Integrated ground penetrating radar and deep learning approach for rebar diameter classification in concrete elements

Abstract

Traditional evaluation of reinforced rebar in concrete elements involves destructive methods that may harm the building. This paper introduces a framework that adopts non-destructive techniques to classify rebar in reinforced concrete elements. The framework integrates Ground Penetrating Radar (GPR) with deep learning to automate rebar detection and analysis in concrete elements. The framework consists of four stages: Data sets Creation, Data sets Processing, Steel Rebar Detection Model, and Transfer Learning. Different deep learning models are tested to choose the highest-performing model. The YOLO v8 model outperforms Faster R-CNN and YOLO v7. The selected YOLO v8 model is trained on experimental and site data and then tested on real data from the building to validate the model’s accuracy and ability to classify rebar diameter. Integrating GPR with deep learning can potentially improve the accuracy and efficiency of rebar detection in structural assessments.

Graphical abstract

Keywords

reinforced concrete inspection / non-destructive testing / GPR / rebar diameter classification / deep learning / YOLO v8

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