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Analysis of principal component-application of SVM model in prediction of ultimate bearing capacity of Static Pressure Pipe Pile

机译:SVM模型在静压管桩极限承载力预测中的主要成分应用分析

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Vertical ultimate bearing capacity of static pressure pipe pile is influenced by comprehensive factors, such as pile body, soil around pile and construction conditions, and the relationship between the impact factors and ultimate bearing capacity of a single pile is highly complexity and non-linear. This paper is based on collecting the data from static load tests in typical geological conditions of Liao-shen area, construction records, and test pile sites. And then combine analysis of principal component with SVM to analysis the prediction of the single pile's vertical ultimate bearing capacity. This model can reduce the number of SVM input variable dimension to improve speed of training support vector effectively. At the same time it can eliminate the influence factors of multiple correlation. The results show that the proposed principal component analysis SVM model has good predictive accuracy and generalization ability, and opens up new avenue of research for analysis of static pressure pipe pile vertical bearing properties.
机译:静压管桩垂直终极承载能力受综合因素的影响,如桩体,桩桩周围的土壤和施工条件,以及单桩的冲击因子和最终承载力之间的关系是高度复杂性和非线性的。本文基于在辽沉地区,施工记录和试验桩位点的典型地质条件下收集来自静载试验的数据。然后将主成分与SVM分析分析,分析单桩垂直极性承载力的预测。该模型可以减少SVM输入可变尺寸的数量,以提高有效的培训速度。同时它可以消除多重相关的影响因素。结果表明,所提出的主要成分分析SVM模型具有良好的预测精度和泛化能力,开辟了静压管桩垂直轴承性能分析的新途径。

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