Correlations among some clay parameters — the multivariate distribution
This paper constructs a 10-dimensional multivariate probability distribution covering 10 clay parameters. The parameters are the liquid limit, plasticity index (PI), liquidity index, effective vertical stress, undrained shear strength, sensitivity, and three piezocone test parameters. A CLAY/10/7490...
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Veröffentlicht in: | Canadian geotechnical journal 2014-06, Vol.51 (6), p.686-704 |
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description | This paper constructs a 10-dimensional multivariate probability distribution covering 10 clay parameters. The parameters are the liquid limit, plasticity index (PI), liquidity index, effective vertical stress, undrained shear strength, sensitivity, and three piezocone test parameters. A CLAY/10/7490 database is compiled in a companion paper for this purpose. The database consists of 7490 data points from 251 studies. The number of data points associated with each study varies from 1 to 419 with an average 30 data points per study. The clay properties cover a wide range of overconsolidation ratios (but mostly 1∼10), a wide range of sensitivity (S
t
) (sites with S
t
= 1∼tens or hundreds are fairly typical), and a wide range of PI (but mostly 8∼100). The constructed multivariate probability distribution can be used as a prior distribution to derive the joint distribution of design parameters based on limited but site-specific field data. Note that the entire joint distribution of the 10 clay parameters is derived, not marginal distributions or simply means and coefficients of variation. These multiple design parameters can be updated from multiple field measurements, which is more useful than updating one design parameter using one field measurement that is typical in current practice. This paper also demonstrates that it is practical to build multivariate probability models by combining available bivariate models, which are prevalent in the geotechnical engineering correlation literature. The proposed approach circumvents the need to collect multivariate data, which are rarely found in typical site investigation programs. |
doi_str_mv | 10.1139/cgj-2013-0353 |
format | Article |
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t
) (sites with S
t
= 1∼tens or hundreds are fairly typical), and a wide range of PI (but mostly 8∼100). The constructed multivariate probability distribution can be used as a prior distribution to derive the joint distribution of design parameters based on limited but site-specific field data. Note that the entire joint distribution of the 10 clay parameters is derived, not marginal distributions or simply means and coefficients of variation. These multiple design parameters can be updated from multiple field measurements, which is more useful than updating one design parameter using one field measurement that is typical in current practice. This paper also demonstrates that it is practical to build multivariate probability models by combining available bivariate models, which are prevalent in the geotechnical engineering correlation literature. The proposed approach circumvents the need to collect multivariate data, which are rarely found in typical site investigation programs.</description><identifier>ISSN: 0008-3674</identifier><identifier>EISSN: 1208-6010</identifier><identifier>DOI: 10.1139/cgj-2013-0353</identifier><identifier>CODEN: CGJOAH</identifier><language>eng</language><publisher>Ottawa, ON: NRC Research Press</publisher><subject>base de données ; Clay ; Clay (material) ; clay properties ; Construction ; Correlation ; Correlation analysis ; correlations ; corrélations ; Data points ; Design ; Design parameters ; distribution multivariée ; Earth sciences ; Earth, ocean, space ; Engineering and environment geology. Geothermics ; Engineering geology ; Exact sciences and technology ; Liquid limits ; Liquidity ; Mathematical models ; Measurement ; Mechanical properties ; Multivariate analysis ; multivariate distribution ; Probability distribution ; propriétés de l’argile ; Shear strength ; Soil mechanics ; Soil research ; statistics ; statistiques</subject><ispartof>Canadian geotechnical journal, 2014-06, Vol.51 (6), p.686-704</ispartof><rights>2015 INIST-CNRS</rights><rights>COPYRIGHT 2014 NRC Research Press</rights><rights>Copyright Canadian Science Publishing NRC Research Press Jun 2014</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a691t-99c928c822fab640f3e5c10fe6e24484974940514a070356fe8427c98f2b09f43</citedby><cites>FETCH-LOGICAL-a691t-99c928c822fab640f3e5c10fe6e24484974940514a070356fe8427c98f2b09f43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://cdnsciencepub.com/doi/pdf/10.1139/cgj-2013-0353$$EPDF$$P50$$Gnrcresearch$$H</linktopdf><linktohtml>$$Uhttps://cdnsciencepub.com/doi/full/10.1139/cgj-2013-0353$$EHTML$$P50$$Gnrcresearch$$H</linktohtml><link.rule.ids>315,782,786,2934,27931,27932,64435,65241</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28615988$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>JIANYE CHING</creatorcontrib><creatorcontrib>PHOON, Kok-Kwang</creatorcontrib><title>Correlations among some clay parameters — the multivariate distribution</title><title>Canadian geotechnical journal</title><description>This paper constructs a 10-dimensional multivariate probability distribution covering 10 clay parameters. The parameters are the liquid limit, plasticity index (PI), liquidity index, effective vertical stress, undrained shear strength, sensitivity, and three piezocone test parameters. A CLAY/10/7490 database is compiled in a companion paper for this purpose. The database consists of 7490 data points from 251 studies. The number of data points associated with each study varies from 1 to 419 with an average 30 data points per study. The clay properties cover a wide range of overconsolidation ratios (but mostly 1∼10), a wide range of sensitivity (S
t
) (sites with S
t
= 1∼tens or hundreds are fairly typical), and a wide range of PI (but mostly 8∼100). The constructed multivariate probability distribution can be used as a prior distribution to derive the joint distribution of design parameters based on limited but site-specific field data. Note that the entire joint distribution of the 10 clay parameters is derived, not marginal distributions or simply means and coefficients of variation. These multiple design parameters can be updated from multiple field measurements, which is more useful than updating one design parameter using one field measurement that is typical in current practice. This paper also demonstrates that it is practical to build multivariate probability models by combining available bivariate models, which are prevalent in the geotechnical engineering correlation literature. The proposed approach circumvents the need to collect multivariate data, which are rarely found in typical site investigation programs.</description><subject>base de données</subject><subject>Clay</subject><subject>Clay (material)</subject><subject>clay properties</subject><subject>Construction</subject><subject>Correlation</subject><subject>Correlation analysis</subject><subject>correlations</subject><subject>corrélations</subject><subject>Data points</subject><subject>Design</subject><subject>Design parameters</subject><subject>distribution multivariée</subject><subject>Earth sciences</subject><subject>Earth, ocean, space</subject><subject>Engineering and environment geology. Geothermics</subject><subject>Engineering geology</subject><subject>Exact sciences and technology</subject><subject>Liquid limits</subject><subject>Liquidity</subject><subject>Mathematical models</subject><subject>Measurement</subject><subject>Mechanical properties</subject><subject>Multivariate analysis</subject><subject>multivariate distribution</subject><subject>Probability distribution</subject><subject>propriétés de l’argile</subject><subject>Shear strength</subject><subject>Soil mechanics</subject><subject>Soil research</subject><subject>statistics</subject><subject>statistiques</subject><issn>0008-3674</issn><issn>1208-6010</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNqV0kuLFDEQB_BGFBxXj94bRdBDr3l1Ojkug4-BRcHHOdRkK70ZujuzSVrcmx_CT-gnMe0u6srAIjkkhF_9oYqqqseUHFPK9Uvb7xpGKG8Ib_mdakUZUY0klNytVoSUN5eduF89SGlHCBWCsVW1WYcYcYDsw5RqGMPU1ymMWNsBLus9RBgxY0z1j2_f63yO9TgP2X-B6CFjfeZTjn47L9UPq3sOhoSPru-j6vPrV5_Wb5vT928265PTBqSmudHaaqasYszBVgriOLaWEocSmRBK6E5oQVoqgHSlD-lQCdZZrRzbEu0EP6qeX-XuY7iYMWUz-mRxGGDCMCdDZUfbjrMyg1tp21Ii2k4t9Ok_dBfmOJVGiuJSKyq5_KN6GND4yYUcwS6h5oR3khHZ_spqDqgeJ4wwhAmdL983_JMD3u79hfkbHR9A5Zzh6O3B1Bc3CorJ-DX3MKdkNh8__Id9d7A7G0NKEZ3ZRz9CvDSUmGUPTdlDs-yhWfaw-GfXk4VkYXARJuvT7yKmJG21UsWRKzdFGzEhRHt-S_RPLB7m4A</recordid><startdate>20140601</startdate><enddate>20140601</enddate><creator>JIANYE CHING</creator><creator>PHOON, Kok-Kwang</creator><general>NRC Research Press</general><general>National Research Council of Canada</general><general>Canadian Science Publishing NRC Research Press</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>ISR</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope></search><sort><creationdate>20140601</creationdate><title>Correlations among some clay parameters — the multivariate distribution</title><author>JIANYE CHING ; PHOON, Kok-Kwang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a691t-99c928c822fab640f3e5c10fe6e24484974940514a070356fe8427c98f2b09f43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>base de données</topic><topic>Clay</topic><topic>Clay (material)</topic><topic>clay properties</topic><topic>Construction</topic><topic>Correlation</topic><topic>Correlation analysis</topic><topic>correlations</topic><topic>corrélations</topic><topic>Data points</topic><topic>Design</topic><topic>Design parameters</topic><topic>distribution multivariée</topic><topic>Earth sciences</topic><topic>Earth, ocean, space</topic><topic>Engineering and environment geology. Geothermics</topic><topic>Engineering geology</topic><topic>Exact sciences and technology</topic><topic>Liquid limits</topic><topic>Liquidity</topic><topic>Mathematical models</topic><topic>Measurement</topic><topic>Mechanical properties</topic><topic>Multivariate analysis</topic><topic>multivariate distribution</topic><topic>Probability distribution</topic><topic>propriétés de l’argile</topic><topic>Shear strength</topic><topic>Soil mechanics</topic><topic>Soil research</topic><topic>statistics</topic><topic>statistiques</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>JIANYE CHING</creatorcontrib><creatorcontrib>PHOON, Kok-Kwang</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>Canadian geotechnical journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>JIANYE CHING</au><au>PHOON, Kok-Kwang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Correlations among some clay parameters — the multivariate distribution</atitle><jtitle>Canadian geotechnical journal</jtitle><date>2014-06-01</date><risdate>2014</risdate><volume>51</volume><issue>6</issue><spage>686</spage><epage>704</epage><pages>686-704</pages><issn>0008-3674</issn><eissn>1208-6010</eissn><coden>CGJOAH</coden><abstract>This paper constructs a 10-dimensional multivariate probability distribution covering 10 clay parameters. The parameters are the liquid limit, plasticity index (PI), liquidity index, effective vertical stress, undrained shear strength, sensitivity, and three piezocone test parameters. A CLAY/10/7490 database is compiled in a companion paper for this purpose. The database consists of 7490 data points from 251 studies. The number of data points associated with each study varies from 1 to 419 with an average 30 data points per study. The clay properties cover a wide range of overconsolidation ratios (but mostly 1∼10), a wide range of sensitivity (S
t
) (sites with S
t
= 1∼tens or hundreds are fairly typical), and a wide range of PI (but mostly 8∼100). The constructed multivariate probability distribution can be used as a prior distribution to derive the joint distribution of design parameters based on limited but site-specific field data. Note that the entire joint distribution of the 10 clay parameters is derived, not marginal distributions or simply means and coefficients of variation. These multiple design parameters can be updated from multiple field measurements, which is more useful than updating one design parameter using one field measurement that is typical in current practice. This paper also demonstrates that it is practical to build multivariate probability models by combining available bivariate models, which are prevalent in the geotechnical engineering correlation literature. The proposed approach circumvents the need to collect multivariate data, which are rarely found in typical site investigation programs.</abstract><cop>Ottawa, ON</cop><pub>NRC Research Press</pub><doi>10.1139/cgj-2013-0353</doi><tpages>19</tpages></addata></record> |
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subjects | base de données Clay Clay (material) clay properties Construction Correlation Correlation analysis correlations corrélations Data points Design Design parameters distribution multivariée Earth sciences Earth, ocean, space Engineering and environment geology. Geothermics Engineering geology Exact sciences and technology Liquid limits Liquidity Mathematical models Measurement Mechanical properties Multivariate analysis multivariate distribution Probability distribution propriétés de l’argile Shear strength Soil mechanics Soil research statistics statistiques |
title | Correlations among some clay parameters — the multivariate distribution |
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