基于大数据的盾构掘进与地质关联关系分析方法

为解决盾构掘进参数设定主要依赖盾构司机的经验，且掘进过程中影响因素较多，很难做到掘进参数与地质参数有效关联的问题，依托盾构TBM大数据平台的海量数据，通过施工经验对关联掘进与地质的参数进行选取和分类，并确定关联参数的范围。通过参数范围界定、数据连续性分析和数据频次统计等方式进行数据的初步清洗；通过提取变量的数字特征建立分布统计算法模型库的方式，对数据库中的数据实时处理，去除异常数据并确定经验区间的频数分布；通过对各关联参数的组合检索，进行关联参数的可视化分析，得到不同盾构在各类地质中主要掘进参数（如刀盘转速、刀盘转矩、掘进速度、油缸推力等）的经验区间和关联关系。将该关联分析方法部署在盾构TBM大数据平台，经过长时间的应用和现场反馈，验证了该方法的适用性和有效性，对盾构施工及盾构选型具有积极的指导作用。

Analysis Method of Correlation between Shield Tunneling and Geology Based on Big Data

The shield tunneling parameters is mainly set according to the experience of shield drivers, but the effective correlation of tunneling parameters and geological parameters is difficult to be achieved due to various factors affecting tunneling process. Hence, related tunneling and geology parameters are selected and classified through construction experience based on the massive data of TBM big data platform, and the range of related parameters is determined. The primary data cleaning is carried out by means of parameter range definition, data continuity analysis and data frequency statistics; by extracting the digital characteristics of variables to establish the distribution statistical algorithm model database, the data in the database are processed in real time, the abnormal data is removed and the frequency distribution of the experience interval is determined; through the combination retrieval of related parameters and the visual analysis of the related parameters, the empirical interval and the related relationship of the main driving parameters (i.e. cutter speed, cutter torque, driving speed, cylinder thrust, etc.) of different shield machines in various geological conditions are obtained. The association analysis method is deployed in the big data platform of shield TBM. After a long time of application and on site feedback, the applicability and effectiveness of the method are verified, which has a positive guiding effect for shield construction and shield machine selection.