Analyzing acoustic emission data to identify cracking modes in cement paste using an artificial neural network
Abstract
This research is focused on the identification of cracking mechanisms for cement paste using acoustic emission data, recorded from compression and notched four-point bending tests. A procedure is developed for analyzing the data by employing an agglomerative hierarchical clustering method, an artificial neural network, and a ray-tracing source location algorithm. An agglomerative hierarchical clustering method is utilized to cluster the AE data from a compression test using frequency-dependent features. A neural network is trained using the compression test data and applied to the AE data emitted during the four-point bending test. The clustered data from the four-point bending test is localized using a ray-tracing algorithm. Based on the occurrence and locations of the clustered events and signal feature analyses, potential cracking mechanisms are identified and assigned.
- Authors:
-
- University of South Carolina, Columbia, SC (United States)
- University of South Carolina, Columbia, SC (United States); Northern Technical University, Nineveh (Iraq)
- University of South Carolina, Columbia, SC (United States); Al-Mustaqbal University College, Babylon (Iraq)
- Publication Date:
- Research Org.:
- Clemson Univ., SC (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1851654
- Alternate Identifier(s):
- OSTI ID: 1778421
- Grant/Contract Number:
- SC0012530
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Construction and Building Materials
- Additional Journal Information:
- Journal Volume: 267; Journal Issue: C; Journal ID: ISSN 0950-0618
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; acoustic emission; artificial neural network; cement paste; crack mechanism; ray-tracing algorithm; unsupervised pattern recognition
Citation Formats
Soltangharaei, V., Anay, R., Assi, L., Bayat, M., Rose, J. R., and Ziehl, P. Analyzing acoustic emission data to identify cracking modes in cement paste using an artificial neural network. United States: N. p., 2021.
Web. doi:10.1016/j.conbuildmat.2020.121047.
Soltangharaei, V., Anay, R., Assi, L., Bayat, M., Rose, J. R., & Ziehl, P. Analyzing acoustic emission data to identify cracking modes in cement paste using an artificial neural network. United States. https://doi.org/10.1016/j.conbuildmat.2020.121047
Soltangharaei, V., Anay, R., Assi, L., Bayat, M., Rose, J. R., and Ziehl, P. Fri .
"Analyzing acoustic emission data to identify cracking modes in cement paste using an artificial neural network". United States. https://doi.org/10.1016/j.conbuildmat.2020.121047. https://www.osti.gov/servlets/purl/1851654.
@article{osti_1851654,
title = {Analyzing acoustic emission data to identify cracking modes in cement paste using an artificial neural network},
author = {Soltangharaei, V. and Anay, R. and Assi, L. and Bayat, M. and Rose, J. R. and Ziehl, P.},
abstractNote = {This research is focused on the identification of cracking mechanisms for cement paste using acoustic emission data, recorded from compression and notched four-point bending tests. A procedure is developed for analyzing the data by employing an agglomerative hierarchical clustering method, an artificial neural network, and a ray-tracing source location algorithm. An agglomerative hierarchical clustering method is utilized to cluster the AE data from a compression test using frequency-dependent features. A neural network is trained using the compression test data and applied to the AE data emitted during the four-point bending test. The clustered data from the four-point bending test is localized using a ray-tracing algorithm. Based on the occurrence and locations of the clustered events and signal feature analyses, potential cracking mechanisms are identified and assigned.},
doi = {10.1016/j.conbuildmat.2020.121047},
journal = {Construction and Building Materials},
number = C,
volume = 267,
place = {United States},
year = {Fri Oct 08 00:00:00 EDT 2021},
month = {Fri Oct 08 00:00:00 EDT 2021}
}
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