The rock mass rating(RMR)system is one of the most commonly used methods for classifying rock masses in underground engineering.Uncertainty of RMR values can signifi cantly aff ect the safety of underground projects.I...The rock mass rating(RMR)system is one of the most commonly used methods for classifying rock masses in underground engineering.Uncertainty of RMR values can signifi cantly aff ect the safety of underground projects.In this regard,we proposed a reliable rating approach for classifying rock masses based on the reliability theory.This theory was incorporated into the RMR system to establish the functions of rock masses of different classifications.By analyzing the probability distribution patterns of various parameters used in the RMR system and using the Monte Carlo method to calculate the reliability probability of surrounding rock belonging to each classifi cation,reliable RMR values for the rock mass to be excavated can be obtained.The results demonstrate that it is feasible to adopt the reliability theory in classifi cation tasks considering the randomness characteristics of rock and soil.As verified through a case study of the Lushan Tunnel project,the proposed approach can be used to obtain the probability of the uncertainty of the calculated RMR values of underground engineering rock masses,and the calculation results are consistent with reality.The proposed approach can serve as a reference for studies in other fi elds and also applies to other rock mass classifi cation methods.展开更多
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-relate...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.展开更多
基金supported by the National Natural Science Foundation of China [Grant No.52079077]China Postdoctoral Science Foundation (Grant No. 2022M711962)。
文摘The rock mass rating(RMR)system is one of the most commonly used methods for classifying rock masses in underground engineering.Uncertainty of RMR values can signifi cantly aff ect the safety of underground projects.In this regard,we proposed a reliable rating approach for classifying rock masses based on the reliability theory.This theory was incorporated into the RMR system to establish the functions of rock masses of different classifications.By analyzing the probability distribution patterns of various parameters used in the RMR system and using the Monte Carlo method to calculate the reliability probability of surrounding rock belonging to each classifi cation,reliable RMR values for the rock mass to be excavated can be obtained.The results demonstrate that it is feasible to adopt the reliability theory in classifi cation tasks considering the randomness characteristics of rock and soil.As verified through a case study of the Lushan Tunnel project,the proposed approach can be used to obtain the probability of the uncertainty of the calculated RMR values of underground engineering rock masses,and the calculation results are consistent with reality.The proposed approach can serve as a reference for studies in other fi elds and also applies to other rock mass classifi cation methods.
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)+2 种基金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)。
文摘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.
