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Application of the Machine Learning Tools in the Integrity Management of Pipelines Containing Dent-Gouges and Corrosions.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Application of the Machine Learning Tools in the Integrity Management of Pipelines Containing Dent-Gouges and Corrosions./
作者:	He, Ziming.
面頁冊數:	1 online resource (172 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-01, Section: A.
Contained By:	Dissertations Abstracts International85-01A.
標題:	Load. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30537980click for full text (PQDT)
ISBN:	9798379868499

Application of the Machine Learning Tools in the Integrity Management of Pipelines Containing Dent-Gouges and Corrosions.
He, Ziming.

Application of the Machine Learning Tools in the Integrity Management of Pipelines Containing Dent-Gouges and Corrosions. - 1 online resource (172 pages)

Source: Dissertations Abstracts International, Volume: 85-01, Section: A.

Thesis (Ph.D.)--The University of Western Ontario (Canada), 2023.

Includes bibliographical references

Dent-gouges and corrosions are two of the well-known failure mechanisms that threaten the structural integrity management of oil and gas pipelines. Dent-gouges or corrosions markedly reduce the burst capacity of pipelines as a result of localized wall thickness reduction. Fitness-forservice (FFS) assessment is commonly employed to maintain the integrity of in-service pipelines containing flaws and the burst capacity evaluation is central to the FFS assessment. As the predictive accuracy of existing FFS models is generally very poor, the use of machine learning (ML) tools provides a viable option to develop burst capacity models with high accuracy. The main objective of the present thesis is to facilitate the FFS assessment of dent-gouges and corrosions based on ML tools.The first study proposes an improved burst capacity model for pipelines containing dent-gouges based on European Pipeline Research Group (EPRG) burst capacity model using full-scale burst tests by adding a correction term. The Gaussian process regression (GPR) is employed to quantify the correction term, which is a function of six non-dimensional random variables incorporating the effect of pipe and geometric properties, sizes of dent-gouges, and internal pressure loading condition. The accuracy of the improved EPRG model, i.e. EPRG-C model, is validated based on the comparison between the test and predicted burst capacities corresponding to the test data, and shown to be markedly greater than that of the EPRG model, suggesting the high effectiveness of the correction term.The second study presents a limit state-based assessment (LSBA) framework for pipelines containing dent-gouges to achieve reliability consistent outcomes. The LSBA is formulated based on the EPRG-C model proposed in the first study by assigning appropriate partial safety factors to key variables as well as the internal pressure. The calibration of partial safety factors is carried out by making the outcomes of LSBA are consistent with those of the reliability-based assessment given different pre-selected allowable failure probabilities. The failure probabilities corresponding to extensive assessment cases covering wide ranges of pipe geometric and material properties, sizes of dent-gouges and the model error are evaluated using the first-order reliability method. The validity of the calibrated partial safety factors is demonstrated using independent assessment cases and two illustrative examples. The advantages of LSBA over the deterministic assessment procedure in terms of achieving reliability-consistent assessment outcomes is further demonstrated.The third study employs a deep learning algorithm tabular generative adversarial network (TGAN) to generate synthetic burst tests by capturing the joint probability distribution based on real fullscale burst test data of corroded pipelines. Two other ML tools, random forest (RF) and extra tree (ET), are used to tune the hyper-parameters and validate the credibility of TGAN-generated data. A simple criterion is proposed to eliminate the outliers contained in the synthetic data. The results indicate that the synthetic burst test data match well with the real data, suggesting that TGAN can accurately capture the joint probability distribution of real test data and generate credible synthetic data.The fourth study develops new ML-based burst capacity models for dent-gouges with combined real and synthetic full-scale burst tests.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379868499Subjects--Topical Terms:

3562902
Load.
Index Terms--Genre/Form:

542853
Electronic books.

Application of the Machine Learning Tools in the Integrity Management of Pipelines Containing Dent-Gouges and Corrosions.
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500 $a Source: Dissertations Abstracts International, Volume: 85-01, Section: A.
500 $a Advisor: Zhou, Wenxing.
502 $a Thesis (Ph.D.)--The University of Western Ontario (Canada), 2023.
504 $a Includes bibliographical references
520 $a Dent-gouges and corrosions are two of the well-known failure mechanisms that threaten the structural integrity management of oil and gas pipelines. Dent-gouges or corrosions markedly reduce the burst capacity of pipelines as a result of localized wall thickness reduction. Fitness-forservice (FFS) assessment is commonly employed to maintain the integrity of in-service pipelines containing flaws and the burst capacity evaluation is central to the FFS assessment. As the predictive accuracy of existing FFS models is generally very poor, the use of machine learning (ML) tools provides a viable option to develop burst capacity models with high accuracy. The main objective of the present thesis is to facilitate the FFS assessment of dent-gouges and corrosions based on ML tools.The first study proposes an improved burst capacity model for pipelines containing dent-gouges based on European Pipeline Research Group (EPRG) burst capacity model using full-scale burst tests by adding a correction term. The Gaussian process regression (GPR) is employed to quantify the correction term, which is a function of six non-dimensional random variables incorporating the effect of pipe and geometric properties, sizes of dent-gouges, and internal pressure loading condition. The accuracy of the improved EPRG model, i.e. EPRG-C model, is validated based on the comparison between the test and predicted burst capacities corresponding to the test data, and shown to be markedly greater than that of the EPRG model, suggesting the high effectiveness of the correction term.The second study presents a limit state-based assessment (LSBA) framework for pipelines containing dent-gouges to achieve reliability consistent outcomes. The LSBA is formulated based on the EPRG-C model proposed in the first study by assigning appropriate partial safety factors to key variables as well as the internal pressure. The calibration of partial safety factors is carried out by making the outcomes of LSBA are consistent with those of the reliability-based assessment given different pre-selected allowable failure probabilities. The failure probabilities corresponding to extensive assessment cases covering wide ranges of pipe geometric and material properties, sizes of dent-gouges and the model error are evaluated using the first-order reliability method. The validity of the calibrated partial safety factors is demonstrated using independent assessment cases and two illustrative examples. The advantages of LSBA over the deterministic assessment procedure in terms of achieving reliability-consistent assessment outcomes is further demonstrated.The third study employs a deep learning algorithm tabular generative adversarial network (TGAN) to generate synthetic burst tests by capturing the joint probability distribution based on real fullscale burst test data of corroded pipelines. Two other ML tools, random forest (RF) and extra tree (ET), are used to tune the hyper-parameters and validate the credibility of TGAN-generated data. A simple criterion is proposed to eliminate the outliers contained in the synthetic data. The results indicate that the synthetic burst test data match well with the real data, suggesting that TGAN can accurately capture the joint probability distribution of real test data and generate credible synthetic data.The fourth study develops new ML-based burst capacity models for dent-gouges with combined real and synthetic full-scale burst tests.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
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773 0 $t Dissertations Abstracts International $g 85-01A.
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