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Estimating Corrosion Rates for Underground Pipelines: A Machine Learning Approach

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Estimating corrosion growth rate for underground pipelines is a non-linear multivariate problem. There are many potential confounding variables such as soil parameters cathodic protection AC/DC interference seasonal / climate conditions and proximity to unique geographic features such as wetlands or polluted environments. The work presented provides an approach for estimating underground corrosion growth rates using a dataset of observations from a North American pipeline operator. Extensive geospatial data is utilized that has been obtained from public and private sources and extrapolated using Inverse distance weighted (IDW) interpolation. This work presents a model using IDW to estimate parameters involving soil interference geography and climate factors for any location in North America.Using this data this work then presents several different machine learning approaches including Generalized Linear Models eXtreme Boosted Trees and Neural Networks. All three provide an accurate estimation for corrosion growth rates for an underground asset at any latitude and longitude pair in North America. Each method comes with potential benefits and pitfalls specifically; trade-offs between model accuracy and transparency. This work presents a framework for comparing geo-spatial and machine learning estimates. Findings and a framework are provided for owners to assess how to think machine learning on their own assets.

Product Number: 51319-13456-SG
Author: Joseph Mazzella, Len Krissa, Thomas Hayden, Haralampos Tsaprailis
Publication Date: 2019
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$20.00
$20.00
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Picture for Estimating Corrosion Rate Risk Distributions using Machine Learning and Geospatial Analytics
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Estimating Corrosion Rate Risk Distributions using Machine Learning and Geospatial Analytics

Product Number: 51320-14640-SG
Author: Joseph Mazzella, Thomas Hayden, Haralampos Tsaprailis, Len Krissa
Publication Date: 2020
$20.00

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. 
Picture for Machine Learning for Erosion-Corrosion Prediction in an Alkaline Environment
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Machine Learning for Erosion-Corrosion Prediction in an Alkaline Environment

Product Number: 51319-13450-SG
Author: Bedi Aydin Baykal
Publication Date: 2019
$20.00

Machine learning is a method that allows interpretations of new data using an established database of older data by referring to already-known results (known as “labels”) and extrapolating between them to estimate the label that would be assigned to a different experiment. This can be a powerful tool for corrosion prediction because it makes it possible to estimate a range of corrosion rates for a certain family of materials in a specific range of environments without actually performing experiments. In this paper the machine learning concept was applied to the erosion-corrosion of steels in white liquor a strongly alkaline industrial chemical used for pulping wood chips. Previously obtained corrosion data in white liquor which included different steel compositions particle concentrations and sizes temperatures and fluid properties such as viscosity were compiled and assigned labels based on previous assessments in the industry as passive acceptable marginal or unsuitable according to observed corrosion rate. Models using thirty selected variables were built based on this data using diverse machine learning methods including support vector machines (SVM) decision trees k-nearest neighbor methods (KNN). discriminant analysis etc. Feature selection was attempted for each model. The best accuracies for each method were compiled and assessed regarding their promise for predictive purposes in erosion-corrosion.
Picture for 96657 COMPUTER LEARNING SYSTEMS IN CORROSION
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96657 COMPUTER LEARNING SYSTEMS IN CORROSION

Product Number: 51300-96657-SG
ISBN: 96657 1996 CP
Author: C.P. Sturrock, W.F. Bogaerts
$20.00
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