Journal
ENVIRONMENTAL EARTH SCIENCES
Volume 75, Issue 10, Pages -Publisher
SPRINGER
DOI: 10.1007/s12665-016-5673-7
Keywords
Soil liquefaction; Support vector machine; Genetic algorithm; Grid search; Liquefaction; Classification accuracy
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available