摘要
为提高砂土地震液化预测的准确性和可靠性,根据其特点,选取地震烈度、地下水位、标准贯入击数、平均粒径、不均匀系数、上覆有效压力、动剪应力比等7个因子作为判别依据,采用粒子群算法（PSO）对支持向量机（SVM）的参数寻优,建立预测砂土地震液化的PSO-SVM模型;选用50个样本训练模型,并用该模型预测14个测试样本,并回判所有样本。结果表明：模型预测准确率为100%,该模型在解决砂土地震液化预测问题中,分类效果较好、效率较高。
To improve the accuracy and reliability of sand liquefaction prediction,according to its characteristics,7 factors including the seismic intensity,groundwater level,standard penetration number,average particle size,non-uniform coefficient,overburden effective pressure and dynamic shear stress ratio were selected as a basis for discrimination. PSO was used to optimize the parameters of SVM,and a PSOSVM model was built for predicting sand liquefaction. Fifty samples were chosen to train the model. The model was used to predict 14 test samples and all the samples were returned to the test. The prediction accuracy was 100%. The result shows that the PSO-SVM model is better in classification and higher in efficiency in solving the problem of sand liquefaction prediction.
作者
毛志勇
黄春娟
路世昌
MAO Zhiyong;HUANG Chunjuan;LU Shichang(School of Business Administration, Liaoning Technical University, Huludao Liaoning 125105, China)
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2018年第3期25-30,共6页
China Safety Science Journal
基金
国家自然科学基金资助(70971059)
关键词
地震
砂土液化
数据归一化
支持向量机(SVM)
粒子群算法(PSO)
earthquake
sandy soil liquefaction
data normalization
support vector machines （SVM）
particle swarm optimization （PSO）
