Abstract

Prediction of utilization based on rock mass properties is critical for any mechanized tunneling project, since the utilization have an influence on duration and costs of project. Using some case studies including Queens, Milyang, Manapouri, Karaj, Varzo, Maen, Pieve mechanized tunneling project around the world, the attempt is made to estimate utilization via Rock Mass Rating (RMR) System herein. It is concluded that the machine utilization has some reasonable correlations with RMR; however, this relationship is depend on not only rock mass properties but also machine specifications. In other words, the utilization may change with both rock mass conditions and utilized machine type. Due to that rock properties and other related downtimes should be examined together to obtain the reliable equations for predicting the utilization. Further, developed relationships may be used for similar rock type to estimate utilization from RMR.

1 Introduction

Utilization (U) is defined as the percentage of machine boring time to the shift time. It is summarily the function of the advance rate, (AR) and the rate of penetration, (ROP), (U=AR/[ROP×Shift Hour]). In this function, the ROP is the ratio of excavated distance to the operating time during continues excavation phase, while the AR is the ratio of both mined and supported actual distance to the total time (Yagiz 2008). In mechanical tunneling, type of operation, management, maintenances, geological conditions, capacity of the backup system and other factors that cause downtimes -including the times of support installation, re-gripping, grouting, maintenance, machine break down, cutter change, mucking delays, stoppage caused by geological adverse conditions and other components such as shift changes- to the operation, have an influence on the utilization.

A wide variety of TBM performance prognosis models and principles with special focus on rate of penetration are introduced in the literature since the early use of TBM. Some methods simply focus on rock parameters such as uniaxial compressive strength and rock abrasion value, while other methods consider a combination of comprehensive laboratory, field and machine data. Although CSM, NTNU and QTBM models have suggested some equations and graphs for estimating the utilization factor (Rostami & Ozdemir 1993, Bruland 1998, Barton 1999,Yagiz 2002), there are a few documents in the literature about estimating TBM utilization with relevant parameters.

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