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Combining engineering and data-driven approaches to model the risk of excavation damage to underground natural gas facilities

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TitleInfo
Title
Combining engineering and data-driven approaches to model the risk of excavation damage to underground natural gas facilities
Name (type = personal)
NamePart (type = family)
Abusnina
NamePart (type = given)
Hamza
NamePart (type = date)
1985-
DisplayForm
Hamza Abusnina
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Gong
NamePart (type = given)
Jie
DisplayForm
Jie Gong
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (encoding = w3cdtf); (qualifier = exact)
2019
DateOther (encoding = w3cdtf); (qualifier = exact); (type = degree)
2019-10
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2019
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
In the United States there are thousands of gas pipe miles, long grids, and networks of natural gas lines across the states. Recent pipeline leaks and explosions in various regions have driven the industry to re-evaluate on-going efforts aimed at aggressive pursuit of preventive strategies. Considering that safety and environmental risk is a major issue, particularly in cases where underground gas line damages and other explosions are involved, pipeline accidental risk represents both financial and social interests in the gas pipeline industry.
It is possible to, knowingly or unknowingly, damage underground gas services, water services, electrical services, etc. Incidents involving infrastructure damage are far more common than perceived; and these incidents result in hundreds-of-thousands, if not millions, of dollars in repair or replacement. Damages to underground facilities may occur by large construction contractors or by homeowners.
The main objective of this research is two-fold: a) to determine the important risk factors contributing to the underground gas pipe damages; b) to identify inputs required for an effective evaluation and assessment of the risk encountered in exchange of information between different parties involved during the repair of underground gas pipelines. Predictive Model will be developed based on machine learning algorithms (Logistic Regression) to be used in predicting the important risk factors affecting the underground Gas Pipe Damages.
The research will systematically analyze the risk of underground gas pipeline network damage including; process the data collected from agency, organize/classify the data based on certain parameters, process the data, develop integrated risk model and influence diagram. Next, Bayesian Network will be developed based on the derived important factors, and calculated probabilities for each attribute.
Subject (authority = RUETD)
Topic
Civil and Environmental Engineering
Subject (authority = local)
Topic
Gas pipe damages
Subject (authority = LCSH)
Topic
Natural gas pipelines -- Maintenance and repair
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_9441
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application/pdf
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text/xml
Extent
1 online resource (x, 116 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
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TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-xqcc-cm66
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Abusnina
GivenName
Hamza
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2018-12-18 12:00:04
AssociatedEntity
Name
Hamza Abusnina
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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2019-04-10T07:37:32
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2019-04-10T07:37:32
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