A new AI-powered framework combining ground penetrating radar with deep learning is revolutionizing how engineers assess concrete structures, delivering rapid and highly accurate rebar detection without causing damage.
Research: Integrated ground penetrating radar and deep learning approach for rebar diameter classification in concrete elements. Image Credit: Andrew Angelov / Shutterstock
With the development of civil and infrastructure engineering, the inspection and evaluation of reinforced concrete quality have become increasingly crucial. Traditional evaluation methods for reinforced rebar in concrete elements are primarily destructive, which can damage the building structure and have limitations in terms of efficiency and accuracy. However, the integration of non-destructive testing techniques and artificial intelligence has provided new solutions for rebar detection. The application of deep learning models in improving the accuracy and efficiency of rebar diameter classification still requires in-depth exploration.
Research Collaboration at Cairo University
Therefore, Mostafa KHEDR, Mahmoud METAWIE, and Mohamed MARZOUK from the Faculty of Engineering, Cairo University, Egypt, have jointly conducted research entitled "Integrated Ground Penetrating Radar and Deep Learning Approach for Rebar Diameter Classification in Concrete Elements."
Proposed Framework and Deep Learning Integration
This study proposes a framework that adopts non-destructive techniques, integrating Ground Penetrating Radar (GPR) with deep learning to automate rebar detection and analysis in concrete elements. The framework consists of four stages: dataset creation, dataset processing, Steel Rebar Detection Model, and Transfer Learning. Different deep learning models (Faster R-CNN, YOLO v7, YOLO v8) are tested, and the results show that the YOLO v8 model outperforms the other two models in terms of accuracy and efficiency.
Key Findings and Accuracy Results
The selected YOLO v8 model is trained on experimental and site data, and tested on real building data. It achieves an overall accuracy of 97.2% for rebar diameters (Ø12, Ø16, Ø18, Ø20) after multiple training iterations, with high accuracy for each diameter class. Integrating GPR with deep learning can significantly enhance the accuracy and efficiency of rebar detection in structural assessments, offering substantial practical value in ensuring the structural integrity and safety of buildings.
Access the Full Study
The paper "Integrated ground penetrating radar and deep learning approach for rebar diameter classification in concrete elements" is authored by Mostafa KHEDR, Mahmoud METAWIE, and Mohamed MARZOUK. Full text of the paper: https://link.springer.com/journal/11709
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