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Probabilistic Characterization of Site-Specific Correlation between Geotechnical Parameters Using Limited Site Observation Data
Characterization of correlation between geotechnical parameters is important in engineering practice, particularly in probabilistic assessment and design. However, geotechnical parameters data obtained from field or laboratory tests of a site are usually limited and insufficient to provide a meaningful joint probability distribution of geotechnical parameters or to quantify their correlation. To address this challenge, Markov Chain Monte Carlo (MCMC) simulation-based Bayesian approaches are developed, for probabilistic characterization of site-specific joint probability distribution of geotechnical parameters and quantification of their correlation, using limited site observation data. Consider, for example, two geotechnical parameters X and Y with a correlation coefficient, ρXY between them. In this study, two ways of modelling ρXY are presented, which depend on availability of existing empirical model between X and Y. The proposed approaches probabilistically integrates the limited site-specific observation data pairs of X and Y with prior knowledge under a Bayesian framework. The integrated knowledge is transformed into a large number of X and Y sample pairs using MCMC simulation. Using the generated X and Y sample pairs, ρXY between X and Y is estimated and the marginal distributions of X and Y are evaluated. The approaches are illustrated and validated using real geotechnical data.
Probabilistic Characterization of Site-Specific Correlation between Geotechnical Parameters Using Limited Site Observation Data
Characterization of correlation between geotechnical parameters is important in engineering practice, particularly in probabilistic assessment and design. However, geotechnical parameters data obtained from field or laboratory tests of a site are usually limited and insufficient to provide a meaningful joint probability distribution of geotechnical parameters or to quantify their correlation. To address this challenge, Markov Chain Monte Carlo (MCMC) simulation-based Bayesian approaches are developed, for probabilistic characterization of site-specific joint probability distribution of geotechnical parameters and quantification of their correlation, using limited site observation data. Consider, for example, two geotechnical parameters X and Y with a correlation coefficient, ρXY between them. In this study, two ways of modelling ρXY are presented, which depend on availability of existing empirical model between X and Y. The proposed approaches probabilistically integrates the limited site-specific observation data pairs of X and Y with prior knowledge under a Bayesian framework. The integrated knowledge is transformed into a large number of X and Y sample pairs using MCMC simulation. Using the generated X and Y sample pairs, ρXY between X and Y is estimated and the marginal distributions of X and Y are evaluated. The approaches are illustrated and validated using real geotechnical data.
Probabilistic Characterization of Site-Specific Correlation between Geotechnical Parameters Using Limited Site Observation Data
Emman Aladejare, Adeyemi (author) / Akeju, Oluwatosin Victor (author) / Wang, Yu (author)
Geo-Risk 2017 ; 2017 ; Denver, Colorado
Geo-Risk 2017 ; 81-90
2017-06-01
Conference paper
Electronic Resource
English
Localized probabilistic site characterization in geotechnical engineering
| British Library Conference Proceedings | 1999
| British Library Conference Proceedings | 2018