Detection of source locations in RC columns using machine learning with acoustic emission data

Detecting and monitoring the damage locations of in-service reinforced concrete (RC) structures is difficult as they exhibit more complex geometries and consist of composite materials. In this study, a new method for detecting the source location of acoustic emission (AE) signals in RC structures using machine learning was introduced and tested on an in-service RC column. An impulse response test was conducted using an AE testing system to gather data regarding the source locations. Meanwhile, the source locations on the test column were generated through manual hits. The corresponding AE data were collected using a AE data acquisition system. The significant AE parameters were determined using neighborhood component analysis for feature selection to obtain a dataset for machine learning. Further, a support vector machine classifier was adopted to classify the source locations of the columns for three sensor groups. Furthermore, the source locations were predicted using feedforward backpropagation neural networks. This study validated the detectability of AE signals in RC columns and proposed a novel detection method, which can be employed to perform continuous nondestructive tests on in-service RC columns.