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Computer vision-based assessment of coastal building structures during hurricane events
Descriptive
TitleInfo
Title
Computer vision-based assessment of coastal building structures during hurricane events
Name
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Zhou
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Zixiang
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1989-
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Zixiang Zhou
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author
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Jie
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Jie Gong
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Advisory Committee
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chair
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Moon
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Franklin
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Franklin Moon
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Advisory Committee
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internal member
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(type = family)
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Husam
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Husam Najm
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Advisory Committee
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internal member
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(type = family)
Zhu
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Zhigang
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Zhigang Zhu
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Advisory Committee
Role
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outside member
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Rutgers University
Role
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degree grantor
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School of Graduate Studies
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school
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theses
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2018
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2018-01
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2018
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xx
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eng
Abstract
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Severe hurricane events have been occurring across the United States, threatening both highly developed urban areas and distressed island communities. Assessment of building damages due to hurricane events is a critical element in disaster management as it supports not only search and recues operations but also provides insights into the performance of existing planning and building practices. But unlike many other extreme weather events, hurricanes can topple an entire region for an extended amount of time, creating a daunting task for traditional foot-on-ground building damage assessment approaches. The overarching goal of this study is to create and test a computational framework to leverage big spatial data acquisition and processing technologies for automated building damage assessment. The specific aims of this study include: (1) formulating a cohesive and multi-scale damage assessment approach that considers the continuously evolving data sources and damage assessment needs during different phases of disaster management; (2) developing algorithms for rapid building damage assessment with airborne lidar data, which are typically collected immediately after the landing of hurricane events; (2) developing algorithms for component-level building damage assessment with high-resolution ground-based lidar data; (3) charactering the performance of image based 3D reconstruction for damage assessment; (4) developing robust image alignment algorithms for geo-registering post-disaster image data from varied sources to realize the fusion of heterogeneous point cloud and image data for comprehensive damage assessment. The proposed methods were applied on several geospatial data sets collected during Hurricane Sandy. The results are compared with the ground truth which was created by a manual labeling process. The results show that the proposed methods are capable of conducting damage assessment of building structures autonomously and at different resolution and extracting useful damage information to support building performance modeling. Future research of this study will be focused on leveraging high performance computing capabilities to accelerate the damage assessment process.
Subject
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Topic
Civil and Environmental Engineering
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Title
Rutgers University Electronic Theses and Dissertations
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ETD_8604
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electronic resource
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application/pdf
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text/xml
Extent
1 online resource (xv, 218 p. : ill.)
Note
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Ph.D.
Note
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Includes bibliographical references
Subject
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Topic
Computer vision
Subject
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Topic
Structural analysis (Engineering)
Note
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by Zixiang Zhou
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Title
School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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NjNbRU
Identifier
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doi:10.7282/T37947XW
Genre
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ETD doctoral
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Rights
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The author owns the copyright to this work.
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Name
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Zhou
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Zixiang
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Permission or license
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2018-01-02 16:08:57
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Zixiang Zhou
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Rutgers University. School of Graduate Studies
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Author Agreement License
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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.
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2018-01-31
DateTime
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2018-08-02
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after August 2nd, 2018.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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