Search
Filters
Close

Estimating Corrosion Rate Risk Distributions using Machine Learning and Geospatial Analytics

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. 

Product Number: 51320-14640-SG
Author: Joseph Mazzella, Thomas Hayden, Haralampos Tsaprailis, Len Krissa
Publication Date: 2020
$0.00
$20.00
$20.00
Also Purchased
Picture for Estimating Corrosion Rates for Underground Pipelines: A Machine Learning Approach
Available for download

Estimating Corrosion Rates for Underground Pipelines: A Machine Learning Approach

Product Number: 51319-13456-SG
Author: Joseph Mazzella, Len Krissa, Thomas Hayden, Haralampos Tsaprailis
Publication Date: 2019
$20.00
Picture for IR 4.0 Integrity Management Using Data Analytics
Available for download

IR 4.0 Integrity Management Using Data Analytics

Product Number: MPWT19-15487
Author: Dr. Haaken Ahnfelt, Dr. Luis Caetano, Dr. Hilde Aas Nøst, Dr. Knut Nordanger, Reidar Kind, Dr. Zeeshan Lodhi, Dr. Lay Seong Teh
Publication Date: 2019
$0.00