Using Optical and Deep Learning Methods
1Faculty of Civil Engineering, Cracow University of Technology, ul. Warszawska 24, 31-155 Kraków, Poland.
Materials (Basel, Switzerland)
|February 26, 2025
Summary
This study introduces a new method using AI and digital image correlation to assess cracks in aged concrete crane beams. This approach enables early detection of structural damage for predictive maintenance.
Area of Science:
- Structural Engineering
- Artificial Intelligence in Infrastructure Assessment
- Non-Destructive Testing Methods
Background:
- Crane beams are critical for industrial infrastructure longevity and safety.
- Traditional crack assessment methods (LVDTs, strain gauges) have limitations.
- Aging infrastructure requires advanced structural health monitoring techniques.
Purpose of the Study:
- To present a novel methodology for evaluating the structural health of aged precast concrete crane beams.
- To integrate deep learning for crack segmentation with digital image correlation for strain analysis.
- To enable accurate, non-destructive assessment of structural deterioration.
Main Methods:
- Adaptation of the U-Net deep convolutional neural network for crack detection and segmentation.
- Application of the Digital Image Correlation (DIC) technique for measuring surface strains and displacements.
- Combined analysis of image segmentation and DIC data for comprehensive structural health evaluation.
Main Results:
- The integrated approach provides a non-destructive and detailed analysis of concrete structural elements.
- Early detection of deterioration in crane beams is facilitated, enhancing safety assessments.
- Field test results validate the effectiveness of the proposed methodology for aging infrastructure.
Conclusions:
- The novel methodology offers a significant advancement in assessing the structural integrity of aging precast crane beams.
- This integrated AI and DIC approach is a promising tool for predictive maintenance in industrial settings.
- The study highlights the potential for advanced digital techniques to ensure the safety and longevity of critical infrastructure.
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