In moderate and destructive environments, the issue of corrosion in the reinforcement of reinforced concrete structures has become a serious problem. In this paper, GPR based on digital image processing was used to monitor and estimate the degree of corrosion based on GPR technology in an intelligent, simple, and cost-effective way. The ground penetrating radar antenna at 1 GHz frequency was employed to evaluate concrete walls, roofs, and floors to demonstrate the corrosion of reinforced steel structures. Five programs were used for the analysis and interpretation as follows: RadExplorer, Easy3D, 3D Vision, Fourier Editor, ERDAS Imagine, and ArcGIS pro 2.8. The radargram images were transformed to frequency domain (Fourier) and enhanced by using a Gaussian low pass filter to remove noise, anomalies, and unwanted information. To enhance the versions of radargram's images, Inverse Fourier Transform was utilized to retransform them. To check the validity, two types of rebars were installed in the specimens. Uncorroded rebar with a 12 mm diameter was installed in the first specimen and corroded rebar of 12 mm with a 15% level of corrosion was installed in the second specimen. When scanning by GPR, the results were promising. Significant results, highest Estimated Level of Corrosion in Rebar that emerged from the analysis of GPR data were 20% in the roof, 15% in the concrete floor, and 11% in the concrete wall, respectively. Both simulation data and actual GPR field test data were used in the experiments. The Results validated the algorithm's efficiency in detecting and identifying corrosion in RC structures.
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