An Experimental-Based Model for Prediction of the Rock Mass-Related TBM Utilization by Adopting the RMR and Moisture-Dependent CAI  

作　　者：Changbin Yan Ziang Gao Gongbiao Yang Zihe Gao Lei Huang Jihua Yang 

机构地区：[1]School of Civil Engineering,Zhengzhou University,Zhengzhou 450001,China [2]China Railway Construction Underwater Tunnel Engineering Laboratory,Jinan 250101,China [3]Three Gorges Research Center for Geohazards,Ministry of Education,China University of Geosciences(Wuhan),Wuhan 430074,China [4]Yellow River Engineering Consulting Co.,Ltd.,Zhengzhou 450003,China

出　　处：《Journal of Earth Science》2025年第2期668-684,共17页地球科学学刊(英文版)

基　　金：financially supported by the National Natural Science Foundation of China(Nos.41972270,52076198);the Key Research and Development Plan of Henan Province(No.182102210014);the Excellent Youth Foundation of Henan Scientific Committee(No.222300420078);the Youth Talent Promotion Project of Henan Province(No.2022HYTP019);the Open Foundation of State Key Laboratory of Shield Machine and Boring Technology(No.SKLST-2019-K06)。

摘　　要：To reduce the uncertainty associated with the traditional definition of tunnel boring machine(TBM)utilization(U)and achieve an effective indicator of TBM performance,a new performance indicator called rock mass-related utilization(U_(r))is introduced;this variable considers only rock mass-related factors rather than all potential factors.This work aims to predict U_(r)by adopting the rock mass rating(RMR)and the moisture-dependent Cerchar abrasivity index(CAI).Substantial U_(r),RMR and CAI data are acquired from a 31.57 km northwestern Chinese water conveyance tunnel via tunnelling field recordings,geological investigations and Cerchar abrasivity tests.The moisture dependence of the CAI is explored across four lithologies:quartz schists,granites,sandstones and metamorphic andesites.The potential influences of RMR and CAI on Ur are then investigated.As the RMR increases,U_(r)initially increases and then peaks at an RMR of 56 before declining.U_(r)appears to decline with CAI.An investigation-based relation among U_(r),RMR and moisture-dependent CAI is developed for estimating U_(r).The developed relation can accurately predict U_(r)using RMR and moisture-dependent CAI in the majority of the tunnelling cases examined.This work proposes a stable indicator of TBM performance and provided a fairly accurate prediction method for this indicator.

关 键 词：tunnel boring machine(TBM) UTILIZATION RMR system Cerchar abrasivity index(CAI) predicting model engineering geology 

分 类 号：U455.43[建筑科学—桥梁与隧道工程]