| Summary: | 碩士 === 國立臺灣大學 === 土木工程學研究所 === 106 === In the design of geotechnical engineering, engineers usually need to obtain local clay parameters from in-situ tests or laboratory tests. However, the data are obtained
from in-situ tests and laboratory tests are incomplete because complete local data are expensive. At this time, the engineer will estimate the clay parameters by the empirical formula which is obtained by the predecessors, but these empirical formulas have large transformation uncertainty. Therefore, a probability density function is found to reduce inaccuracy when the parameter is estimated. In this study, a probability density function
is constructed of site-specific data, but site-specific data are incomplete and sparse. As a result, there is large statistical uncertainty in site-specific model. Bayesian analysis can be used to quantify statistical uncertainty. In order to reduce statistical uncertainty, site-specific probability density function and the generic probability density function are hybridized. There is an effect that the hybrid probability density function is governed by the site-specific probability density function, when the site-specific data are abundant. The hybrid probability density function is governed by the generic probability density function, when the site-specific data are sparse. In this study, eight cases were used to verify the effect of the hybrid probability density function and each case has missing data. The eight cases are located in Brazil, China, Canada, South Korea, the United States, Australia, and Singapore.
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