Estimating corrosion growth rates for underground pipelines is a challenging problem. There are confounding variables with complex interaction effects that may result in unexpected outcomes. For instance, the relationship between soil conditions and AC interference is highly non-linear and challenging to model. This work expands upon prior work using a suite of machine learning tools to estimate corrosion rates. However, instead of estimating a single corrosion growth rate for a single girth weld address (GWA), this work estimates a distribution of potential corrosion growth rates. Modeling distributions provide a more effective risk-measurement framework, especially concerning high volatility or areas of severe tail risk.
This work relies heavily on machine learning and geospatial tools - particularly artificial neural networks and gradient boosted trees to estimate the corrosion rates and non-linear processes. Building upon prior work using data from a North American Operator, the models in this paper use additional variables from recent research in AC interference and microbiologically influenced corrosion to construct a higher accuracy and distribution-based model of pipeline corrosion risk.
CP coupons have been used since the 1930s by several of the pioneers of the corrosion-control industry, both in North America and in Europe. Over the last two decades, the use of CP coupons has been rediscovered as a practical method to determine the level of polarization of a buried structure and to confirm the voltage drop in a potential measurement. Acceptance of CP coupon technology is slowly occurring. Research sponsored by the pipeline industry has explored the use of CP coupons and has helped validate the use of this technology.