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

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, comb...

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Bibliographic Details
Published in:Bulletin of engineering geology and the environment 2018-02, Vol.77 (1), p.191-204
Main Authors: Hoang, Nhat-Duc, Bui, Dieu Tien
Format: Article
Language:English
Subjects:
Analysis
Construction
Construction industry
Data
Data structures
Discriminant analysis
Earth and Environmental Science
Earth Sciences
Earthquake prediction
Earthquakes
Foundations
Frameworks
Geoecology/Natural Processes
Geoengineering
Geological engineering
Geotechnical Engineering & Applied Earth Sciences
Hydraulics
Least squares
Liquefaction
Nature Conservation
Original Paper
Project engineering
Seismic activity
Seismic engineering
Seismic response
Soft computing
Soil
Soil structure
Structural hierarchy
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Summary: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.
ISSN:1435-9529
1435-9537
DOI:10.1007/s10064-016-0924-0