Analyzing acoustic emission data to identify cracking modes in cement paste using an artificial neural network

Soltangharaei, V.; Anay, R.; Assi, L.; Bayat, M.; Rose, J. R.; Ziehl, P.

doi:10.1016/j.conbuildmat.2020.121047

Analyzing acoustic emission data to identify cracking modes in cement paste using an artificial neural network

Journal Article · Fri Oct 08 00:00:00 EDT 2021 · Construction and Building Materials

DOI:https://doi.org/10.1016/j.conbuildmat.2020.121047· OSTI ID:1851654

Soltangharaei, V. ^[1]; Anay, R. ^[2]; Assi, L. ^[3]; Bayat, M. ^[4]; Rose, J. R. ^[4]; Ziehl, P. ^[4]

University of South Carolina, Columbia, SC (United States); OSTI
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)
University of South Carolina, Columbia, SC (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: Clemson University, SC (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: SC0012530

OSTI ID:: 1851654

Alternate ID(s):: OSTI ID: 1778421

Journal Information:: Construction and Building Materials, Journal Name: Construction and Building Materials Journal Issue: C Vol. 267; ISSN 0950-0618

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Related Subjects

36 MATERIALS SCIENCE
acoustic emission
artificial neural network
cement paste
crack mechanism
ray-tracing algorithm
unsupervised pattern recognition

Analyzing acoustic emission data to identify cracking modes in cement paste using an artificial neural network

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