Development of a spatial decision support framework for risk assessment of oil and gas pipelines exposed to external pitting corrosion
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Woldesellasse, Haile
issued: 2023-04-21T11:21:17Z
issued: 2023
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: This dissertation proposes a comprehensive decision support framework for risk assessment of oil and gas pipelines exposed to external pitting corrosion. The study begins by performing a bibliometric analysis to identify the top cited research publications on this area. After that, several imputation and class imbalance handling techniques are proposed to handle the missing values and class imbalance that are frequently observed problems in a corrosion dataset. The usefulness of both proposed techniques was assessed using a neural network model. The model's prediction performance improved when it was trained using the cleaned corrosion dataset. Then, the risk assessment framework is developed using the corrosion dataset in a bow-tie approach, which represents an entire accident scenario starting with causes and ending with consequences. The proposed framework was developed by integrating Bayesian belief network (BBN) with a geographic information system (GIS). The framework part for evaluating the failure probability of buried pipelines is developed using three basic components: corrosion model, failure pressure model, and limit state function to estimate the probability of failure due to burst and leak. Lastly, the framework part for evaluating the consequence assessment is developed using an existing event tree and additional factors including failure probability of a pipeline, igniting probability, population density, and the presence of protected or conserved areas. The consequence of gas pipeline failure are assessed based on casualty and environmental impact. The reasoning power of the BBN and the spatial capability of the GIS combine to form a powerful tool to aid decision makers manage a spatially distributed aging pipeline. Additionally, it aids pipeline operators in making timely, and cost-effective decisions to avoid accidents in high consequence areas.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/84396
note: Development of a spatial decisionsupport framework for risk assessment ofoil and gas pipelines exposed to externalpitting corrosionbyHaile WoldesellasseM.Sc., Khalifa University, 2017B.Sc., Eritrea Institute of Technology, 2013A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinTHE COLLEGE OF GRADUATE STUDIES(Civil Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)April 2023© Haile Woldesellasse, 2023The following individuals certify that they have read, and recommend tothe College of Graduate Studies for acceptance, a dissertation entitled:Development of a spatial decision support framework forrisk assessment of oil and gas pipelines exposed to externalpitting corrosionsubmitted by Haile Woldesellasse in partial fulfilment of the require-ments of the degree of Doctor of PhilosophyDr. Solomon Tesfamariam, School of EngineeringSupervisorDr. Sumi Siddiqua, School of EngineeringSupervisory Committee MemberDr. Ahmad Al-Dabbagh, School of EngineeringSupervisory Committee MemberDr. Anas Chaaban, School of EngineeringUniversity ExaminerDr. Samer Adeeb, University of AlbertaExternal ExamineriiAbstractThis dissertation proposes a comprehensive decision support frameworkfor risk assessment of oil and gas pipelines exposed to external pitting corro-sion. The study begins by performing a bibliometric analysis to identify thetop cited research publications on this area. After that, several imputationand class imbalance handling techniques are proposed to handle the missingvalues and class imbalance that are frequently observed problems in a corro-sion dataset. The usefulness of both proposed techniques was assessed usinga neural network model. The model’s prediction performance improved whenit was trained using the cleaned corrosion dataset. Then, the risk assessmentframework is developed using the corrosion dataset in a bow-tie approach,which represents an entire accident scenario starting with causes and endingwith conseque…
has type: Thesis/Dissertation
date issued: 2023-05
is shown at: 10.14288/1.0431322
language: eng
degree discipline: Civil Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Tesfamariam, Solomon
type: Text
identifier URI: http://hdl.handle.net/2429/84396