Corrosion Assessment: Data Mining for Quantifying Associations between Indoor Accelerated and Outdoor Natural Tests

Material scientists study corrosion degradation of metallic structures due to its heavy economic and maintenance burdens. Assessing corrosion is both time consuming and labor intensive when utilizing outdoor tests under natural exposure conditions. Accelerated indoor corrosion tests are conducted in laboratory settings by material scientists to gage performance in a shorter period of time than outdoor tests. However, these indoor tests do not always correlate well with the actual performance in outdoor environments. Thus, there is a need to apply data-science methodologies to analyze and establish quantitative associations between indoor accelerated and outdoor exposure assessments to optimize artificially accelerated methods. We work with material experimental records including images, notes, meta-data and human-rated assessments collected over years. We apply data mining methods, such as Canonical Correlation Analysis (CCA) and its variants, to extract latent associations with corresponding feature mappings between the indoor and outdoor assessments in a projected data subspace. We find that CCA provides not only reliable quantitative associations but also interpretive mappings between the indoor and outdoor assessments. Further, three methods applied to control bias due to distinctive coating system stack up yield Pearson's correlation coefficients ranging from 0.70 to 0.83 in the optimized CCA subspaces, respectively. Moreover, predicting outdoor assessments from the CCA-projected indoor test data compared to using the original indoor data is shown to result in an increase in accuracy from 88% to 90% - confirming the effectiveness of our approach. Lastly, our results facilitate interesting domain-relevant discovery that could potentially lead to experimentalists better understanding corrosion resistance.