Towards understanding and prediction of corrosion degradation of organic coatings under tropical marine atmospheric environment via a data-driven approach

Abstract

The corrosion degradation of organic coatings in tropical marine atmospheric environments results in substantial economic losses across various industries. The complexity of a dynamic environment, combined with high costs, extended experimental periods, and limited data, places a limit on the comprehension of this process. This study addresses this challenge by investigating the corrosion degradation of damaged organic coatings in a tropical marine environment using an atmospheric corrosion monitoring sensor and a random forest (RF) model. For damage simulation, a polyurethane coating applied to a Fe/graphite corrosion sensor was intentionally scratched and exposed to the marine atmosphere for over one year. Pearson correlation analysis was performed for the collection and filtering of environmental and corrosion current data. According to the RF model, the following specific conditions contributed to accelerated degradation: relative humidity (RH) above 80% and temperatures below 22.5°C, with the risk increasing significantly when RH exceeded 90%. High RH and temperature exhibited a cumulative effect on coating degradation. A high risk of corrosion occurred in the nighttime. The RF model was also used to predict the coating degradation process using environmental data as input parameters, with the accuracy showing improvement when the duration of influential environmental ranges was considered.

Keywords

organic coating degradation / atmospheric corrosion / machine learning / exposure test / random forest / coating sensor

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