This paper proposes a nondestructive evaluation method based on deep learning using combined ground-penetrating radar (GPR) and electromagnetic induction (EMI) data for autonomic and accurate estimation of the cover thickness and diameter of reinforcement bars. A real-time object detection algorithm—You Only Look Once–version 3 (YOLO v3)—is adopted to automatically identify the reinforcement bar reflected signals from radargrams, with which the range of the cover thickness is roughly predicted. Subsequently, EMI data, accompanied with the cover thickness range, are imported to a one-dimensional convolutional neural network (1D CNN), pretrained by calibrated EMI and GPR data, to simultaneously estimate the cover thickness and reinforcement bar diameter. Testing with the on-site GPR data shows that YOLO v3 is superior to Single Shot Multibox Detector method in GPR hyperbolic signal identification. Testing of 1D CNN with the EMI and GPR data collected in an in-house sand pit experiment shows that the estimation accuracy of the cover thickness and reinforcement bar diameter is, respectively, 96.8% and 90.3% with a permissible error of 1 mm. Further, an experiment with concrete specimens demonstrates that among the 22 estimated values (including the reinforcement bar diameter and cover thickness), there are 17 values accurately estimated, while the inaccurately estimated values have an error up to 2 mm. The experimental results show that the proposed method can autonomically evaluate the reinforcement bar diameter and cover thickness with a high accuracy.

中文翻译：

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使用探地雷达和电磁感应数据对钢筋进行基于深度学习的无损评估

本文提出了一种基于深度学习的无损评估方法，该方法使用结合探地雷达 (GPR) 和电磁感应 (EMI) 数据来自主准确地估计钢筋的覆盖层厚度和直径。采用实时目标检测算法You Only Look Once-version 3 (YOLO v3)，自动识别雷达图上的钢筋反射信号，粗略预测覆盖层厚度范围。随后，将伴随覆盖层厚度范围的 EMI 数据导入一维卷积神经网络 (1D CNN)，通过校准的 EMI 和 GPR 数据进行预训练，以同时估计覆盖层厚度和钢筋直径。通过现场探地雷达数据测试表明，YOLO v3 在探地雷达双曲线信号识别方面优于 Single Shot Multibox Detector 方法。使用在内部沙坑实验中收集的 EMI 和 GPR 数据对 1D CNN 进行测试表明，覆盖层厚度和钢筋直径的估计精度分别为 96.8% 和 90.3%，允许误差为 1 mm。此外，混凝土试件实验表明，在 22 个估算值（包括钢筋直径和覆盖层厚度）中，有 17 个估算值准确，而不准确估算值的误差高达 2 mm。实验结果表明，所提出的方法可以自主评估钢筋直径和覆盖层厚度，精度高。