Title:
Automated safety analysis of construction site activities using spatio-temporal data

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dc.contributor.advisor Teizer, Jochen
dc.contributor.author Cheng, Tao en_US
dc.contributor.committeeMember Charles Eastman
dc.contributor.committeeMember Ioannis Brilakis
dc.contributor.committeeMember Lawrence F. Kahn
dc.contributor.committeeMember Patricio A. Vela
dc.contributor.department Civil and Environmental Engineering en_US
dc.date.accessioned 2013-06-15T02:42:19Z
dc.date.available 2013-06-15T02:42:19Z
dc.date.issued 2013-03-26 en_US
dc.description.abstract During the past 10 years, construction was the leading industry of occupational fatalities when compared to other goods producing industries in the US. This is partially attributed to ineffective safety management strategies, specifically lack of automated construction equipment and worker monitoring. Currently, worker safety performance is measured and recorded manually, assessed subjectively, and the resulting performance information is infrequently shared among selected or all project stakeholders. Accurate and emerging remote sensing technology provides critical spatio-temporal data that has the potential to automate and advance the safety monitoring of construction processes. This doctoral research focuses on pro-active safety utilizing radio-frequency location tracking (Ultra Wideband) and real-time three-dimensional (3D) immersive data visualization technologies. The objective of the research is to create a model that can automatically analyze the spatio-temporal data of the main construction resources (personnel, materials, and equipment), and automatically measure, assess, and visualize worker's safety performance. The research scope is limited to human-equipment interaction in a complex construction site layout where proximities among construction resources are omnipresent. In order to advance the understanding of human-equipment proximity issues, extensive data has been collected in various field trials and from projects with multiple scales. Computational algorithms developed in this research process the data to provide spatio-temporal information that is crucial for construction activity monitoring and analysis. Results indicate that worker's safety performance of selected activities can be automatically and objectively measured using the developed model. The major contribution of this research is the creation of a proximity hazards assessment model to automatically analyze spatio-temporal data of construction resources, and measure, evaluate, and visualize their safety performance. This research will significantly contribute to transform safety measures in construction industry, as it can determine and communicate automatically safe and unsafe conditions to various project participants located on the field or remotely. en_US
dc.description.degree PhD en_US
dc.identifier.uri http://hdl.handle.net/1853/47564
dc.publisher Georgia Institute of Technology en_US
dc.subject Proximity en_US
dc.subject Visualization en_US
dc.subject Real-time location tracking en_US
dc.subject Construction safety en_US
dc.subject.lcsh Data mining
dc.subject.lcsh Construction industry Research
dc.subject.lcsh Construction industry Safety measures
dc.subject.lcsh Construction industry Statistics
dc.title Automated safety analysis of construction site activities using spatio-temporal data en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.corporatename School of Civil and Environmental Engineering
local.contributor.corporatename College of Engineering
relation.isOrgUnitOfPublication 88639fad-d3ae-4867-9e7a-7c9e6d2ecc7c
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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