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- W3025641539 endingPage "105675" @default.
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- W3025641539 abstract "Abstract In this study, a database is compiled for 11 clay parameters covering 50 sites in Shanghai with 4051 data points (labeled as SH-CLAY/11/4051). These sites are distributed in the eight districts of Shanghai covering an area of 145 km2. This is the first sizeable multivariate soil database compiled at the municipal level. General information, including the geology of Shanghai, the borehole locations and depths, the types of parameters, and the basic statistics associated with these parameters are presented first. The quality of the compiled data is then assessed by comparing them with the data in the published global database CLAY/10/7490 with respect to marginal statistics. Results show that data points in SH-CLAY/11/4051 fall within the range of CLAY/10/7490. It is not surprising that local Shanghai data are more clustered than those in CLAY/10/7490. The range of values spanned by each clay parameter is smaller at a regional/municipal scale than at the global scale. A preliminary study to understand what is “unique” in Shanghai clay data is conducted by comparing global correlations between any two parameters with the corresponding Shanghai versions. Some correlations found in SH-CLAY/11/4051 (e.g., normalized effective vertical stress and normalized undrained shear strength) are observed to be similar to those found in CLAY/10/7490. However, there are other distinct correlations produced by SH-CLAY/11/4051, indicating a regional/municipal effect. This study constructs a multivariate probability distribution based on SH-CLAY/11/4051 to capture these distinct correlations between Shanghai clay parameters. One useful application of this multivariate distribution is act as prior for Bayesian updating." @default.
- W3025641539 created "2020-05-21" @default.
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- W3025641539 date "2020-08-01" @default.
- W3025641539 modified "2023-10-17" @default.
- W3025641539 title "Multivariate probability distribution of Shanghai clay properties" @default.
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- W3025641539 doi "https://doi.org/10.1016/j.enggeo.2020.105675" @default.
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