Author(s): Neha Vivek A
Published in: International Journal of Engineering Research & Technology
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Volume/Issue: Volume. 6 - Issue. 03 , March - 2017
Monitoring the crack modes in concrete is of importance because the performance of the entire structural system is revealed. The cause and location of cracks is crucial to determine which type of crack is predominant. Assessment of failure or structural monitoring by non-destructive methods is desirable. Acoustic Emission (AE) method shows promising outcomes for monitoring cracks in concrete at real-time using some AE parameters like Rise Angle (RA) and Average Frequency (AF). This paper introduces a probabilistic approach based on Gaussian Mixture Modeling (GMM) to classify the crack modes based on the AE signals. The crack classification is checked for accuracy using Support Vector Machine (SVM) method. The algorithms are validated by an experimental study on concrete cylinders subjected to uniaxial compression.
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