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Elastic Wave Application for Damage Detection in Concrete Slab with GFRP Reinforcement
Dominika Ziaja1, Michał Jurek1, Agnieszka Wiater2
1Department of Structural Mechanics, Rzeszow University of Technology, ul. Poznańska 2, 35-084 Rzeszów, Poland.
Materials (Basel, Switzerland)
|December 11, 2022
Summary
This study monitors GFRP-reinforced concrete using elastic waves and digital image correlation. An artificial neural network accurately detects structural damage by analyzing wave signals during loading.
Area of Science:
- Civil Engineering
- Materials Science
- Structural Health Monitoring
Background:
- Glass Fiber Reinforced Polymer (GFRP) concrete members require effective condition-monitoring techniques.
- Elastic wave propagation offers a non-destructive method for assessing structural integrity.
- Existing methods struggle with complex crack patterns in concrete structures.
Purpose of the Study:
- To implement elastic wave propagation for condition-monitoring of GFRP-reinforced concrete deck slabs.
- To investigate the use of Digital Image Correlation (DIC) and piezoelectric sensors for damage detection.
- To apply an Artificial Neural Network (ANN) for accurate classification of structural conditions.
Main Methods:
- A C30/37 concrete deck slab specimen was subjected to three-point bending.
- Digital Image Correlation (DIC) system captured strain fields during loading.
- Lead-zirconate-titanate (PZT) sensors recorded elastic wave propagation signals.
- Crack-opening measurements were performed alongside wave analysis.
Main Results:
- Elastic wave signals showed changes in shape and amplitude due to crack formation and propagation.
- These signal changes were dependent on sensor location and not always straightforward to interpret.
- An Artificial Neural Network (ANN) successfully classified the structural condition with 100% accuracy.
- The ANN distinguished between loaded and unloaded states of the concrete element.
Conclusions:
- Elastic wave propagation combined with ANN is a promising technique for GFRP-reinforced concrete structural health monitoring.
- DIC and PZT sensors provide valuable data for damage assessment.
- ANNs overcome limitations of simple parameter analysis for detecting structural damage like cracking.
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