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An alternative approach to the ground motion prediction problem by a non-parametric adaptive regression method
Date
2014-12-01
Author
Yerlikaya-Ozkurt, Fatma
Askan Gündoğan, Ayşegül
Weber, Gerhard-Wilhelm
Metadata
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Ground Motion Prediction Equations (GMPEs) are empirical relationships which are used for determining the peak ground response at a particular distance from an earthquake source. They relate the peak ground responses as a function of earthquake source type, distance from the source, local site conditions where the data are recorded and finally the depth and magnitude of the earthquake. In this article, a new prediction algorithm, called Conic Multivariate Adaptive Regression Splines (CMARS), is employed on an available dataset for deriving a new GMPE. CMARS is based on a special continuous optimization technique, conic quadratic programming. These convex optimization problems are very well-structured, resembling linear programs and, hence, permitting the use of interior point methods. The CMARS method is performed on the strong ground motion database of Turkey. Results are compared with three other GMPEs. CMARS is found to be effective for ground motion prediction purposes.
Subject Keywords
Conic multivariate adaptive regression splines
,
ground motion prediction equation
,
Non-parametric regression
,
Continuous optimization
,
Engineering seismology
URI
https://hdl.handle.net/11511/47274
Journal
ENGINEERING OPTIMIZATION
DOI
https://doi.org/10.1080/0305215x.2013.858141
Collections
Department of Civil Engineering, Article
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BibTeX
F. Yerlikaya-Ozkurt, A. Askan Gündoğan, and G.-W. Weber, “An alternative approach to the ground motion prediction problem by a non-parametric adaptive regression method,”
ENGINEERING OPTIMIZATION
, pp. 1651–1668, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47274.