多参数岩体结构面优势分组方法研究

在岩体力学和水力学分析中，搞清岩体结构面的分布规律是一项重要的基础工作。当岩体结构面的产状一致，而其他性质不一致时，岩体结构面的力学和水力学性质是不同的，而传统的方法仅根据岩体结构面的产状进行分组，这势必存在缺陷。提出了一种基于变尺度混沌优化算法的多参数结构面数据的优势组划分方法，通过度量结构面之间的相似性建立目标函数，运用变尺度混沌优化算法搜索目标函数的最优解来确定聚类中心，可同时考虑结构面的多个参数，把具有相似性质的结构面归并成组。通过计算机模拟的结构面数据验证了此算法的正确性，最后将此算法应用于实际工程中测量的多参数结构面数据的优势组划分，得到了清晰可靠的分组结果。

Study of method for multivariate parameter dominant partitioning of discontinuities of rock mass

The analysis of discontinuities occurrence in rock mass is an important fundamental work for mechanical and hydraulic behaviors analysis of rock mass. Discontinuities with the same occurrence and different other properties have different mechanical and hydraulic behaviors. Therefore, there are defects in conventional method, due to that they partition discontinuities only according to their occurrence. A new method based on mutative scale chaos optimization algorithm is proposed for partitioning multivariate data for charactering discontinuities. The objective function is established based on the similarity measure between discontinuities; then, mutative scale chaos optimization algorithm is used to search the optimum solution. This new method can simultaneously divide discontinuities into several groups according to their multivariate properties. The accuracy of the new method is proved by artificial data. Finally, this method is applied to grouping of field discontinuity data with five properties; and reliable cluster results are obtained.