K-means聚类神经网络在边坡稳定性评价中的应用探究

边坡稳定性研究内容具有非线性,复杂性,影响因素繁杂等特点,为了寻求对于边坡稳定性更加准确的评价,提出基于K-means聚类及神经网络的边坡稳定性评价模型,并发现K-means神经网络运用在边坡稳定性分析中具有可行的预测性及良好的精确度。针对K-means聚类对数据内在结构高效分层归并能力及神经网络自学习能力的优缺点,选定45组实验数据,并选择其中容重、内摩擦角、黏聚力、坡角、坡高、孔隙水压力比6个影响因素,通过改进的K-means聚类方法进行分析并筛选出有效数据,再通过神经网络对输入的数据进行大量训练不断调整权值,输出稳定性评价安全系数。预测结果显示,此模型对边坡稳定性评价预测能力高于同类型分析方法。

Study on the application of K-means clustering algorithm and neural network in slope stability evaluation

The study of the slope stability has the characteristics of non-linearity, complexity and complicated influence factors. In order to find a more accurate evaluation of slope stability, a slope stability evaluation model based on K-means clustering algorithm and neural network is proposed. It is found that the K-means neural network is feasible and accurate in slope stability analysis. By comparing the advantages and disadvantages of K-means clustering on the high efficient inherent hierarchical merging ability and self-learning ability of neural network , 45 groups of experimental data were selected, and 6 groups of influencing factors, which were bulk density, internal friction angle, cohesion, slope angle, slope height, pore pressure ratio, were analyzed and filtered out valid data through the improved K-means algorithm method Then the input data were put into a large number of training and adjustment of weight through the neural network, in order to output the safety factors of stability evaluation. Prediction results show that the predictive ability of the model to the stability of the slope is higher than that of the same type analysis method.