Please use this identifier to cite or link to this item:
Scopus Web of ScienceĀ® Altmetric
Type: Conference paper
Title: Automated monitoring of bonding materials' properties in complex structures using machine learning
Author: Chlingaryan, A.
Melkoumian, N.
Citation: Proceedings of the Tenth IASTED International Conference on Artificial Intelligence and Applications / M. H. Hamza (ed.): pp. 489-493
Publisher: ACTA Press
Publisher Place: USA
Issue Date: 2010
ISBN: 9780889868175
Conference Name: IASTED International Conference on Artificial Intelligence and Applications (10th : 2010 : Innsbruck, Austria)
Statement of
A. Chlingaryan and N.S. Melkoumian
Abstract: A method is proposed for automated monitoring the properties of bonding materials in complex structured. An acoustic source is used to generate waves in the structure which are then registered by a sensor located on the other side of the bonding interface. The influence of the bonding material on the onset time is used to predict the elastic modulus of the bonding material. The proposed method is based on statistical supervised machine learning. The correlation between the onset time and the elastic properties of the bonding material are modelled employing Gaussian processes. The proposed approach is cheap to implement, can automatically clean the noise in the dataset and with small training dataset can produce reliable non-linear probabilistic model for predicting the properties of the bonding material using the onset time of the acoustic waves.
Keywords: Machine Learning
Acoustic Emission
Complex Structure
Rights: Copyright status unknown
DOI: 10.2316/p.2010.674-155
Published version:
Appears in Collections:Aurora harvest
Civil and Environmental Engineering publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.