Volume 47 Issue 3
Jun.  2019
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MAO Zhiyong, HUANG Chunjuan, LU Shichang. GA_SVM_Adaboost model for prediction of earthquake-induced sandy soil liquefaction[J]. COAL GEOLOGY & EXPLORATION, 2019, 47(3): 166-171. DOI: 10.3969/j.issn.1001-1986.2019.03.026
Citation: MAO Zhiyong, HUANG Chunjuan, LU Shichang. GA_SVM_Adaboost model for prediction of earthquake-induced sandy soil liquefaction[J]. COAL GEOLOGY & EXPLORATION, 2019, 47(3): 166-171. DOI: 10.3969/j.issn.1001-1986.2019.03.026
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GA_SVM_Adaboost model for prediction of earthquake-induced sandy soil liquefaction

  • System Engineering Institute, Liaoning Technical University, Huludao 125105, China

Funds: 

National Natural Science Foundation of China(70971059)

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  • Received Date: April 14, 2018
  • Published Date: June 24, 2019
    Graphical Abstract
    • Abstract
  • In order to predict the liquefaction of sand, the seismic intensity, groundwater level, covering thickness, standard number, average particle size, landform, soil quality and inhomogeneity coefficient are selected as influencing factors. Genetic algorithm(GA) is used to optimize the parameters of support vector machine(SVM) by using correlation analysis and factor analysis model, and combining with Adaboost iterative algorithm, the GA_SVM_Adaboost model for predicting the liquefaction of sand is established. 329 sets of survey data of sandy liquefaction site in Tangshan earthquake were used to train the model, and 68 samples of sandy liquefaction data were predicted by using the good model. Finally, The predicted results are compared with that of GA_SVM model and SVM model. The results show that the average prediction accuracy of three models is 100%, 98.04% and 89.71% respectively. The GA_SVM_Adaboost model based on factor analysis is better than GA_SVM model and SVM model which could improve the prediction accuracy. It is an effective method to predict the liquefaction of earthquakes.
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    • References (19)
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