テクノロジーの進歩に関する国際ジャーナル
オープンアクセス

概要

Limitation of the Lateral Angled Broadband Low Frequency Impact Excitation on the Non-Destructive Condition Assessment of the Timber Utility Poles

Bahram Jozi, Robin Braun, Bijan Samali, Jianchun Li and Ulrike Dackermann

Timber utility poles play a significant role in the infrastructure of Australia as well as many other countries for power distribution and communication networks. Due to the advanced age of Australia’s timber pole infrastructure, substantial efforts are undertaken on maintenance and asset management to avoid any failures of the utility lines. Nevertheless, the lack of reliable tools for assessing the condition of in-service poles seriously jeopardizes the maintenance and asset management. For instance, each year approximately 300,000 poles are replaced in the Eastern States of Australia with up to 80% of them still being in a very good condition, resulting in major waste of natural resources and money. Non-destructive testing (NDT) methods based on stress wave propagation can potentially offer simple and cost-effective tools for identifying the in-service condition of timber poles. Nonetheless, most of the currently available methods are not appropriate for condition assessment of timber poles in-service due to presence of uncertainties such as complicated material properties, environmental conditions, interaction of soil and structure, and an impact excitation type. In order to address these complexities, advanced digital signal processing methodologies are needed to be employed. Deterministic signal separation, blind signal separation, and frequency-wavenumber velocity filtering are the three groups of methodologies, which could most probably provide solutions. In this paper applicability and effectiveness of the blind signal separation methods is investigated through a numerical data obtained from of a timber pole modelled with both isotropic and orthotropic material properties. Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and K-means clustering algorithms are the blind signal separation methodologies that are employed in this research work.