岩体结构面的多参数优势分组方法研究

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。

A method for multivariate parameter dominant partitioning of discontinuities of rock masses

The hydraulic and mechanical properties of rock masses are affected significantly by the discontinuities, and thus it is vital to characterize the development of various discontinuities for the fundamental stability analysis of rock masses. The hydraulic and mechanical behaviors are different for the discontinuities with the same dip direction but different other parameters. Although the conventional method is widely used to classify the discontinuities by the dip direction, this approach is challenging to apply for those discontinuities having the same orientation but different other parameters. Therefore, we propose a new method based on quantum particle swarm optimization algorithm for partitioning multivariate discontinuities data. An objective function is established by the similarity measure of discontinuities. To search global optimal solution, quantum particle swarm optimization algorithm is employed. The newly proposed method classifies the discontinuities by their multivariate parameters, which is also validated by categorizing the simulated data into groups. Finally, the new method is applied to analysis the data of multivariate discontinuities collected from the Songta dam site on the NuJiang River, and there is good agreement with the in-situ measurement.