Predictive Corrosion Models to Mitigate Environmental Hazards on Ground Assets

Author(s):

Daniel Pope, James A. Ellor, John P. Repp, C. Thomas Savell, Thomas A. Considine, and Lindsey M. Blohm

Abstract:

Coating degradation on U.S. Army ground systems incurs significant maintenance cost. The objective of this proposed work is to develop a predictive Bayesian model for coating degradation and subsequent substrate corrosion on Army ground assets. With a better understanding of the root causes, steps can be taken to reduce corrosion impacts on Army materiel. In this effort, a model was created by incorporating significant analysis and learning from field surveys of over 15,000 assets and 250,000 components, coatings performance following standardized testing, and observations of coating conditions on fielded items. Model outputs will provide basic support to a Commodity Manager to determine optimal repaint intervals and minimize expenditures as well as assist in developing new products/processes beneficial to coating performance and yield increased life expectancy of an asset’s protective coating systems.