A random forest: based intelligent framework for automated electrochemical impedance analysis in industrial corrosion monitoring

Abstract

<title>Abstract</title> Electrochemical impedance spectroscopy (EIS) is widely applied for corrosion characterization of pipeline steel under complex environmental conditions. However, conventional EIS analysis relies heavily on expert experience for equivalent circuit identification and initial parameter estimation, which limits its scalability and repeatability in long-term industrial monitoring. To address this challenge, this study proposes an engineering-oriented intelligent analysis framework based on random forest models to enable automated equivalent circuit identification and parameter initialization.A complete workflow is established, including data preprocessing, model construction, and post-processing. In the preprocessing stage, a physics-informed multi-strategy normalization scheme is designed to handle the heterogeneous and multi-scale characteristics of EIS features while preserving the intrinsic physical meanings of different impedance components. Within the proposed framework, a random forest classifier is employed to automatically identify equivalent circuit types, followed by circuit-specific multi-output random forest regressors to predict initial values of circuit parameters, thereby enabling stable and efficient nonlinear fitting.The proposed method is validated using atmospheric EIS data of pipeline steel and demonstrates robust performance in both circuit classification and parameter regression tasks, with consistent improvements over conventional machine learning baseline methods. Experimental results show that the proposed framework effectively reduces reliance on expert intervention while maintaining high accuracy, robustness, and interpretability. By integrating data-driven modeling with electrochemical physical insights, this work provides a practical and reliable solution for automated EIS analysis in industrial corrosion monitoring applications.