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Abstract
This thesis investigates the macroscopic corrosion of steel under potentiostatic conditions using electrochemical experiments and a probabilistic modeling approach. A probabilistic cellular automata (PCA) model is developed to predict the propagation and penetration of corrosive material in the steel. The PCA model is developed in MATLAB and is corrected using experimental results from a three-electrode corrosion cell. Several steel specimens are corroded under various environmental conditions, and their mechanical strengths are evaluated. The corrected model results are evaluated using finite element analysis (FEA) and tensile testing of the experimental specimens. The trends from the FEA results correlated closely with the trends from the tensile testing, across three different specimen designs. This thesis contributes to the understanding of the corrosion behavior of steel under potentiostatic conditions and provides a tool for predicting the corrosion behavior and mechanical properties of steel under such conditions.