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Reinforced concrete structures undergo various degradation processes, mainly rebars corrosion. Recently, magnetic non-destructive tests have been used to study the steel components used in concrete structures. This paper proposes a non-destructive method, based on the electromagnetic induction technique, capable of identifying corrosion in steel bars. Therefore, through signals obtained from sinusoidal and triangular waveform excitations, magnetic hysteresis loops were determined in steel bars of 5.0, 6.3, 8.0, 10.0, 12.5 and 16.0 mm diameters at 0, 10 and 20 mm depths. Magnetic tests were performed in uncorroded steel bars and in bars that have been exposed to corrosion by the salt spray test. The magnetic responses, remanence, coercivity and the maximum values of hysteresis curves obtained were organised into classes for uncorroded and corroded bars of each steel diameter, totalling 12 classes. A statistical analysis of the experimental results has been performed, and a detailed analysis of the data has also been performed by using the pattern classification techniques, namely, principal component analysis, Karhunen–Loéve transformation, Gaussian classifier and artificial neural network. The results show that the investigated method has great potential of becoming an electromagnetic non-destructive technique for the identification of corroded steel bars in concrete structures. [ABSTRACT FROM AUTHOR]

Carbon steel is an important component in reinforced concrete structures. These composite materials are susceptible to various degradation processes, especially the corrosion of steel bars. Since they play an important role in the stability of reinforced concrete structures, these structures require periodic inspections to verify their physical conditions. Therefore, a nondestructive testing technique is proposed that is based on electromagnetic induction, which is capable of discriminating the diameters of steel bars. This technique was simulated with the COMSOL Multiphysics software and experimentally tested in the laboratory. Magnetic hysteresis loops were obtained for steel bars with 5.0, 6.3, 8.0, and 10.0 mm diameters at 0, 10, and 20 mm depths for sinusoidal and triangular excitation fields. The results obtained from the hysteresis loops for the maximum values of the field, remanence, and coercivity were processed by principal component analysis, Karhunen–Loéve transformation, Gaussian classifier, and artificial neural network. The confusion matrices produced by these pattern recognition techniques were analyzed, and the results show that the proposed method can distinguish different steel bar diameters, but is limited to a maximum depth. [ABSTRACT FROM AUTHOR]