You are not logged in.

Condition assessment of timber utility poles based on a hierarchical data fusion model

Yu, Yang, Dackermann, Ulrike, Li, Jianchun and Subhani, Mahbube 2016, Condition assessment of timber utility poles based on a hierarchical data fusion model, Journal of computing in civil engineering, vol. 30, no. 5, pp. 1-13, doi: 10.1061/(ASCE)CP.1943-5487.0000563.

Attached Files
Name Description MIMEType Size Downloads

Title Condition assessment of timber utility poles based on a hierarchical data fusion model
Author(s) Yu, Yang
Dackermann, Ulrike
Li, Jianchun
Subhani, MahbubeORCID iD for Subhani, Mahbube orcid.org/0000-0001-9565-3271
Journal name Journal of computing in civil engineering
Volume number 30
Issue number 5
Start page 1
End page 13
Total pages 13
Publisher American Society of Civil Engineers
Place of publication Reston, Va.
Publication date 2016-01-27
ISSN 0887-3801
1943-5487
Keyword(s) condition assessment
timber poles
hierarchical fusion
support vector machine
Dempster-Shafer (D-S)
evidence theory
Non-destructive testing
Summary This paper proposes a novel hierarchical data fusion technique for the non-destructive testing (NDT) and condition assessment of timber utility poles. The new method analyzes stress wave data from multisensor and multiexcitation guided wave testing using a hierarchical data fusion model consisting of feature extraction, data compression, pattern recognition, and decision fusion algorithms. The researchers validate the proposed technique using guided wave tests of a sample of in situ timber poles. The actual health states of these poles are known from autopsies conducted after the testing, forming a ground-truth for supervised classification. In the proposed method, a data fusion level extracts the main features from the sampled stress wave signals using power spectrum density (PSD) estimation, wavelet packet transform (WPT), and empirical mode decomposition (EMD). These features are then compiled to a feature vector via real-number encoding and sent to the next level for further processing. Principal component analysis (PCA) is also adopted for feature compression and to minimize information redundancy and noise interference. In the feature fusion level, two classifiers based on support vector machine (SVM) are applied to sensor separated data of the two excitation types and the pole condition is identified. In the decision making fusion level, the Dempster–Shafer (D-S) evidence theory is employed to integrate the results from the individual sensors obtaining a final decision. The results of the in situ timber pole testing show that the proposed hierarchical data fusion model was able to distinguish between healthy and faulty poles, demonstrating the effectiveness of the new method.
Language eng
DOI 10.1061/(ASCE)CP.1943-5487.0000563
Field of Research 090506 Structural Engineering
Socio Economic Objective 870201 Civil Construction Design
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, ASCE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083316

Document type: Journal Article
Collection: School of Engineering
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 91 Abstract Views, 0 File Downloads  -  Detailed Statistics
Created: Fri, 06 May 2016, 15:51:38 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.