Abstract

Conventional corrosion monitoring techniques often require costly instrumentation and direct access to structures, limiting their suitability for long-term monitoring. This study presents a machine learning feasibility study for early-stage corrosion detection using Ultra-High Frequency (UHF) Radio Frequency Identification (RFID) measurements. Machine learning algorithms were applied to a previously published RFID corrosion dataset obtained from steel specimens exposed to marine atmospheric corrosion for 0, 1, 3, and 6 months. RFID-derived features, including Analogue Identifier (AID), forward power, frequency, phase, and backscattered power, were analysed using unsupervised and supervised learning methods. For corrosion-stage discrimination, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) achieved an Adjusted Rand Index (ARI) of 1.00, a Normalised Mutual Information (NMI) score of 1.00, and a Silhouette score of 0.790. For nominal corrosion-thickness state estimation, a Random Forest regressor achieved an R2 of 1.00, RMSE of 1.34 µm, and MAE of 0.21 µm under 15-fold cross-validation. Additional Leave-One-Sample-Out (LOSO) validation using readcount-based measurement-event groupings yielded an RMSE of 0.12 µm and an R2 of 1.00. These results reflect nominal corrosion-thickness state estimation from repeated measurements on a single physical specimen per corrosion stage. SHAP analysis identified forward power and AID as the dominant predictive features. The results demonstrate the potential of RFID-enabled machine learning for early-stage corrosion assessment and provide a foundation for future experimental validation.

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