Search

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.

Reinforced concrete structures are susceptible to the appearance of corrosion in their reinforcement. Therefore, in order to obtain reliable and accurate information about the physical conditions of these reinforcements, as they play an important role in the stability of concrete structures, periodic inspections are required preferably non-destructive. Therefore, the present study proposes a non-destructive test, based on the electromagnetic induction technique, capable of discriminating the diameters of the steel rebars and identifying the presence of corrosion in them. This technique was simulated in the COMSOL Multiphysics program and performed in the laboratory. To simulate the corrosion in the reinforcements, the salt spray test was used. By means of signals obtained from sine or triangular waveform excitation magnetic field, hysteresis cycles were determined on rebar diameters of 5, 6.3, 8, 10, 12.5 and 16 mm, at depths of 0, 10 and 20 mm and with and without corrosion. From the hysteresis cycles, the maximum values of the fields, the remnant and the coercivity were obtained and analyzed by the mean and standard deviation and, they have also been organized in classes and classified by the pattern recognition techniques Principal Component Analysis, transform Karhunen-Loéve, Gaussian classifier and Artificial Neural Network implemented in the MATLAB program. The results showed that the proposed method can distinguish different diameters of reinforcement and has potential in the qualitative analysis of corrosion, although limited to a maximum depth. As estruturas de concreto armado são suscetíveis ao aparecimento da corrosão em suas armaduras. Então, para obter informações confiáveis e precisas sobre as condições físicas dessas armaduras, pois elas desempenham um papel importante na estabilidade das estruturas de concreto, inspeções periódicas são requeridas, de preferência, não destrutivas. Portanto, o presente estudo propõe um ensaio não destrutivo, baseado na técnica de indução eletromagnética, capaz de discriminar os diâmetros de armaduras e identificar a presença da corrosão nelas. Essa técnica foi simulada no programa COMSOL Multiphysics e realizada em laboratório. Para simular a corrosão nas armaduras foi utilizado o ensaio de névoa salina. Por meio de sinais elétricos obtidos com um campo magnético de excitação de forma de onda senoidal e triangular, ciclos de histerese foram determinados em vergalhões de diâmetros de 5, 6,3, 8, 10, 12,5 e 16 mm, em profundidades de 0, 10 e 20 mm e com e sem corrosão. A partir dos ciclos de histerese, os valores máximos dos campos, a remanência e a coercividade obtidos foram analisados pela média e desvio padrão, bem como organizados em classes e classificados pelas técnicas de reconhecimento de padrões como Análise de Componentes Principais, transformada de Karhunen-Loéve, classificador Gaussiano e Rede Neural Artificial implementados no programa MATLAB. Os resultados mostraram que o método proposto pode distinguir diferentes diâmetros de armaduras e tem potencial na análise qualitativa da corrosão, embora limitados a uma profundidade máxima.