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Subsurface Simulation Using Stochastic Modeling Techniques for Reliability Based Design of Geo-structures

Li, Zhao
http://rave.ohiolink.edu/etdc/view?acc_num=akron1466691128

Abstract Details

2016, Doctor of Philosophy, University of Akron, Civil Engineering.
Obtaining adequate and accurate subsurface lithological stratification is an essential and the first task in solving many geotechnical engineering problems. However, due to limited field observations constraint by geotechnical investigation techniques and project budget, inference of subsurface stratigraphic structure unavoidably involves various degree of uncertainty. To obtain better understanding of the uncertain subsurface stratigraphic structure, there is a need to describe stratigraphic structure in a probabilistic manner, and to estimate the stratigraphic uncertainty with a quantitative measure. For this end, a stochastic geological modeling framework is proposed in this study to generate possible stratigraphic configurations conditional on available site investigation data, and further develop a compatible uncertainty quantification procedure for estimating the stratigraphic uncertainty. The developed stochastic geological modeling framework, by employing Markov random field with a specific spatial correlation, is intended to describe the inherent heterogeneous, anisotropic and non-stationary characteristics of stratigraphic configurations. In particular, a potential function by means of a local neighborhood system was introduced to account for spatial correlations of lithological units and strata extensions. On the basis of the proposed stochastic geological modeling framework, an uncertainty quantification procedure is established to provide quantitative estimation of the stratigraphic uncertainty. The sensitivity analysis of the proposed geological model is conducted to reveal the influence of mesh density and the model parameter on the simulation results. Bayesian inferential framework is introduced to allow for the estimation of the posterior distribution of model parameter, when additional or subsequent borehole information becomes available. Furthermore, the uncertainties associated with the interpretation of lithological profiles and the spatial variation of soil properties for each identified lithological unit may be significant and should be considered in the geotechnical design. An integrated approach is proposed for probabilistic analysis and design of geotechnical structures that consider both sources of uncertainty by utilizing a Markov random field (MRF) for stochastic modeling of the stratigraphic profile and a Gaussian random field (GRF) for modeling the spatially varying soil properties within each lithological unit. The detailed simulation procedure in the framework of MRF and GRF are described. Finally, since various sources of uncertainties exist during the design of geotechnical structures, such as the uncertainties from stratigraphic structure, soil properties, external loading, design methodology, and performance requirements, how to handle these uncertainties becomes a challenge for geotechnical engineers. An integrated approach is also proposed to handle all these uncertainties comprehensively and systematically and incorporates these uncertainties into reliability analysis of deep foundation.
Robert Liang (Advisor)
Malena Espanol (Committee Member)
Chang Ye (Committee Member)
Junliang Tao (Committee Member)
Zhe Luo (Committee Member)
181 p.
Civil Engineering
Subsurface simulation; Markov random field; Reliability-based design; geotechnical structures

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Citations

  • Li, Z. (2016). Subsurface Simulation Using Stochastic Modeling Techniques for Reliability Based Design of Geo-structures [Doctoral dissertation, University of Akron]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=akron1466691128

    APA Style (7th edition)

  • Li, Zhao. Subsurface Simulation Using Stochastic Modeling Techniques for Reliability Based Design of Geo-structures. 2016. University of Akron, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=akron1466691128.

    MLA Style (8th edition)

  • Li, Zhao. "Subsurface Simulation Using Stochastic Modeling Techniques for Reliability Based Design of Geo-structures." Doctoral dissertation, University of Akron, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=akron1466691128

    Chicago Manual of Style (17th edition)

akron1466691128
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© 2016, all rights reserved.
This open access ETD is published by University of Akron and OhioLINK.