Abstract
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 ( p ≥ 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.
Highlights
Tunnel boring machines (TBMs), a type of engineering machinery, have been widely applied in the construction of tunnels, such as water conveyance, highway, and railway tunnels [1]
With the development of information technology [2] and construction of big data management platforms [1], the Internet of ings technology for real-time TBM data acquisition has been used widely. e method for data acquisition has been used in the Yinsong TBM project [3, 4] for effectively recording extensive data, including cutterhead thrust, cutterhead torque, cutterhead rotation speed, advance rate, and penetration, which are closely related to rock mass fragmentation [5, 6]. erefore, the analyzing and mining of TBM tunneling data can provide valuable information regarding the rock mass [7]
TBM tunneling can be regarded as a large-scale field linear cutting test [8]. e main characteristics of rock mass fragmentation are the interaction between the TBM cutters and rock mass, as well as loading the thrust and torque of the cutterhead acting on the rock mass, which exceeds the ultimate strength of the rock mass [9]. e performance
Summary
Tunnel boring machines (TBMs), a type of engineering machinery, have been widely applied in the construction of tunnels, such as water conveyance, highway, and railway tunnels [1]. Many indexes have been derived from TBM performance parameters to reflect the characteristics of rock mass. Some of the proposed indexes are the field penetration index (FPI) [19], specific penetration (SP) [20], boreability index (BI) [5], and drilling efficiency index (TPI) [4], which can be used for rock mass parameter prediction [21], rock mass classification [22], and adverse geological diagnosis [4] They can achieve better results based on the fine relationships of thrust-penetration (F-p) and torque-penetration (T-p). E key contribution of this study is proposing a novel model and data hybrid-driven boring indexes in cyclic tunneling processes, followed by a preliminary application for support measure suggestions.
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