Condition Assessment of Foundation Piles and Utility Poles Based on Guided Wave Propagation Using a Network of Tactile Transducers and Support Vector Machines
Open accessAbstract
This paper presents a novel non-destructive testing and health monitoring system using a network of tactile transducers and accelerometers for the condition assessment and damage classification of foundation piles and utility poles. While in traditional pile integrity testing an impact hammer with broadband frequency excitation is typically used, the proposed testing system utilizes an innovative excitation system based on a network of tactile transducers to induce controlled narrow-band frequency stress waves. Thereby, the simultaneous excitation of multiple stress wave types and modes is avoided (or at least reduced), and targeted wave forms can be generated. The new testing system enables the testing and monitoring of foundation piles and utility poles where the top is inaccessible, making the new testing system suitable, for example, for the condition assessment of pile structures with obstructed heads and of poles with live wires. For system validation, the new system was experimentally tested on nine timber and concrete poles that were inflicted with several types of damage. The tactile transducers were excited with continuous sine wave signals of 1 kHz frequency. Support vector machines were employed together with advanced signal processing algorithms to distinguish recorded stress wave signals from pole structures with different types of damage. The results show that using fast Fourier transform signals, combined with principal component analysis as the input feature vector for support vector machine (SVM) classifiers with different kernel functions, can achieve damage classification with accuracies of 92.5% ± 7.5%.
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SVM is beneficial for pattern recognition and can be used for regression analysis.
“The technique is beneficial for pattern recognition and can be used for regression analysis [242-244].”
From Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review (2023) · cites this paper as [244]
Section 5 describes using SVM to classify pole-damage patterns, and Section 6 reports strong classification results. The paper does not discuss using SVM for regression analysis; searches for “regression analysis” and “support vector regression” found no supporting passage. Page numbers were not available in our copy. (LLM, conf=0.86)
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- Authors
- Dackermann, U., Yu, Y., Niederleithinger, E., Li, J., & Wiggenhauser, H.
- Affiliations
- UNSW Sydney (AU)
- University of Technology Sydney (AU)
- Federal Institute For Materials Research and Testing (DE)
- Corresponding author
- Yu, Yang
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2017-12-18
- Volume
- 17
- Issue
- 12
- Pages
- 2938
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC5751589
- OpenAlex
- W2777093287
- Cited by
- 33
- References
- 23
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 5 · 2023: 3 · 2022: 1 · 2021: 5 · 2020: 7 · 2019: 3 · 2018: 3
- FWCI
- 2.57
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- Funders
- Bundesanstalt für Materialforschung und -Prüfung
- Deutscher Akademischer Austauschdienst
- Division of Materials Research
- Topics
- Physical Sciences › Engineering › Mechanics of Materials › Ultrasonics and Acoustic Wave Propagation
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Engineering and Materials Analysis
- Keywords
- pile foundations, utility poles, guided wave propagation, damage classification, support vector machine, non-destructive testing, health monitoring, timber poles, concrete poles, principal component analysis, feature extraction
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