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Article: Bayesian probabilistic approach for the correlations of compression index for marine clays

TitleBayesian probabilistic approach for the correlations of compression index for marine clays
Authors
KeywordsBayesian analysis
Correlation
Marine clays
Probability
Soil compression
Issue Date2010
PublisherAmerican Society of Civil Engineers. The Journal's web site is located at http://www.pubs.asce.org/journals/gt.html
Citation
Journal Of Geotechnical And Geoenvironmental Engineering, 2010, v. 135 n. 12, p. 1932-1940 How to Cite?
AbstractThe compression index is an important soil property that is essential to many geotechnical designs. Over the decades, a number of empirical correlations have been proposed to relate the compressibility to other soil index properties, such as the liquid limit, plasticity index, in situ water content, void ratio, specific gravity, etc. The reliability and thus predictability of these correlations are always being questioned. Moreover, selection between simple and complicated models is a difficult task and often depends on subjective judgments. A more complicated model obviously provides "better fit" to the data but not necessarily offers an acceptable degree of robustness to measurement noise and modeling error. In the present study, the Bayesian probabilistic approach for model class selection is used to revisit the empirical multivariate linear regression formula of the compression index. The criterion in the formula structure selection is based on the plausibility of a class of formulas conditional on the measurement, instead of considering the likelihood only. The plausibility balances between the data fitting capability and sensitivity to measurement and modeling error, which is quantified by the Ockham factor. The Bayesian method is applied to analyze a data set of 795 records, including the compression index and other well-known geotechnical index properties of marine clay samples collected from various sites in South Korea. It turns out that the correlation formula linking the compression index to the initial void ratio and liquid limit possesses the highest plausibility among a total of 18 candidate classes of formulas. The physical significance of this most plausible correlation is addressed. It turns out to be consistent with previous studies and the Bayesian method provides the confirmation from another angle. © 2009 ASCE.
Persistent Identifierhttp://hdl.handle.net/10722/91203
ISSN
2015 Impact Factor: 1.696
2015 SCImago Journal Rankings: 2.344
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorYan, WMen_HK
dc.contributor.authorYuen, KVen_HK
dc.contributor.authorYoon, GLen_HK
dc.date.accessioned2010-09-17T10:14:50Z-
dc.date.available2010-09-17T10:14:50Z-
dc.date.issued2010en_HK
dc.identifier.citationJournal Of Geotechnical And Geoenvironmental Engineering, 2010, v. 135 n. 12, p. 1932-1940en_HK
dc.identifier.issn1090-0241en_HK
dc.identifier.urihttp://hdl.handle.net/10722/91203-
dc.description.abstractThe compression index is an important soil property that is essential to many geotechnical designs. Over the decades, a number of empirical correlations have been proposed to relate the compressibility to other soil index properties, such as the liquid limit, plasticity index, in situ water content, void ratio, specific gravity, etc. The reliability and thus predictability of these correlations are always being questioned. Moreover, selection between simple and complicated models is a difficult task and often depends on subjective judgments. A more complicated model obviously provides "better fit" to the data but not necessarily offers an acceptable degree of robustness to measurement noise and modeling error. In the present study, the Bayesian probabilistic approach for model class selection is used to revisit the empirical multivariate linear regression formula of the compression index. The criterion in the formula structure selection is based on the plausibility of a class of formulas conditional on the measurement, instead of considering the likelihood only. The plausibility balances between the data fitting capability and sensitivity to measurement and modeling error, which is quantified by the Ockham factor. The Bayesian method is applied to analyze a data set of 795 records, including the compression index and other well-known geotechnical index properties of marine clay samples collected from various sites in South Korea. It turns out that the correlation formula linking the compression index to the initial void ratio and liquid limit possesses the highest plausibility among a total of 18 candidate classes of formulas. The physical significance of this most plausible correlation is addressed. It turns out to be consistent with previous studies and the Bayesian method provides the confirmation from another angle. © 2009 ASCE.en_HK
dc.languageengen_HK
dc.publisherAmerican Society of Civil Engineers. The Journal's web site is located at http://www.pubs.asce.org/journals/gt.htmlen_HK
dc.relation.ispartofJournal of Geotechnical and Geoenvironmental Engineeringen_HK
dc.rightsJournal of Geotechnical and Geoenvironmental Engineering. Copyright © American Society of Civil Engineers.-
dc.subjectBayesian analysisen_HK
dc.subjectCorrelationen_HK
dc.subjectMarine claysen_HK
dc.subjectProbabilityen_HK
dc.subjectSoil compressionen_HK
dc.titleBayesian probabilistic approach for the correlations of compression index for marine claysen_HK
dc.typeArticleen_HK
dc.identifier.emailYan, WM:ryanyan@hku.hken_HK
dc.identifier.authorityYan, WM=rp01400en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1061/(ASCE)GT.1943-5606.0000157en_HK
dc.identifier.scopuseid_2-s2.0-75949102267en_HK
dc.identifier.hkuros168270-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-75949102267&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume135en_HK
dc.identifier.issue12en_HK
dc.identifier.spage1932en_HK
dc.identifier.epage1940en_HK
dc.identifier.eissn1943-5606-
dc.identifier.isiWOS:000272181800015-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridYan, WM=35369531200en_HK
dc.identifier.scopusauthoridYuen, KV=7202333739en_HK
dc.identifier.scopusauthoridYoon, GL=7103257922en_HK

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