Classification of cracking sources of different engineering media via
machine learning
Abstract
Complex civil structures require the cooperation of many building
materials. However, it is difficult to accurately monitor and evaluate
the inner damage states of various material systems. Based on a
convolutional neural network (CNN) and the acoustic emission (AE)
time-frequency diagram, we used the transfer learning method for
classifying the AE signals of different materials under external loads.
The results show the CNN model can accurately classify cracks that come
from different materials based on AE signals. The recognition accuracy
can reach 90% just by re-training the full connection layer of the
pre-trained model, and its accuracy can reach 97% after re-training the
top 2 convolutional layers of this model. A realization of cracking
source identification mainly depends on the differences in mineral
particles in materials. This work highlights the great potential for
real-time and quantitative monitoring of the health status of composite
civil structures.