A Study of Damage Mode 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 the damage modes of polypropylene fiber (PPF)-reinforced recycled concrete, ten groups of specimens with coarse aggregate (CA) replacement rates of 0% and 25% and with different particle sizes, are designed and fabricated. Uniaxial compression AE tests are conducted to obtain AE parameters during the fracture process of PPF-reinforced recycled concrete. In this study, the Pearson correlation coefficient is employed to investigate the correlations among AE parameters. Then, principal component analysis (PCA) is 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 is determined based on the silhouette coefficient. Finally, the k-means clustering algorithm is introduced to perform cluster analysis on the principal components of AE signals of PPF-reinforced recycled concrete. The clustering results are compared with each other to explore the characteristics of each cluster and to identify the corresponding damage mode for each cluster. The discriminability of AE parameters with respect to damage modes is also investigated. The research findings can provide a reference for predicting the fracture mechanism of PPF-reinforced recycled concrete.