Application of genetic algorithm-based support vector machines for prediction of soil liquefaction

被引:24
|
作者
Xue, Xinhua [1 ,2 ]
Xiao, Ming [2 ]
机构
[1] Sichuan Univ, Coll Water Resource & Hydropower, State Key Lab Hydraul & Mt River Engn, 24 South Sect 1,Yihuan Rd, Chengdu 610065, Sichuan, Peoples R China
[2] Penn State Univ, Dept Civil & Environm Engn, University Pk, PA 16802 USA
关键词
Soil liquefaction; Support vector machine; Genetic algorithm; Grid search; Liquefaction; Classification accuracy; STANDARD PENETRATION TEST; NEURAL-NETWORK; AUTOMATIC DETECTION; MODULUS; SANDS;
D O I
10.1007/s12665-016-5673-7
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper presents a hybrid genetic algorithm (GA) and support vector machine (SVM) techniques to predict the potential of soil liquefaction. GA is employed in selecting the optimal values of the kernel function and the penalty parameter in SVM model to improve the forecasting accuracy. The database used in this study includes 109 CPT-based field observations from five major earthquakes between 1964 and 1983. Several important parameters, including the cone resistance, total vertical stress, effective vertical stress, mean grain size, normalized peak horizontal acceleration at ground surface, cyclic stress ratio, and earthquake magnitude, were used as the input parameters, while the potential of soil liquefaction was the output parameter. The predictions from the GA-SVM model were compared with those from three methods: grid search (GS) method, artificial neural network (ANN) model, and C4.5 decision tree approach. The overall classification success rates for the entire dataset predicted by GA-SVM, ANN, C4.5 decision tree, and GS-SVM models are 97.25, 97.2, 96.3, and 92.66 %, respectively. The study concluded that the proposed GA-SVM model improves the classification accuracy and is a feasible method in predicting soil liquefaction.
引用
收藏
页数:11
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