抄録
In this paper we present an online unsupervised method based on clustering to find void-type defects in concrete structures using hammering. The dataset of sound samples is clustered in order to find the regular model for the hammering sound, which is assumed to be the non-defective sound model. The algorithm is fast and reliable enough to allow efficient diagnosis by running it each time a new sample is acquired.
