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<p>This review examines machine learning approaches for predicting pipeline residual strength, which is crucial for assessing operational lifespan and safety in industrial applications. We analyze various machine learning models, data preprocessing methods, and evaluation metrics used in existing research. The study highlights how data characteristics and model selection influence prediction accuracy, providing practitioners with guidelines for model implementation, while discussing current challenges and future research directions.</p>

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Machine learning methods for predicting residual strength in corroded oil and gas steel pipes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月24日

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  • Qiankun Wang,
  • Hongfang Lu

摘要

This review examines machine learning approaches for predicting pipeline residual strength, which is crucial for assessing operational lifespan and safety in industrial applications. We analyze various machine learning models, data preprocessing methods, and evaluation metrics used in existing research. The study highlights how data characteristics and model selection influence prediction accuracy, providing practitioners with guidelines for model implementation, while discussing current challenges and future research directions.