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Products tagged with 'neural networks'

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Picture for 51318-11332- Intelligent Corrosion Monitoring System for the Management of Existing Steel Transmission Structures
Available for download

51318-11332- Intelligent Corrosion Monitoring System for the Management of Existing Steel Transmission Structures

Product Number: 51318-11332-SG
Author: Karen Callery / Ibrahim Hathout
Publication Date: 2018
$20.00

A large power transmitter completed a multiyear corrosion study to verify the corrosion rate models for zinc and steel. As a result of that study, a long term field corrosion monitoring program, at 125 sites, was initiated.
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. 
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