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A Machine Learning-Based Tool for Assessing the Condition of Wood Utility Poles

  • 2025
  • OriginalPaper
  • Chapter
Published in:
Authors
Niloofar Elyasi
Isabel Crant
Eugen Kim
Mahesh Pandey
Published in
Proceedings of the Canadian Society for Civil Engineering Annual Conference 2024, Volume 13
Publisher
Springer Nature Switzerland

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Abstract

This chapter introduces a machine learning-based tool designed to assess the condition of wood utility poles, crucial for maintaining electrical distribution networks. The study employs a dataset of approximately 80,000 wood poles, with inspection records including factors such as age, condition, material type, and damage causes. The model uses a random forest algorithm to predict future pole conditions with an accuracy of around 91%. Key influential features identified include inclined angle, grounding decay, internal decay, hammer test results, woodpecker damage, and insect presence. The model's accuracy is maintained even when focusing on these top influential parameters, streamlining the assessment process. This tool aims to enhance the reliability of electrical distribution networks by enabling proactive maintenance and replacement strategies, ultimately reducing costs and mitigating safety hazards.
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previous chapter Machine Learning-Based Surrogate Model for Layout Optimization Frameworks: A Comparative Study
next chapter Simplified Design Method for Utility Poles Under the Effect of Downbursts and Tornadoes
Title
A Machine Learning-Based Tool for Assessing the Condition of Wood Utility Poles
Authors
Niloofar Elyasi
Isabel Crant
Eugen Kim
Mahesh Pandey
Copyright Year
2025
Book
Proceedings of the Canadian Society for Civil Engineering Annual Conference 2024, Volume 13

Print ISBN: 978-3-031-96762-7

Electronic ISBN: 978-3-031-96763-4

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-3-031-96763-4_28
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