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oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/151862026-10-18T20:29:58Zopenairedrivercom_ebyu_10000col_ebyu_10001

Site characterization using GP, MARS and GPRSamui, PijushDalkiliç, YildirimJagan, J.C (programming language)Genetic algorithmsGenetic programmingFunctional relationshipGaussian process regressionMultivariate adaptive regression splinesNon-parametric modelRegression techniquesSite characterizationStandard penetration testThree-dimensional analysisRegression analysisThis 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.Springer International Publishing2015Book Chapterhttps://rdlopen.rawdatalibrary.net/r/ebyu/15186https://doi.org/10.1007/978-3-319-20883-1_13978-331920883-1; 978-331920882-4enHandbook of Genetic Programming Applicationsinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/bookParthttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/15186