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

K. Kaklis, O. Saubi, R. Jamisola, Z. Agioutantis

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

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.

Original languageEnglish
Pages (from-to)251-258
Number of pages8
JournalProcedia Structural Integrity
Volume33
Issue numberC
DOIs
StatePublished - 2021
Event26th International Conference on Fracture and Structural Integrity, IGF26 2021 - Turin, Italy
Duration: May 26 2021May 28 2021

Bibliographical note

Publisher Copyright:
© 2021 The Authors. Published by Elsevier B.V.

Keywords

  • Acoustic emission signals
  • Artificial neural nteworks
  • Failure index
  • Three-point bending test

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering
  • General Materials Science
  • Civil and Structural Engineering

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