基于主成分分析和优化支持向量机的砂土地震液化预测
Prediction of Seismic Liquefaction of Sand Based on Principal Component Analysis and Optimized Support Vector Machine
刘佩瑶1
作者信息
- 1. 华北水利水电大学 国际教育学院(乌拉尔学院),郑州 450045
- 折叠
摘要
对影响砂土地震液化的 9 个影响因素进行主成分分析,提取了 4 个主成分,同时引入支持向量机建立了砂土地震液化预测模型,并结合工程实例,将预测结果与未进行主成分提取的优化支持向量机模型预测结果进行对比.结果表明:基于主成分分析和优化支持向量机的砂土地震液化预测模型精度更高,可以为震灾防治工作提供有效支撑.
Abstract
Seismic liquefaction of sand is a dynamic geological phenomenon caused by the joint action of multiple influencing factors,and it is difficult to accurately distinguish the seismic liquefaction state of sand by conventional models.In this paper,the principal component analysis was carried out on the selected nine influencing factors of sand seismic liquefaction,and four principal components were extracted.At the same time,the support vector machine was introduced to establish the prediction model of sand seismic liquefaction.Combined with an engineering example,the prediction results were compared with the prediction results of optimized support vector machine model without principal component extraction.The results showed that the prediction model of sand seismic liquefaction based on principal component analysis and optimized support vector machine had higher accuracy,and could provide effective support for earthquake disaster prevention and control work.
关键词
砂土液化/主成分分析/支持向量机Key words
liquefaction of sand/principal component analysis/support vector machine引用本文复制引用
出版年
2024
NETL
NSTL
万方数据