dc.contributor.authorZhang, Wengang
dc.contributor.authorGoh, Anthony Teck Chee
dc.identifier.citationZhang, W., & Goh, A. T. C. (2015). Nonlinear structural modeling using multivariate adaptive regression splines. Computers and Concrete, 16(4), 569-585.en_US
dc.description.abstractVarious computational tools are available for modeling highly nonlinear structural engineering problems that lack a precise analytical theory or understanding of the phenomena involved. This paper adopts a fairly simple nonparametric adaptive regression algorithm known as multivariate adaptive regression splines (MARS) to model the nonlinear interactions between variables. The MARS method makes no specific assumptions about the underlying functional relationship between the input variables and the response. Details of MARS methodology and its associated procedures are introduced first, followed by a number of examples including three practical structural engineering problems. These examples indicate that accuracy of the MARS prediction approach. Additionally, MARS is able to assess the relative importance of the designed variables. As MARS explicitly defines the intervals for the input variables, the model enables engineers to have an insight and understanding of where significant changes in the data may occur. An example is also presented to demonstrate how the MARS developed model can be used to carry out structural reliability analysis.en_US
dc.format.extent17 p.en_US
dc.relation.ispartofseriesComputers and Concreteen_US
dc.rights© 2015 Techno-Press. This paper was published in Computers & Concrete and is made available as an electronic reprint (preprint) with permission of Techno-Press. The published version is available at: [http://dx.doi.org/10.12989/cac.2015.16.4.569]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.en_US
dc.subjectMultivariate adaptive regression splinesen_US
dc.subjectStructural analysis
dc.subjectBasis function
dc.subjectNeural networks
dc.titleNonlinear structural modeling using multivariate adaptive regression splinesen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Civil and Environmental Engineeringen_US
dc.description.versionPublished versionen_US

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