Home
Resource
Computer vision-based assessment of coastal building structures during hurricane events
PDF
PDF format is widely accepted and good for printing.
Plug-in required
PDF-1(16.76 MB)
Citation & Export
View Usage Statistics
Staff View

Citation & Export
Hide

Simple citation

Zhou, Zixiang. Computer vision-based assessment of coastal building structures during hurricane events. Retrieved from https://doi.org/doi:10.7282/T37947XW

Export

Click here for information about Citation Management Tools at Rutgers.

Statistics
Hide

Description

TitleComputer vision-based assessment of coastal building structures during hurricane events
NameZhou, Zixiang (author); Gong, Jie (chair); Moon, Franklin (internal member); Najm, Husam (internal member); Zhu, Zhigang (outside member); Rutgers University; School of Graduate Studies
Date Created2018
Other Date2018-01 (degree)
SubjectCivil and Environmental Engineering, Computer vision, Structural analysis (Engineering)
Extent1 online resource (xv, 218 p. : ill.)
DescriptionSevere 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.
NotePh.D.
NoteIncludes bibliographical references
Noteby Zixiang Zhou
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T37947XW
Languageeng
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
Version 8.5.5
Rutgers University Libraries - Copyright ©2024