Acoustic Emission from Concrete: Critical Slowing Down Analysis and Compressive Strength Prediction

Abstract

This work pursues two primary aims: identifying the precursory point by the CSD analysis of AE series and using acoustic emission parameters to predict the compressive strength of concrete utilizing the artificial neural network (ANN), extreme learning machine (ELM), and support vector machine (SVM). The concrete specimens with a water-cement ratio of 0.45 and 0.55 were tested for compressive strength, and the failure process of concrete was monitored by acoustic emission. The results demonstrate that the advantage of variance was being intuitive, robust, and less affected by the window. The fluctuation and abrupt increase of the variance can be regarded as the critical point and the formation of the main failure surface, which provided warning information for preventing catastrophic failures. The precursory point determined by the variance was 80%–90% of the ultimate strength, consistent with the improved b value (Ib). Then, an approach to predict the compressive strength of concrete was proposed to predict the compressive strength of concrete utilizing the ANN, ELM, and SVM. The results of the ELM were more outstanding than those of the ANN and SVM. The results suggest that it was possible to predict the compressive strength of concrete with a small number of samples using the AE parameters.