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Title |
A Study on the Prediction of Buried Rebar Thickness Using CNN Based on GPR Heatmap Image Data
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Authors |
박세환(Sehwan Park) ; 김주원(Juwon Kim) ; 김원규(Wonkyu Kim) ; 김한선(Hansun Kim) ; 박승희(Seunghee Park) |
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DOI |
https://doi.org/10.11112/jksmi.2019.23.7.66 |
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Keywords |
GPR; B-scan; 히트맵; 합성곱 신경망; 철근; 두께 Ground Penetrating Radar; B-scan; heatmap; Convolution Neural Network; Rebar Thickness |
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Abstract |
In this paper, a study was conducted on the method of using GPR data to predict rebar thickness inside a facility. As shown in the cases of poor construction, such as the use of rebars below the domestic standard and the construction of reinforcement, information on rebar thickness can be found to be essential for precision safety diagnosis of structures. For this purpose, the B-scan data of GPR was obtained by gradually increasing the diameter of rebars by making specimen. Because the B-scan data of GPR is less visible, the data was converted into the heatmap image data through migration to increase the intuition of the data. In order to compare the results of application of commonly used B-scan data and heatmap data to CNN, this study extracted areas for rebars from B-scan and heatmap data respectively to build training and validation data, and applied CNN to the deployed data. As a result, better results were obtained for the heatmap data when compared with the B-scan data. This confirms that if GPR heatmap data are used, rebar thickness can be predicted with higher accuracy than when B-scan data is used, and the possibility of predicting rebar thickness inside a facility is verified.
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