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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50503

Title: Application of a Support Vector Machine for Liquefaction Assessment
Authors: Ching-Yinn Lee
Shuh-Gi Chern
Contributors: 國立臺灣海洋大學:河海工程學系
Keywords: ANN
Date: 2013-06
Issue Date: 2018-10-12T06:53:54Z
Publisher: Journal of Marine and Technology
Abstract: Abstract: This study presents a support vector machine (SVM)-based approach for predicting earthquake liquefaction. The SVM model database includes five indexes: earthquake magnitude, total overburden pressure, effective overburden pressure, qc values from cone penetration tests (CPT), and peak ground acceleration. The proposed model was trained and tested on a dataset comprising 466 field liquefaction performance records and CPT measurements. A grid search method with k-fold cross-validation was also used to verify the feasibility. Compared with an artificial neural network (ANN)-based method, the SVM-based method has the advantage of increased accuracy and simpler operation. Experimental results show that the proposed SVM approach can increase the classification accuracy rate to a standard of 98.71%.
Relation: 21(3) pp.318-324
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50503
Appears in Collections:[河海工程學系] 期刊論文

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