模拟退火聚类算法在结构面产状分组中的应用

鉴于以往的结构面产状分组方法常存在算法复杂、聚类精度差及分组效率低的不足，提出了一种新型的融合模拟退火算法及K-means聚类(SAK)的结构面分组算法，该算法简单易实现.利用模拟退火算法的退火原理，对K-means算法聚类的结构面分组结果进行优化，以期克服K-means算法易受初始聚类中心影响的缺陷.计算机模拟生成的结构面数据的分析表明，所提方法相较于传统K-means算法具有明显优势.将该方法应用于重庆市三环高速公路兴隆隧道实测结构面的分组中，并与已有方法进行对比.结果表明:该方法不仅聚类精度高，而且迭代速度也较快，具有较强的工程实用性.

Application of Simulated Annealing Clustering Algorithm in Grouping of Discontinuity Orientation

Aiming at the complexity， poor clustering accuracy and low grouping efficiency of the previous methods of dominant grouping of discontinuity orientation， a new method of dominant grouping of discontinuity orientation based on simulated annealing algorithm and K-means clustering (SAK) was proposed. The algorithm is simple and easy to implement. Based on the annealing principle of simulated annealing algorithm， the grouping results of K-means algorithm were optimized， which aimed to overcome the shortcoming of the K-means algorithm’s susceptibility to the initial clustering center. The analysis of discontinuities generated by computer simulation showed that the proposed method is superior to the traditional K-means algorithm. The method was applied to the grouping of measured discontinuity orientation of Xinglong Tunnel on Third Ring Expressway of Chongqing City， and compared with the existing methods. The results showed that this method not only has high clustering accuracy， but also has fast iteration speed and strong engineering practicability.