A Study of Damage Modes Recognition of Polypropylene Fiber Reinforced Recycled Aggregate Concrete Based on Principal Components of Acoustic Emission Signals

Abstract

To investigate the principal components of acoustic emission (AE) signals and damage modes of polypropylene fiber (PPF) reinforced recycled concrete, ten groups of specimens with coarse aggregate (CA) replacement rates of 0% and 25% and different particle sizes were designed and fabricated. Uniaxial compression AE tests were conducted to obtain the AE parameters during the fracture process of PPF reinforced recycled concrete. In this study, the Pearson correlation coefficient was employed to investigate the correlations among AE parameters. Then, the principal component analysis (PCA) was performed on the AE signals to conduct dimensionality reduction of the multi-dimensional data. On this basis, the optimal number of clusters for the principal components of AE signals was determined based on the silhouette coefficient. Finally, the K-means clustering algorithm was introduced to perform cluster analysis on the principal components of AE signals of PPF reinforced recycled concrete. The clustering results were compared with each other to explore the characteristics of each cluster and identify the corresponding damage mode for each cluster. The discriminability of AE parameters with respect to damage modes was investigated, and the research findings can provide a reference for predicting the fracture mechanism of PPF reinforced recycled concrete.