Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89975
Title: Evaluating seismic liquefaction potential using multivariate adaptive regression splines and logistic regression
Authors: Zhang, Wengang
Goh, Anthony Teck Chee
Keywords: Multivariate Adaptive Regression Splines
Logistic Regression
DRNTU::Engineering::Civil engineering
Issue Date: 2016
Source: Zhang, W., & Goh, A. T. (2016). Evaluating seismic liquefaction potential using multivariate adaptive regression splines and logistic regression. Geomechanics and Engineering, 10(3), 269-284. doi:10.12989/gae.2016.10.3.269
Series/Report no.: Geomechanics and Engineering
Abstract: Simplified techniques based on in situ testing methods are commonly used to assess seismic liquefaction potential. Many of these simplified methods were developed by analyzing liquefaction case histories from which the liquefaction boundary (limit state) separating two categories (the occurrence or non-occurrence of liquefaction) is determined. As the liquefaction classification problem is highly nonlinear in nature, it is difficult to develop a comprehensive model using conventional modeling techniques that take into consideration all the independent variables, such as the seismic and soil properties. In this study, a modification of the Multivariate Adaptive Regression Splines (MARS) approach based on Logistic Regression (LR) LR_MARS is used to evaluate seismic liquefaction potential based on actual field records. Three different LR_MARS models were used to analyze three different field liquefaction databases and the results are compared with the neural network approaches. The developed spline functions and the limit state functions obtained reveal that the LR_MARS models can capture and describe the intrinsic, complex relationship between seismic parameters, soil parameters, and the liquefaction potential without having to make any assumptions about the underlying relationship between the various variables. Considering its computational efficiency, simplicity of interpretation, predictive accuracy, its data-driven and adaptive nature and its ability to map the interaction between variables, the use of LR_MARS model in assessing seismic liquefaction potential is promising.
URI: https://hdl.handle.net/10356/89975
http://hdl.handle.net/10220/46449
ISSN: 2092-6219
DOI: 10.12989/gae.2016.10.3.269
Rights: © 2016 Techno-Press, Ltd. This paper was published in Geomechanics and Engineering and is made available as an electronic reprint (preprint) with permission of Techno-Press, Ltd. The published version is available at: [http://dx.doi.org/10.12989/gae.2016.10.3.269]. 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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