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dc.contributor.advisorChen, Zhen, 1958-eng
dc.contributor.authorBewick, Bryan T., 1979-eng
dc.date.issued2010eng
dc.date.submitted2010 Springeng
dc.descriptionTitle from PDF of title page (University of Missouri--Columbia, viewed on October 21, 2010).eng
dc.descriptionThe entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.eng
dc.descriptionDissertation advisor: Dr. Zhen Chen.eng
dc.descriptionVita.eng
dc.descriptionPh. D. University of Missouri--Columbia 2010.eng
dc.description.abstract[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Blast barrier walls have been shown to reduce the blast load on structures in most scenarios. Analysis of existing data for blast barrier response reveals that a need exists to determine the bounds of the problem and produce a fast-running accurate model for the effects of barrier walls on blast wave propagation. Since blast experiments are very time intensive and extremely cost prohibitive, it is vital that computational capabilities be developed to generate the required data set that can be utilized to produce simplified design tools. The combination of high fidelity first principles model-based simulation with artificial neural network techniques for providing solutions to blast barrier problems results in a very effective means to tackle the challenging problem. A review of current methods of modeling blast wave propagation identifies a need for a modeling approach that is both fast and versatile in its scope for application. Artificial neural network approaches to modeling the propagation of blast waves in a built-up environment are developed. A comprehensive study of numerical simulation approaches for modeling blast propagation is presented and applied to populating data for blast barrier site configurations. The proposed approach is demonstrated to estimate the peak pressure, impulse, time of arrival, and time of duration of blast loads on buildings protected by simple barriers, using data generated from validated computational hydrocode simulations. Once verified and validated, the proposed neural-network model-based simulation procedure provides an efficient engineering tool for predicting blast loads on structures which are protected by blast barrier walls.eng
dc.description.bibrefIncludes bibliographical references.eng
dc.format.extentxxiv, 291 pageseng
dc.identifier.merlinb82634798eng
dc.identifier.oclc733766381eng
dc.identifier.urihttps://doi.org/10.32469/10355/10923eng
dc.identifier.urihttps://hdl.handle.net/10355/10923
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcollectionUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.rightsAccess is limited to the campus of the University of Missouri-Columbia.eng
dc.subject.lcshBlast effect -- Equipment and supplieseng
dc.subject.lcshBuildings -- Blast effectseng
dc.subject.lcshBuildings -- Impact testingeng
dc.subject.lcshBuilding, Bombproofeng
dc.titleFirst-principle simulation of blast barrier effectiveness for the development of simplified design toolseng
dc.typeThesiseng
thesis.degree.disciplineCivil engineering (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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