基于人工蜂群算法的岩体结构面多参数优势分组研究

岩体结构面优势分组是研究岩体力学性质与水力特性的基础，通常的分析方法是只根据产状进行划分。鉴于结构面其他特征对岩体力学性质的重要影响，考虑结构面倾向、倾角、迹长、张开度、表面形态5个特征参数，提出了基于人工蜂群算法的岩体结构面多参数优势分组方法。以样本总体离差平方和为目标函数，建立岩体结构面多参数优势分组的数学模型，应用人工蜂群优化算法求解，以目标函数值最小时的解作为聚类中心，并自动确定分组边界。对人工生成的结构面数据的计算结果验证了该方法的正确性，该方法的求解精度是令人满意的。最后，将该方法应用于怒江松塔水电站坝址区岩体结构面多参数优势组的划分，得到了较为合理的分组结果，进一步验证了此方法具有较高的运行效率与工程实用性。

A method for multivariate parameter dominant partitioning of discontinuities of rock mass based on artificial bee colony algorithm

In geological engineering, dominant partitioning of discontinuities of rock mass is a fundamental work for mechanical and hydraulic behaviors analysis of rock mass. In common methods, only two characteristic parameters (dip and dip angle) are selected to identify discontinuity sets. Trace length, joint opening and surface morphology of discontinuities also influence the mechanical behaviors of rock mass. Therefore, a novel scheme for discontinuities classification is proposed based on multivariate parameters and artificial bee colony algorithm. The sum of deviations squares of the entire sample data is taken as an objective function. The artificial bee colony algorithm is used to search the optimal solution which makes the objective function achieve the minimum value. The optimal solution is the cluster centers. On this basis, a mathematical model is established. At the same time, the boundaries between different sets are determined automatically. The validation of the novel scheme is proved by results based on artificial data. The calculation precision of the method is satisfactory. Finally, the proposed method is applied to multivariate parameter dominant partitioning of discontinuities of rock mass at Songta dam site on the NuJiang River. The classification result verifies that the method is efficient and practical.