论文标题：Integrated ground penetrating radar and deep learning approach for rebar diameter classification in concrete elements

期刊：Frontiers of Structural and Civil Engineering

作者：Mostafa KHEDR, Mahmoud METAWIE, Mohamed MARZOUK

发表时间：21 Jan 2025

DOI：10.1007/s11709-025-1177-4

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文章亮点/Highlights

针对传统钢筋混凝土构件检测方法存在的破坏性缺陷，本文提出了一种融合探地雷达(Ground Penetrating Radar, GPR)与深度学习技术的无损检测框架。该框架包括四个阶段：数据集创建、数据集处理、钢筋检测模型和迁移学习，实现了混凝土内部钢筋的自动化识别与直径分类。

实验采用对比研究方法，系统评估了YOLO v8、Faster R-CNN及YOLO v7等主流深度学习模型的性能表现。结果表明，YOLO v8模型优于Faster R-CNN和YOLO v7模型。通过迁移学习策略，模型在实验室标准数据集与现场实测数据上均展现出优异的泛化能力。

本研究的创新点在于：1）建立了首个面向GPR数据的钢筋特征标注规范；2）开发了基于注意力机制的多尺度特征融合网络；3）验证了该方法在实际工程结构评估中的适用性。研究证实，GPR-深度学习融合技术可有效提升检测效率，为建筑结构健康监测提供了新的技术路径。

提出的框架方法论

摘要/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.

关键词/Keywords

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

引用信息/Citation Information

Mostafa KHEDR, Mahmoud METAWIE, Mohamed MARZOUK. Integrated ground penetrating radar and deep learning approach for rebar diameter classification in concrete elements. Front. Struct. Civ. Eng., 2025, 19(4): 524?540 https://doi.org/10.1007/s11709-025-1177-4

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https://doi.org/10.1007/s11709-025-1177-4

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