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<p>Polarization resistance measurements of steel in concrete are widely used in laboratory investigations and, to a lesser extent, in field assessments of corrosion to assess the corrosion rate or behavior of the metal. However, the complex geometry of reinforced concrete complicates the interpretation of these measurements, particularly when relating the apparent response to the true interfacial polarization resistance. In this work, genetic programming-based symbolic regression is used to develop an accurate mathematical expression that predicts polarization resistance as a function of geometric parameters under the assumption of uniform corrosion. Training data for the machine learning model are generated using finite element simulations spanning a range of beam and counter-electrode dimensions. The error distributions of the machine learning model and existing theoretical approaches are compared, and the advantages and limitations of each are discussed. This study focuses on beam geometries, with the aim of enabling future application to more complex structural configurations and reinforcement layouts.</p>

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Theoretical and data-driven models for the apparent polarization resistance of reinforced concrete beams

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月29日

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  • Christopher L. Alexander,
  • Ryan M. Katona,
  • David Montes de Oca Zapiain

摘要

Polarization resistance measurements of steel in concrete are widely used in laboratory investigations and, to a lesser extent, in field assessments of corrosion to assess the corrosion rate or behavior of the metal. However, the complex geometry of reinforced concrete complicates the interpretation of these measurements, particularly when relating the apparent response to the true interfacial polarization resistance. In this work, genetic programming-based symbolic regression is used to develop an accurate mathematical expression that predicts polarization resistance as a function of geometric parameters under the assumption of uniform corrosion. Training data for the machine learning model are generated using finite element simulations spanning a range of beam and counter-electrode dimensions. The error distributions of the machine learning model and existing theoretical approaches are compared, and the advantages and limitations of each are discussed. This study focuses on beam geometries, with the aim of enabling future application to more complex structural configurations and reinforcement layouts.