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The issue of pipeline failure has garnered considerable interest from various research communities due to its notable repercussions on the worldwide economy, as well as the risks associated with leaks, explosions, and expensive periods of downtime. This paper aims to build a model for classifying and predicting the corrosion degradation of a pipe used to transport water in mines by the Quebec Metallurgy Center. To this end, two types of models were developed: three binary classification models: SVM, RF, and KNN, yielding F1-measurements of 0.968, 0.969, and 0.945 respectively, and a time series model, LSTM, which, with a loss of less than 0.01, was able to predict average variations in pipeline thickness for 63 days.

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Machine Learning-Based Classification and Prediction to Assess Corrosion Degradation in Mining Pipelines

  • 会议论文

  • 发表日期：

    2025年

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  • Kalidou Moussa Sow,
  • Nadia Ghazzali

摘要

The issue of pipeline failure has garnered considerable interest from various research communities due to its notable repercussions on the worldwide economy, as well as the risks associated with leaks, explosions, and expensive periods of downtime. This paper aims to build a model for classifying and predicting the corrosion degradation of a pipe used to transport water in mines by the Quebec Metallurgy Center. To this end, two types of models were developed: three binary classification models: SVM, RF, and KNN, yielding F1-measurements of 0.968, 0.969, and 0.945 respectively, and a time series model, LSTM, which, with a loss of less than 0.01, was able to predict average variations in pipeline thickness for 63 days.