Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81543
Title: Nonlinear structural modeling using multivariate adaptive regression splines
Authors: Zhang, Wengang
Goh, Anthony Teck Chee
Keywords: Nonlinearity
Basis function
Multivariate adaptive regression splines
Structural analysis
Neural networks
Issue Date: 2015
Source: Zhang, W., & Goh, A. T. C. (2015). Nonlinear structural modeling using multivariate adaptive regression splines. Computers and Concrete, 16(4), 569-585.
Series/Report no.: Computers and Concrete
Abstract: Various 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.
URI: https://hdl.handle.net/10356/81543
http://hdl.handle.net/10220/39605
ISSN: 1598-8198
DOI: 10.12989/cac.2015.16.4.569
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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CEE Journal Articles

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