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Descriptive statistical analysis of TBM performance at Abu Hamour Tunnel Phase I

机译:ABU HAMOUR隧道阶段TBM性能的描述性统计分析

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The Abu Hamour Surface and Ground Water Drainage Tunnel Phase I is a 9.5-km-long, 3.7-m-inside-diameter storm water tunnel about 30 m below ground surface. Integral to phase I of the project are 19 access shafts on the tunnel line. The authors performed a rock mass quality assessment program combining borehole logging, shaft wall mapping, and laboratory testing. The results, including RMR and Q estimates, are presented and their relation to tunnel boring machine (TBM) performance examined. Rock mass properties and TBM operational data and charts are discussed. The paper also presents the results of a regression analysis linking the TBM penetration rate (PR) (mm/min) and field penetration index (FPI) (kN/cutter/mm/rev) with some geotechnical and operational parameters aggregated by strokes. For this purpose, simple, interpretable, and fairly strong general linear regression models were estimated. Then, a predictive neural network regression model was built and evaluated, revealing considerable predictive potential of the data.
机译:ABU Hamour表面和地下水排水隧道期I是9.5公里长,3.7米内径的雨水隧道,地面下面约30米。该项目的I阶段I的积分是隧道线上的19个进入轴。作者执行了岩石测井,轴壁式测绘和实验室检测的岩石质量评估计划。介绍了结果,包括RMR和Q估计,并考虑了隧道钻机(TBM)性能的关系。讨论了岩石质量和TBM运行数据和图表。本文还介绍了将TBM渗透率(PR)(MM / MIN)和场渗透指数(FPI)(kn /切割器/ mm / Rev)连接的回归分析结果,其中具有由笔触汇总的一些岩土和操作参数。为此目的,估计简单,可解释和相当强的一般线性回归模型。然后,建立和评估预测神经网络回归模型,揭示了数据的相当大的预测潜力。

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