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RUcore Resource Object
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Uniform TitleGeneric probabilistic inversion technique for geotechnical and transportation engineering applications
NameHadidi, Rambod (author), Gucunski, Nenad (chair), Maher, Ali (internal member), Najm, Husam (internal member), Zaghloul, Sameh (outside member), Rutgers University, Graduate School-New Brunswick,
Degree Date2007
Date Created2007
SubjectCivil and Environmental Engineering,
Transportation,
Inverse problems (Differential equations),
Civil engineering,
Transportation engineering
DescriptionA 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.
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[degree] Ph.D.
Note[bibliography] Includes bibliographical references.
Genretheses
Persistent URLhttp://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.13464
LanguageEnglish
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.