Tunnel boring machine (TBM) tunneling data have been extensively collected to utilize TBM information technology by analyzing and mining the data for achieving a safe and efficient TBM tunneling. Feature extraction of big data could reduce the complexity for problems, but conventional indexes based on feature extraction, such as field penetration index (FPI), specific penetration (SP), and boreability index (BI), have some disadvantages. Thus, we present novel boring indexes derived from tunneling data in the Yinchao TBM project. Linear thrust-penetration and torque-penetration relationships in filtered ascending sections ( ≥ 2 mm/r) are proposed using statistical features and through physical mechanism analysis of parameters in the TBM cyclic tunneling process. Boring indexes, such as normal boring difficulty index, initial rock mass fragmentation difficulty index, and tangential boring difficulty index, are defined using the coefficients of the linear thrust-penetration and torque-penetration relationships. Subsequently, the defined boring indexes are verified using performance prediction of 291 cyclic tunneling processes. Finally, a preliminary application of support measure suggestions is conducted using the statistical features of boring indexes, where certain criteria are proposed and verified. The results showed that the criterion of boring indexes for support measure suggestions could achieve a reasonable confirmation, potentially providing quantitative quotas for support measure suggestions in the subsequent construction process.

中文翻译：

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基于大数据的掘进指标及其在TBM隧道掘进中的应用

隧道掘进机 (TBM) 隧道掘进数据已被广泛收集，通过分析和挖掘数据，利用 TBM 信息技术实现安全高效的 TBM 隧道掘进。大数据的特征提取可以降低问题的复杂度，但传统的基于特征提取的指标，如油田穿透指数（FPI）、比穿透指数（SP）、钻孔指数（BI）等，存在一定的不足。因此，我们提出了从银潮 TBM 项目中的隧道数据中得出的新的钻孔指标。过滤上升段中的线性推力-穿透和扭矩-穿透关系（ ≥ 2 mm/r) 是利用统计特征和通过对 TBM 循环掘进过程中参数的物理机制分析提出的。常用钻孔难度指数、初始岩体破碎难度指数、切向钻孔难度指数等钻孔指标是利用线性推力-贯入系数和扭矩-贯入系数的系数定义的。随后，使用 291 个循环隧道掘进过程的性能预测来验证定义的钻孔指标。最后，利用枯燥指标的统计特征，对支持措施建议进行了初步应用，提出并验证了一定的标准。结果表明，支撑措施建议的枯燥指标准则能够得到合理的确认，