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结合粗糙集和支持向量机两种智能算法,建立了基于粗糙集与支持向量机的岩质边坡稳定性评价模型。首先根据有限的经验数据建立属性决策表,通过属性约算法找出影响边坡稳定性的关键因素;然后将所提取的关键信息训练支持向量机。本文以铁路沿线边坡为例,进行边坡稳定性验算,结果表明算法能有效降低边坡稳定性影响因素集数据维数及支持向量机的复杂程度,提高训练速度和泛化能力。
Combined with two kinds of intelligent algorithms, such as rough sets and support vector machines, a rock slope stability evaluation model based on rough set and support vector machine is established. Firstly, the attribute decision table is established based on the limited empirical data, and the key factors affecting the stability of the slope are found through the attribute approximation algorithm. Then the key information extracted is trained by SVM. In this paper, slope stability along the railway line is taken as an example to verify the stability of the slope. The results show that the algorithm can effectively reduce the data dimension of the influencing factors of slope stability and the complexity of SVM, and improve the training speed and generalization ability.