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<p>This study presents a novel electrochemical noise (EN)-based data mining approach for non-invasive measurement of environmental chloride ion (Cl<sup>−</sup>) concentration in reinforced concrete structures (RCS) exposed to stray current interference. A custom experimental system captures EN signals from mortar-embedded steel rebars under varying Cl<sup>−</sup> concentrations (0.05–0.9&#xa0;mol/L) and stray current densities (0.05–0.1&#xa0;A/cm²). Time-domain statistical features and frequency-domain wavelet-decomposed energy parameters are extracted from EN signals as regression inputs. To overcome the complexity of signal-environment relationships, an intelligent algorithm (WOA-XGBoost-Attention) is proposed, integrating Whale Optimization Algorithm (WOA) for hyperparameter training, XGBoost for regression, and an attention mechanism to weight critical features dynamically. Validation shows the model achieves 95.33% average accuracy and a 0.9929 correlation coefficient (<i>R</i><sup>2</sup>) for Cl<sup>−</sup> prediction, significantly outperforming benchmark methods (XGBoost, Random Forest, etc.). The framework enables early warning of stray current corrosion by detecting critical Cl<sup>−</sup> thresholds, offering a robust solution for monitoring subway shield tunnel durability where traditional methods are impractical.</p>

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Electrochemical noise-based data mining to environmental Cl− concentration measurement of reinforced concrete structure under stray current interference

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月5日

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  • Fangfang Xing,
  • Shaoyi Xu,
  • Yuqiao Wang,
  • Wei Li,
  • Chengtao Wang

摘要

This study presents a novel electrochemical noise (EN)-based data mining approach for non-invasive measurement of environmental chloride ion (Cl−) concentration in reinforced concrete structures (RCS) exposed to stray current interference. A custom experimental system captures EN signals from mortar-embedded steel rebars under varying Cl− concentrations (0.05–0.9 mol/L) and stray current densities (0.05–0.1 A/cm²). Time-domain statistical features and frequency-domain wavelet-decomposed energy parameters are extracted from EN signals as regression inputs. To overcome the complexity of signal-environment relationships, an intelligent algorithm (WOA-XGBoost-Attention) is proposed, integrating Whale Optimization Algorithm (WOA) for hyperparameter training, XGBoost for regression, and an attention mechanism to weight critical features dynamically. Validation shows the model achieves 95.33% average accuracy and a 0.9929 correlation coefficient (R2) for Cl− prediction, significantly outperforming benchmark methods (XGBoost, Random Forest, etc.). The framework enables early warning of stray current corrosion by detecting critical Cl− thresholds, offering a robust solution for monitoring subway shield tunnel durability where traditional methods are impractical.