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岩体中的结构面对岩体的水力和力学性质有很大影响,因此,弄清岩体中结构面的发育规律是岩体稳定性评价的基础。当结构面的产状一致,其他性质不一致时,结构面的水力学特性和力学性质是不同的。而传统的结构面优势分组方法仅根据产状数据分组,无法分辨产状相同、其他性质不同的结构面。因此,提出了一种基于量子粒子群优化算法的多参数结构面的优势分组方法。该方法通过结构面的相似性度量建立目标函数,运用量子粒子群优化算法通过搜索目标函数的全局最优解来确定聚类中心,可用于结构面多个参数的优势分组。通过对计算机模拟的多参数结构面数据的分组,验证了该方法的可靠性。最后,将该方法应用于怒江松塔水电站坝址区实测的多参数结构面数据的划分,得到了符合实际的分组结果。
The structure of rock mass has a great influence on the hydraulic and mechanical properties of rock mass. Therefore, it is the basis for rock mass stability evaluation to find out the regularity of structural surface in rock mass. When the structural surface of the same shape, the other nature is inconsistent, the structural surface of the hydraulic and mechanical properties are different. However, the traditional grouping method of structural plane superiority grouping is only based on the data of producing shape, so it can not distinguish the structural surfaces with the same shape and other properties. Therefore, an optimal grouping method based on quantum particle swarm optimization algorithm for multi-parameter structure plane is proposed. This method establishes the objective function through the similarity measure of structural plane, and uses the quantum particle swarm optimization algorithm to search the global optimal solution of the objective function to determine the clustering center, which can be used for the dominant grouping of multiple parameters on the structural plane. The reliability of this method is verified by grouping the multi-parameter surface data simulated by computer. Finally, the method is applied to the data classification of the multi-parameter structure plane measured at the dam site of Nujiang Songta Hydropower Station, and the practical grouping results are obtained.