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Erschienen in: Bulletin of Engineering Geology and the Environment 1/2018

30.07.2016 | Original Paper

Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi-dataset study

verfasst von: Nhat-Duc Hoang, Dieu Tien Bui

Erschienen in: Bulletin of Engineering Geology and the Environment | Ausgabe 1/2018

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Abstract

Assessment of the earthquake-induced liquefaction potential is a critical concern in design processes of construction projects. This study proposes a novel soft computing model with a hierarchical structure for evaluating earthquake-induced soil liquefaction. The new approach, named KFDA-LSSVM, combines kernel Fisher discriminant analysis (KFDA) with a least squares support vector machine (LSSVM). Based on the original data set, KFDA is used as a first-level analysis to construct an additional feature that best represents the data structure with consideration of different class labels. In the next level of analysis, based on such additional features and the original features, LSSVM generalizes a classification boundary that separates the learning space into two decision domains: “liquefaction” and “non-liquefaction.” Three data sets of liquefaction records have been used to train and verify the proposed method. The model performance is reliably assessed via a repeated sub-sampling process. Experimental results supported by the Wilcoxon signed-rank test demonstrate significant improvements of the hybrid framework over other benchmark approaches.

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Metadaten
Titel
Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi-dataset study
verfasst von
Nhat-Duc Hoang
Dieu Tien Bui
Publikationsdatum
30.07.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Bulletin of Engineering Geology and the Environment / Ausgabe 1/2018
Print ISSN: 1435-9529
Elektronische ISSN: 1435-9537
DOI
https://doi.org/10.1007/s10064-016-0924-0

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