TBM Performance Analysis in Pyroclastic Rocks: A Case History of Karaj Water Conveyance Tunnel

Hassanpour, Jafar; Rostami, Jamal; Khamehchiyan, Mashalah; Bruland, Amund; Tavakoli, Hamidreza

doi:10.1007/s00603-009-0060-2

Cited by 142 publications

(44 citation statements)

References 6 publications

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“…In several TBM performance studies, it has been indicated that the uniaxial compressive strength (UCS) is the single most important rock parameter controlling TBM ROP (e.g. Cassinelli et al 1982;Innaurato et al 1991;Hassanpour et al 2010). In this study, the UCS normalized by cutter load (F f ) is used in the model.…”

Section: Rock Mass Fabric Index In Tbm Performance Predictionmentioning

confidence: 98%

Use of Rock Mass Fabric Index in Fuzzy Environment for TBM Performance Prediction

Hedayatzadeh

Hamidi

2014

Engineering Geology for Society and Territory - Volume 6

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Precise prediction of TBM performance is one of the most crucial issues in mechanized tunneling projects. The main objective of this study is to estimate the TBM rate of penetration by constructing a fuzzy inference system analysis. A database consisting of fabric index of Q rock mass classification system, rock material properties and machine characteristics and performance along the tunnel alignment was compiled. In order to verify the validity of the developed model, the predicted and field measured penetration rates were compared. Results picked out from this predictor model revealed that the model has a strong capability for estimation of TBM performance with a correlation coefficient of 81.5 %. IntroductionPerformance prediction of tunnel boring machine is a complex and ambiguous engineering geological problem. This issue is crucial because a precise estimation of machine performance can considerably influence the cost and duration analysis during the planning stage of a mechanized tunneling project. A comprehensive review of the literature on the subject can be found in Khademi Hamidi et al. (2010b) and Farrokh et al. (2012). Among the most recently developed empirical prediction models, some ones have used intelligent systems such as artificial neural network (ANN), fuzzy logic and Neuro-Fuzzy hybrid techniques. Taking into consideration the nature of the problem, the main purpose of the present study is to develop a model by utilizing the fuzzy logic for predicting TBM performance. In order to achieve this aim, a database composed of rock mass properties including fabric indices of four rock mass classification and the angle between plane of weakness and tunnel axis, intact rock properties including uniaxial compressive strength, machine specifications including net thrust per cutter together with actual measured TBM rate of penetration(ROP) is compiled along the tunnel alignment. Alborz service tunnel situated in Tehran-Shomal freeway is chosen as a case study. The TehranShomal freeway project with about 120 km in length is a new freeway to connect the capital Tehran with the city of Chalus at the Caspian Sea in the north of Iran. The freeway alignment has more than 30 twin tunnels for double lanes. The Alborz tunnel is the longest of these with a length of 6,400 m at an altitude of 2,400 m. At this stage of the Alborz tunnel construction, an exploratory pilot tunnel is being driven in Kandovan region. This tunnel, 5.2 m in boring diameter, will be used for exploratory purposes prior to the start of the construction of main tunnels and will also serve as an emergency access and ventilation way and drainage during the life of the tunnels. Besides, it will be used for transportation and other services during the construction of main tunnels. Longitudinal profile of the geological sections is also shown in Fig. 185.1.

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Section: Rock Mass Fabric Index In Tbm Performance Predictionmentioning

confidence: 98%

Use of Rock Mass Fabric Index in Fuzzy Environment for TBM Performance Prediction

Hedayatzadeh

Hamidi

2014

Engineering Geology for Society and Territory - Volume 6

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“…In the literature, various performance prediction models are available. Most of these models are concerned with the estimation of ROP, U, AR or field penetration index (FPI) (Bruland 1999;Rostami and Ozdemir 1993;Kim 2004;Yagiz et al 2009a;Hassanpour et al 2010;Yagiz et al 2010Yagiz et al , 2013. Some simple or multiple regression equations were introduced to estimate the machine performance using intact rock properties, including uniaxial compressive and Brazilian tensile strength, Schmidt hardness, Taber abrasion, Point load index, brittleness, Drilling rate index etc.…”

Section: Background On Tunnel Boring Machine and Rock Mass Interactionmentioning

confidence: 99%

“…Geological units of this area comprise a sequence of Karaj formations having variety of pyroclastic rocks, often interbedded with sedimentary rocks. The characteristic rock types are green vitric to crystal lithic tuff, tuff breccias, sandy and silty tuffs with shale, siltstone and sandstone (Hassanpour et al 2010). The engineering geological profile of the tunnel (Fig.…”

Section: Project Description and Geological Condition Of The Study Areamentioning

confidence: 99%

Application of RMR for Estimating Rock-Mass–Related TBM Utilization and Performance Parameters: A Case Study

2014

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List of symbolsAlpha (a) The angle between tunnel axis and the planes of weakness AR Advanced rate BI Brittleness index BTS Brazilian tensile strength BWI Bit wear index CLI Cutter life index CAI Cherchar abrasivity index CCS Constant cross section CSM Colorado School of Mines Model DPW Distance between planes of weakness DRI Drilling rate index FPI Field penetration index GRRD Geology and rock related downtime MCSM Modified Colorado School of Mines Model NTNU Norwegian University of Sciences and University ORD Other related downtime Q Rock mass classification system Q TBM Modified Q system RME Rock mass excavation index RMi Rock mass index RMR Rock mass rating ROP Rate of penetration RQD Rock quality designation RSR Rock structure rating U (%) Utilization UCS Uniaxial compressive strength 1 Introduction

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“…In the last couple of decades, with growing use of TBMs in the world and the necessity to accurately predict performance of machines in different ground conditions, many researchers have worked to develop new prediction models or adjustment factors for the common existing models. Research works by Barton (1999Barton ( , 2000, Yagiz (2002Yagiz ( , 2007, Sapigni et al (2002), Gong and Zhao (2009), Hassanpour (2009, 2010, Hassanpour et al (2009Hassanpour et al ( , 2010, etc. are the most recent works on this topic.…”

Section: Introductionmentioning

confidence: 96%

“…In a more recent study by the authors (Hassanpour, 2009(Hassanpour, , 2010Hassanpour et al, 2009Hassanpour et al, , 2010, based on data obtained from main tunneling projects in Iran and investigating relationships between rock mass properties and actual machine performance, some new empirical equations have been proposed for estimation of TBM performance in the given ground conditions.…”

Section: Introductionmentioning

confidence: 98%

A new hard rock TBM performance prediction model for project planning

Hassanpour¹,

Rostami

Zhao

2011

Tunnelling and Underground Space Technology

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227

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TBM Performance Analysis in Pyroclastic Rocks: A Case History of Karaj Water Conveyance Tunnel

Cited by 142 publications

References 6 publications

Use of Rock Mass Fabric Index in Fuzzy Environment for TBM Performance Prediction

Use of Rock Mass Fabric Index in Fuzzy Environment for TBM Performance Prediction

Application of RMR for Estimating Rock-Mass–Related TBM Utilization and Performance Parameters: A Case Study

A new hard rock TBM performance prediction model for project planning

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