支持向量机在软土区深基坑变形预测中的应用

Q1 Social Sciences HumanMachine Communication Journal Pub Date : 2010-04-24 DOI:10.1109/MVHI.2010.167
Fuxue Sun
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引用次数: 4

摘要

基于实测变形数据序列,利用支持向量机(SVM)模型对软土区深基坑未来变形值进行预测。通过测试确定了高斯核函数、序贯最小优化算法以及C和ε的参数值。算例表明,该方法的结果与实测数据和本文报道的规律吻合较好,说明支持向量机在求解类似于变形预测的模糊岩土工程问题上具有较好的效果。本文的方法和结论可供同行参考。
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SVM in Predicting the Deformation of Deep Foundation Pit in Soft Soil Area
Based on the measured deformation data series, future deformation value of deep foundation pit was predicted using Support Vector Machine (SVM) model in soft soil area. Gauss kernel function, Sequential minimal optimization arithmetic, and the parameter value of C andε were determined by testing. By using the method in example, results are shown to be in good agreement with measured data and laws reported in paper, and illustrates that SVM could perform well in solving fuzzy geotechnical engineering problem similar to deformation prediction. As another act, the method and conclusion can be considered as reference for colleagues.
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来源期刊
CiteScore
10.00
自引率
0.00%
发文量
10
审稿时长
8 weeks
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