Quantitative monitoring of pitting corrosion based on 3-D cellular automata and real-time ENA for RC structures

A quantitative 3-D cellular automata (CA) prediction algorithm based on real-time electrochemical noise analysis (ENA) has been established for the pitting corrosion monitoring of reinforced concrete (RC) structures. The energy distribution ratio of wavelet energy spectroscopy (WES) has been used to drive a 3-D CA model to qualitatively predict the development of the corrosion pit. The EN characteristics of Q235 carbon steel in simulated concrete pore solutions (SCPS) have been investigated. Finally, the feasibility of the 3-D CA model (driven by the real-time ENA) has been verified by 3-D Ultra-depth Video Microscope experiments.