Deep learning-based classification of damage-induced acoustic emission signals in UHPC

Abstract

Acoustic emission (AE) is advantageous in studying and monitoring the damage state of UHPC, and its performance can be further improved if various damage-induced AE signals are classified accurately. The deep learning method is promising to enhance the classification accuracy, but it is limited by the absence of the corresponding AE dataset. In this study, fiber pullout tests and direct tension tests (DTTs) were performed to explore the time–frequency features of various damage-induced AE signals in UHPC. The AE sources can be classified into five categories: matrix cracking, fiber debonding, fiber sliding, fiber scraping, and matrix spalling. A corresponding AE dataset was established after manual pre-labeling, pseudo-labeling with transfer learning, and mix-up data augmenting. Several lightweight convolutional neural networks (CNNs) were trained from scratch based on the dataset, with modified time–frequency spectrograms as data input. The performances of CNN-based classification models are superior to traditional shallow-learning methods. The popular lightweight CNN structure of ResNet18 achieves the highest overall accuracy of 93.94% among adopted CNN structures such as GoogleNet, ResNet18, EfficientNet-b0, and MobileNetV2. The AE signal classification results of DTTs show that fiber-pullout damage and matrix cracking occur intensively at the strain-hardening phase and increase with fiber content. That suggests the increasing fiber bridging and multi-crack propagation in the uniaxial tension of UHPC with higher fiber content.