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Machine learning analysis of features extracted from time–frequency domain of ultrasonic testing results for wood material assessment

journal contribution
posted on 2023-05-21, 14:47 authored by Mousavi, M, Gandomi, AH, Damien HollowayDamien Holloway, Berry, A, Chen, F
In this paper, a systematic machine learning strategy is proposed to classify wood properties based on a contact ultrasonic testing results. As such, several aspects of the wood material including the type of wood (hardwood or softwood), the direction of an ultrasonic test with respect to the growth rings of the wood, and, whether the wood is damaged or intact are investigated. As a pre-processor, the Variational Mode Decomposition technique is applied to the nonlinearly modulated ultrasonic signals, and the centre frequencies of the decomposition results are taken as features for Machine learning Algorithms (MLAs). Then with each of the MLAs, hyperparameter settings were optimised and technical aspects of the feature engineering are discussed. Best results were achieved using Ensemble classifiers, SVM, and KNN using three features/decomposition. To explore the physics behind the nonlinear problem, the relative false discovery rate obtained from the confusion matrix associated with applying the MLAs is proposed as a metric. We show that different features are capable of exploring different aspects of the problem better. The techniques of this paper can be applied to quality assessment of wood materials. The paper also demonstrates the capability of MLAs in exploring some physics of such problems. Moreover, billets with natural imperfections harvested from the site Collie in WA, Australia are classified to demonstrate the applicability of the proposed approach in real world problems. The result of 94% 5-fold cross-validation accuracy indicates the effectiveness of the proposed approach.

History

Publication title

Construction and Building Materials

Volume

342

Issue

Part A

Article number

127761

Number

127761

Pagination

1-18

ISSN

0950-0618

Department/School

School of Engineering

Publisher

Elsevier Sci Ltd

Place of publication

The Boulevard, Langford Lane, Kidlington, Oxford, England, Oxon, Ox5 1Gb

Rights statement

© 2022 Elsevier Ltd. All rights reserved.

Repository Status

  • Restricted

Socio-economic Objectives

Timber materials

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