LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
PhysicalDescription
Form (authority = marcform)
electronic
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
xv, 195 pages
Abstract (type = abstract)
A wide range of important problems in civil engineering can be classified as inverse problems. In such problems, the observational data related to the performance of a system is known, and the characteristics of the system or the input are sought. There are two general approaches to the solution of inverse problems: deterministic and probabilistic. Traditionally, inverse problems in civil engineering have been solved using a deterministic approach. In this approach, the objective is to find a model of the system that its theoretical response best fits the observed data. In deterministic approach to the solution of inverse problems, it is implicitly assumed that the uncertainties in data and theoretical models are negligible. However, this assumption is not valid in many applications, and therefore, effects of data and modeling uncertainties on the obtained solution should be evaluated. In this dissertation, a general probabilistic approach to the solution of the inverse problems is introduced, which offers the framework required to obtain uncertainty measures for the solution. Techniques for direct analytical evaluation and numerical approximation of the probabilistic solution using Monte Carlo Markov Chains (MCMC), with and without Neighborhood Algorithm (NA) approximation, are introduced and explained. The application of the presented concepts and techniques are then illustrated for three important classes of inverse problems in geotechnical and transportation engineering as application examples. These applications are: Falling Weight Deflectometer (FWD) backcalculation, model calibration based on geotechnical instrument measurements, and seismic waveform inversion for shallow subsurface characterization. For each application, the probabilistic formulation is presented; the solution is obtained; and the advantages of the probabilistic approach are illustrated and discussed.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references.
Subject (authority = RUETD)
Topic
Civil and Environmental Engineering
Subject (authority = LCSH)
Topic
Transportation
Subject (authority = ETD-LCSH)
Topic
Inverse problems (Differential equations)
Subject (authority = ETD-LCSH)
Topic
Civil engineering
Subject (authority = ETD-LCSH)
Topic
Transportation engineering
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TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Genre (authority = ExL-Esploro)
ETD doctoral
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AssociatedEntity (AUTHORITY = rulib); (ID = 1)
Name
Rambod Hadidi
Role
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Affiliation
Rutgers University. Graduate School-New Brunswick
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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.