Identification of the concrete damage degree based on the principal component analysis of acoustic emission signals and neural networks

Abstract

Abstract This paper aims to improve the calculation efficiency and accuracy of concrete damage degree identification, and then to analyze the damage mechanism of concrete damage. First, the correlation analysis and principal component analysis of 15 characteristic parameters of acoustic emission signals accompanying concrete uniaxial compression and splitting damage process are performed through which the dimension is reduced into 5 non-correlated principal components. Then, based on the analysis of the relationship between each principal component and the damage and cracking mechanism of concrete, the damage degree of concrete is identified as an input variable of the BP neural network. The results show that the 5 principal components effectively eliminate redundant information and carry information on the failure mechanism of concrete damage and the damage process. Principal component analysis and the neural network are used to achieve the accurate recognition of acoustic emission parameters and the degree of concrete damage.