为探明掘进机服役过程中岩体地质关键参数与掘进机工作参数间的耦合关系，在综合岩机参数静态映射关系基础上，将多维关联算法引入到隧道掘进岩机耦合模型的建立中。在充分利用服役过程地质岩层条件、掘进速度等原始数据的基础上采用K-means聚类方法处理，分类编码后，选取抗拉强度、抗压强度、峰斜指数、薄弱面间距、掘进速度、岩石连续性方向的α角、岩石类型7个主要参数。使用关联规则算法进行分析，总结出20条掘进机掘进规则。相较于决策树，其规则更加丰富直观，适用于初期决策。可以看出,关联算法作为一种有效的数据挖掘手段，能够为智能掘进提供理论参考依据。
To explore the coupling relationship between key
geological parameters of rock mass and the working parameters of tunnel boring
machines (TBMs), a multidimensional association algorithm is
developed to establish a coupling model of TBM based on the staticmapping relationship of rock parameters. The Kmeans clustering method is used to determine the coupling
relationship between geological and rock conditions, as well as the tunneling
speed during the service process. After class coding, seven main parameters,
including tensile strength, compressive strength, peak slope index, weak face
spacing, tunneling speed, alpha angle, and rock type, are selected to analyze
and summarize the rules of shield tunneling. Compared with the decision tree,
the rules are more abundant and intuitive, making them suitable for early
decisions. As an effective datamining method, the
association algorithm can provide a theoretical reference for intelligent
tunneling.
