Pattern Recognition Method for the Source Location of Acoustic Emission Generated During the Damage of Hydraulic Concrete

Abstract

Abstract: To overcome the disadvantages of current acoustic emission (AE) source location methods, such as classical approaches based on times of arrival and artificial neural networks based on AE signal features, support vector machines (SVM)‐based models have been employed to recognise AE source regions in structures. However, in some circumstances, it seems that a more accurate positioning of AE sources is needed. This study concerns the spatial three‐dimensional (3D) positioning (i.e. coordinates) for damages in hydraulic concrete structures using the least squares SVM (LS‐SVM) regression with AE signal features. The data of artificial discrete AE sources were acquired from simulated AE events on a hydraulic concrete specimen. Various combinations of signal features were chosen to adequately excavate effective information and to obtain the multi‐output LS‐SVM regression model of the best performance. The training and testing results show that the proposed model can realise the accurate spatial 3D positioning of damages in hydraulic concrete structures in laboratory situations and reduce human factors (e.g. judgment of AE propagation velocity, etc.) in the AE source location process. Meanwhile, the work remaining in taking this idea to a practical implementation was discussed.