基于LS-SVM的模式判别技术在边坡稳定性分析中的应用

Application of Pattern Recognition Algorithm in Slope Stability Analysis Based on LS-SVM Model

由于边坡外部影响因素与内部力学性质的复杂性,边坡稳定性与各影响因素间具有高度的非线性关系,很难构建出一种普适性模型来描述边坡稳定性变化的过程。SVM(支持向量机)是一种智能化的经验学习算法,在处理非线性问题中表现出极大的优势。利用支持向量机对边坡各影响因素的数据进行分析,对边坡稳定性状态与影响因素的对应关系进行经验学习,根据学习的经验知识,利用模态判别方法对边坡的稳定性状态进行判断。试验结果表明:所提出的边坡稳定性判别方法具有很好的应用效果,可以满足工程应用的实际需求。

As the complexity of external factors and internal mechanics in slope ,there is a highly non-linear relativity between slope stability and its influence factors,it is hard to construct a general model to describe the process of change of slope stability,SVM（ Support Vectors Machine）is a intelligent experience learning algorithm, it performs great in processing the nonlinear problem. Analyzed the slope factor data with the SVM algorithm then got the experience learning model aboul corresponding relativity between slope stability and its influence factors, finally did the evaluation with the experience model get previously. Experience results showed that the method proposed peribrmed well in evaluating state of the slope stability ; it just could satisfy the need of projects.