边坡稳定性的k-近邻估计

k-nearest neighbor estimation of slope stability

边坡稳定性估计的精度直接关系到边坡工程的成败.然而,边坡稳定性与其影响因素之间存在复杂的非线性关系.当目标函数很复杂时,如果只建立目标函数的局部逼近,并将其应用于待测实例的邻域,就能获得较高的预测精度.这种局部建模方法的典型代表就是k-近邻及其改进算法.在研究k-近邻算法的基本原理及其改进方法的基础上,提出了应用距离加权的k-近邻方法对由岩石容重、岩石内聚力、内摩擦角、边坡角、边坡高度和孔隙水压力6个特征参数组成的岩土参数进行建模,估计表征边坡稳定性的安全系数.实验中,用82个圆弧破坏边坡实例中的71个实例进行建模,对另外11个实例进行推广预测.实验结果表明:用k-近邻算法进行边坡稳定性预测有较高的精度.

The estimation precision of slope stability was directly related to the success or failure of the slope engineering. However, the relationship between slope stability and influencing factors was complex and nonlin-ear. When the target function was complex, approximating the target function locally and differently for each distinct query instance could get higher accuracy. The typical representative of these methods was k-nearest-neighbor （ KNN） and its improved methods. It was firstly studied the basic principle of KNN and its improved methods, and then the distance weighted KNN （ KNNDW） was applied to build the predicting model based on the instance data composed of 6 characteristic parameters and predict safety factors characterizing slope stabili-ty. In the experiments, 71 instances from 82 arc slope destruction instances were used to build the predicting model and the other 11 instances were used for promotion prediction. The experimental results proved that KNNDW was more accurate than the modified BP algorithm, the GA-BP algorithm and the υ-SVR algorithm.