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contributor authorVerdín Martinez, Adrián
contributor authorSosa Hernandez, Eliceo
contributor authorAlamilla Lopez, Jorge Luis
contributor authorLiu, Hongbo
date accessioned2026-08-20T12:06:14Z
date available2026-08-20T12:06:14Z
date copyright2026/02/26
date issued2026
identifier otherJPSEA2.PSENG-1938.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313101
description abstractAbstractA scheme based on two historical in-line inspection data sets was developed to estimate the evolution of pitting depth caused by internal corrosion in pipelines. This process involved carefully aligning inspection pitting data and modeling pitting ...Practical ApplicationsPipelines serve as the backbone of energy transportation, but internal corrosion can weaken the steel, leading to costly failures. This study presents a practical method for predicting and managing internal corrosion by combining ...
publisherAmerican Society of Civil Engineers
titlePipeline Corrosion Pits Growth Prediction with Probabilistic Model and Artificial Neural Network
typeJournal Article
journal volume17
journal issue2
journal titleJournal of Pipeline Systems Engineering and Practice
identifier doi10.1061/JPSEA2.PSENG-1938
journal fristpage04026018-1
journal lastpage04026018-9
page9
treeJournal of Pipeline Systems Engineering and Practice:;2026:;Volume ( 017 ):;issue: 002
contenttypeFulltext


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