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Site characterization using GP, MARS and GPR

Book ChapterAçık Erişim

Özet

This article examines the capability of Genetic Programming (GP), Multivariate Adaptive Regression Spline (MARS) and Gaussian Process Regression (GPR) for developing site characterization model of Bangalore (India) based on corrected Standard Penetration Test (SPT) value (Nc). GP, MARS and GPR have been used as regression techniques. GP is developed based on genetic algorithm. MARS does not assume any functional relationship between input and output variables. GPR is a probabilistic, non-parametric model. In GPR, different kinds of prior knowledge can be applied. In three dimensional analysis, the function. © Springer International Publishing Switzerland 2015.

Anahtar Kelimeler

C (programming language)Genetic algorithmsGenetic programmingFunctional relationshipGaussian process regressionMultivariate adaptive regression splinesNon-parametric modelRegression techniquesSite characterizationStandard penetration testThree-dimensional analysisRegression analysis
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Tam Kayıt
AlanDeğerDil
dc.contributor.authorSamui, Pijush
dc.contributor.authorDalkiliç, Yildirim
dc.contributor.authorJagan, J.
dc.date.accessioned2026-10-18T20:29:58.447620520Z
dc.date.available2026-10-18T20:29:58.447620520Z
dc.date.issued2015
dc.description.abstractThis article examines the capability of Genetic Programming (GP), Multivariate Adaptive Regression Spline (MARS) and Gaussian Process Regression (GPR) for developing site characterization model of Bangalore (India) based on corrected Standard Penetration Test (SPT) value (Nc). GP, MARS and GPR have been used as regression techniques. GP is developed based on genetic algorithm. MARS does not assume any functional relationship between input and output variables. GPR is a probabilistic, non-parametric model. In GPR, different kinds of prior knowledge can be applied. In three dimensional analysis, the function. © Springer International Publishing Switzerland 2015.
dc.identifier.doi10.1007/978-3-319-20883-1_13
dc.identifier.endpage357
dc.identifier.isbn978-331920883-1; 978-331920882-4
dc.identifier.scopus2-s2.0-84957659358
dc.identifier.startpage345
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/ebyu/15186
dc.language.isoen
dc.publisherSpringer International Publishing
dc.relation.ispartofHandbook of Genetic Programming Applications
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectC (programming language)
dc.subjectGenetic algorithms
dc.subjectGenetic programming
dc.subjectFunctional relationship
dc.subjectGaussian process regression
dc.subjectMultivariate adaptive regression splines
dc.subjectNon-parametric model
dc.subjectRegression techniques
dc.subjectSite characterization
dc.subjectStandard penetration test
dc.subjectThree-dimensional analysis
dc.subjectRegression analysis
dc.titleSite characterization using GP, MARS and GPR
dc.typeBook Chapter
dspace.entity.typePublication
rdl.citation.scopus3
rdl.source.indexSCOPUS