Identification of corrosion formation in CORTEN steel using acousto-ultrasonic approach and deep learning

Abstract

PurposeThe acousto-ultrasonic approach is used for propagating stress waves through different configurations of CORTEN steel specimens. The propagated waves are recorded and analysed by piezoelectric sensors. The purpose of the study is to study the characteristics of the CORTEN steel by analysing the propagated waves.Design/methodology/approachTo investigate the attenuation in acoustic wave propagation due to the corrosion formation in CORTEN steel specimens and to train a neural network model to classify the attenuated acoustic waves automatically.FindingsDue to the corrosion formation in CORTEN steel specimens, attenuation is observed in amplitude, energy, counts and duration of the propagated waves. When the waves are analysed in their time-frequency characteristics, attenuation is observed in their frequency and spectral energy.Originality/valueThe corrosion formation in CORTEN steel can automatically be analysed by using the acousto-ultrasonic approach and the trained deep learning neural network.