Computational based automated pipeline corrosion data assessment

Corrosion is a complex process influenced by the surrounding environment and operational systems which cannot be interpreted by deterministic approach as in the industry codes and standards. The advancement of structural inspection technologies and tools has produced a huge amount of corrosion data....

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Main Author: Mat Din, Mazura
Format: Thesis
Language:English
Published: 2015
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Online Access:http://eprints.utm.my/id/eprint/77711/2/MazuraMatDinPFC2015.pdf
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spelling my.utm.777112018-06-29T21:29:49Z http://eprints.utm.my/id/eprint/77711/ Computational based automated pipeline corrosion data assessment Mat Din, Mazura QA75 Electronic computers. Computer science Corrosion is a complex process influenced by the surrounding environment and operational systems which cannot be interpreted by deterministic approach as in the industry codes and standards. The advancement of structural inspection technologies and tools has produced a huge amount of corrosion data. Unfortunately, available corrosion data are still under-utilized. Complicated assessment code, and manual analysis which is tedious and error prone has overburdened pipeline operators. Moreover, the current practices produce a negative corrosion growth data defying the nature of corrosion progress, and consuming a lot of computational time during the reliability assessment. Therefore, this research proposes a computational based automated pipeline corrosion data assessment that provides complete assessment in terms of statistical and computational. The purpose is to improve the quality of corrosion data as well as performance of reliability simulation. To accomplish this, .Net framework and Hypertext Preprocessor (PHP) language is used for an automated matching procedure. The alleviation of deterministic value in corrosion data is gained by using statistical analysis. The corrosion growth rate prediction and comparison is utilized using an Artificial Neural Network (ANN) and Support Vector Machine (SVM) model. Artificial Chemical Reaction Optimization Algorithm (ACROA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) model is used to improve the reliability simulation based on the matched and predicted corrosion data. A computational based automated pipeline corrosion data assessment is successfully experimented using multiple In-Line Inspection (ILI) data from the same pipeline structure. The corrosion data sampling produced by the automated matching is consistent compared to manual sampling with the advantage of timeliness and elimination of tedious process. The computational corrosion growth prediction manages to reduce uncertainties and negative rate in corrosion data with SVM prediction is superior compared to A ^N . The performance value of reliability simulation by ACROA outperformed the PSO and DE models which show an applicability of computational optimization models in pipeline reliability assessment. Contributions from this research are a step forward in the realization of computational structural reliability assessment. 2015-12 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/77711/2/MazuraMatDinPFC2015.pdf Mat Din, Mazura (2015) Computational based automated pipeline corrosion data assessment. PhD thesis, Universiti Teknologi Malaysia, Faculty of Computing. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:96431
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Mat Din, Mazura
Computational based automated pipeline corrosion data assessment
description Corrosion is a complex process influenced by the surrounding environment and operational systems which cannot be interpreted by deterministic approach as in the industry codes and standards. The advancement of structural inspection technologies and tools has produced a huge amount of corrosion data. Unfortunately, available corrosion data are still under-utilized. Complicated assessment code, and manual analysis which is tedious and error prone has overburdened pipeline operators. Moreover, the current practices produce a negative corrosion growth data defying the nature of corrosion progress, and consuming a lot of computational time during the reliability assessment. Therefore, this research proposes a computational based automated pipeline corrosion data assessment that provides complete assessment in terms of statistical and computational. The purpose is to improve the quality of corrosion data as well as performance of reliability simulation. To accomplish this, .Net framework and Hypertext Preprocessor (PHP) language is used for an automated matching procedure. The alleviation of deterministic value in corrosion data is gained by using statistical analysis. The corrosion growth rate prediction and comparison is utilized using an Artificial Neural Network (ANN) and Support Vector Machine (SVM) model. Artificial Chemical Reaction Optimization Algorithm (ACROA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) model is used to improve the reliability simulation based on the matched and predicted corrosion data. A computational based automated pipeline corrosion data assessment is successfully experimented using multiple In-Line Inspection (ILI) data from the same pipeline structure. The corrosion data sampling produced by the automated matching is consistent compared to manual sampling with the advantage of timeliness and elimination of tedious process. The computational corrosion growth prediction manages to reduce uncertainties and negative rate in corrosion data with SVM prediction is superior compared to A ^N . The performance value of reliability simulation by ACROA outperformed the PSO and DE models which show an applicability of computational optimization models in pipeline reliability assessment. Contributions from this research are a step forward in the realization of computational structural reliability assessment.
format Thesis
author Mat Din, Mazura
author_facet Mat Din, Mazura
author_sort Mat Din, Mazura
title Computational based automated pipeline corrosion data assessment
title_short Computational based automated pipeline corrosion data assessment
title_full Computational based automated pipeline corrosion data assessment
title_fullStr Computational based automated pipeline corrosion data assessment
title_full_unstemmed Computational based automated pipeline corrosion data assessment
title_sort computational based automated pipeline corrosion data assessment
publishDate 2015
url http://eprints.utm.my/id/eprint/77711/2/MazuraMatDinPFC2015.pdf
http://eprints.utm.my/id/eprint/77711/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:96431
_version_ 1643657611354046464
score 13.211869