NDLI: Multivariate Adaptive Regression Splines Application for Multivariate Geotechnical Problems with Big Data

Content Provider	Springer Nature Link
Author	Zhang, Wengang Goh, Anthony T. C. Zhang, Yanmei
Copyright Year	2015
Abstract	Despite the rapid increases in processing speed and memory of low-cost computers, the enormous computational costs of running complicated numerical analyses such as finite element simulations makes it impractical to rely exclusively on simulation for the purpose of design optimization since many geotechnical problems are highly nonlinear and multivariate. To reduce the cost, surrogate models, also known as meta-models, are constructed and then used in place of the actual numerical simulation models. To ensure the surrogate model is more reliable, the ranges of the design variables should be as wide as possible. Thus meta-modeling techniques capable of analyzing multivariate problems are desirable. This paper explores the use of a fairly simple nonparametric regression procedure known as multivariate adaptive regression splines (MARS) in approximating the relationship between the inputs and outputs with a big data. First the basis of the MARS methodology and its associated procedures are explained in detail. Then two complicated geotechnical problems are presented to demonstrate the function approximating capabilities of MARS and its efficiency in dealing with multivariate problems involving large amounts of data. This paper demonstrates that the MARS algorithm is capable of producing simple, accurate and easy-to-interpret models and estimating the contributions of the input variables.
Starting Page	193
Ending Page	204
Page Count	12
File Format	PDF
ISSN	09603182
Journal	Geotechnical and Geological Engineering
Volume Number	34
Issue Number	1
e-ISSN	15731529
Language	English
Publisher	Springer International Publishing
Publisher Date	2015-10-23
Publisher Place	Cham
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Multivariate adaptive regression splines Multivariate Surrogate model Big data Geotechnical Engineering & Applied Earth Sciences Hydrogeology Terrestrial Pollution Waste Management/Waste Technology Civil Engineering
Content Type	Text
Resource Type	Article
Subject	Soil Science Geology Architecture Geotechnical Engineering and Engineering Geology

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