US 12,422,407 B2
Short-term AE monitoring to identifying ASR progression in concrete structures
Paul Ziehl, Irmo, SC (US); and Vafa Soltangharaei, Columbia, SC (US)
Assigned to University of South Carolina, Columbia, SC (US)
Filed by University of South Carolina, Columbia, SC (US)
Filed on Jul. 8, 2022, as Appl. No. 17/860,171.
Claims priority of provisional application 63/242,635, filed on Sep. 10, 2021.
Prior Publication US 2023/0087465 A1, Mar. 23, 2023
Int. Cl. G01N 29/06 (2006.01); G01N 29/12 (2006.01); G01N 29/14 (2006.01); G01N 33/38 (2006.01)
CPC G01N 29/0654 (2013.01) [G01N 29/12 (2013.01); G01N 29/14 (2013.01); G01N 33/383 (2013.01)] 18 Claims
OG exemplary drawing
 
1. An acoustic emission method for monitoring structural health of concrete comprising:
affixing at least one acoustic emission sensor to a concrete structure;
obtaining at least one acoustic emission waveform from the concrete structure;
employing at least one deep learning algorithm to analyze the at least one acoustic emission waveform to determine a condition of the concrete structure with respect to at least one Alkali-silica reaction progress wherein the at least one deep learning algorithm attributes the Alkali-silica reaction process to an Alkali-silica expansion range and an Alkali-silica reaction damage phase is determined for the concrete structure based on the at least one acoustic emission waveform from the concrete structure;
wherein the at least one deep learning algorithm includes at least one convolutional neural network and at least one stacked autoencoder:
wherein at least one heterogeneous ensemble learning network, including the at least one convolutional neural network, and at least one random forest classifies the Alkali-silica reaction damage phase; and
wherein the method is nondestructive to the concrete structure being monitored.