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Wood utility poles are widely used to support electrical cables in transmission and distribution networks in North America. Inspection and assessment of wood pole condition are largely based on manual methods, which are costly and inefficient in a large network. This paper presents an innovative solution to this problem by introducing a machine learning (ML)-based assessment tool to forecast the future condition of wood poles. The proposed approach uses the Random Forest (RF) algorithm, which achieves 91% accuracy in predicting the pole condition state. This study highlights the potential of ML in enhancing the reliability and cost-effectiveness of utility network maintenance.

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

  • 会议论文

  • 发表日期：

    2025年

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  • Niloofar Elyasi,
  • Isabel Crant,
  • Eugen Kim,
  • Mahesh Pandey

摘要

Wood utility poles are widely used to support electrical cables in transmission and distribution networks in North America. Inspection and assessment of wood pole condition are largely based on manual methods, which are costly and inefficient in a large network. This paper presents an innovative solution to this problem by introducing a machine learning (ML)-based assessment tool to forecast the future condition of wood poles. The proposed approach uses the Random Forest (RF) algorithm, which achieves 91% accuracy in predicting the pole condition state. This study highlights the potential of ML in enhancing the reliability and cost-effectiveness of utility network maintenance.