This study presents a novel approach for accurately classifying rebar diameters in reinforced concrete elements by integrating ground penetrating radar (GPR) data with advanced deep learning techniques. Traditional GPR-based rebar detection often faces limitations in resolution and noise, especially in complex or dense reinforcement scenarios. To address these challenges, this research employs data fusion strategies and a convolutional neural network (CNN) model to enhance the interpretability and precision of GPR signals. Experimental validation on concrete samples with varying rebar diameters demonstrates significant improvements in classification accuracy compared to conventional methods. The proposed framework offers a non-destructive, efficient, and scalable solution for structural health monitoring and assessment in civil engineering applications.

Rebar classification, ground penetrating radar (GPR), deep learning

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