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A preliminary application of a machine learning model for the prediction of the load variation in three-point bending tests based on acoustic emission signals

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dc.contributor.author Kaklis, Kostas
dc.contributor.author Saubi, Onalethata
dc.contributor.author Jamisola, Rodrigo S
dc.contributor.author Agioutantis, Zach G
dc.date.accessioned 2022-06-28T10:25:38Z
dc.date.available 2022-06-28T10:25:38Z
dc.date.issued 2021
dc.identifier.citation Kaklis, K. et al. (2021) A preliminary application of a machine learning model for the prediction of the load variation in three-point bending tests based on acoustic emission signals. Procedia Structural Integrity, 33, 251-258. https://doi.org/10.1016/j.prostr.2021.10.031. en_US
dc.identifier.issn 2452-3216
dc.identifier.uri http://repository.biust.ac.bw/handle/123456789/464
dc.description.abstract The load variation during three-point bending (TPB) tests on prismatic Nestos (Greece) marble specimens instrumented by piezoelectric sensors is predicted using acoustic emission (AE) signals. The slope of the cumulative amplitude vs the predicted load curve is potentially useful for determining the forthcoming specimen failure as well as the indirect tensile strength of the material. The optimum artificial neural networks (ANN) model was selected based on a comparison of different machine learning techniques with respect to the root mean square error (RMSE) and the coefficient of determination (CoD). The top three best-performing techniques were decision trees, random forests and artificial neural networks. Results show that decision trees and random forests have a coefficient of determination of 98.8% and 99.2%, respectively. The artificial neural network has an accuracy of 99.6% with a root mean square error of 0.022. The comparison of results with experimental data shows that ANNs can potentially be utilized to predict rock behavior and/or establish a failure index. en_US
dc.language.iso en en_US
dc.publisher Elsevier B.V. en_US
dc.subject Artificial Neural Networks (ANN) en_US
dc.subject Machine Learning en_US
dc.subject Three-point bending test en_US
dc.title A preliminary application of a machine learning model for the prediction of the load variation in three-point bending tests based on acoustic emission signals en_US
dc.description.level phd en_US
dc.description.accessibility unrestricted en_US
dc.description.department mge en_US


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