Impact damage judgment of polyurea resin coated RC slab using hitting sound based on machine learning

Abstract

The purpose of this study is to confirm the applicability of the Local Outlier Factor (LOF) method in non-destructive inspection using impact sounds to detect damage caused by projectile impacts on RC spec- imen coated with polyurea resin, aimed at preventing delamination and the scattering of scabbing fragments. In experiments targeting RC specimen damaged by projectile impacts, the impact sound spectra during hammer strikes on the resin-coated layer are used as input data for LOF-based damage assessment. The study confirms the feasibility of damage detection using LOF and examines the influence of the number and acquisition position of training data on the assessment results.