Electrochemical noise-based data mining to environmental Cl

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.