TBM Performance Analysis with Reference to Rock Properties

Nelson, Priscilla P.

doi:10.1016/b978-0-08-042067-7.50017-9

Cited by 18 publications

(12 citation statements)

References 10 publications

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“…The performance analysis of TBM and the development of accurate estimation model have been the main targets for many researchers (Ozdemir 1977;Blindheim 1979;Nelson 1993;Rostami and Ozdemir 1993;Barton 2000;Yagiz 2002). TBM performance is usually characterized by the rate of penetration (ROP), utilization factor (U) and advance rate (AR).…”

Section: Background On Tunnel Boring Machine and Rock Mass Interactionmentioning

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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Section: Background On Tunnel Boring Machine and Rock Mass Interactionmentioning

confidence: 99%

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

2014

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“…the time while the cutterhead rotates and is pushed towards the face. The penetration rate of a TBM is typically estimated from some properties of intact rock [18], [19], [20], often the uniaxial intact rock compressive strength σC. However, the influence of discontinuity spacing and conditions on the penetration rates is well known [21], [22].…”

Section: Tbm Penetration Ratesmentioning

confidence: 99%

An Integrated Approach to Penetration, Advance Rates and Disc Cutter Wear for Hard Rock TBM Drives

Alber

2008

Geomechanics and Tunnelling

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Tunnel boring machines (TBM) are increasingly used for excavating subsurface infrastructure such as road and railway, water supply and sewer tunnels, to name a few applications. When designing a hard rock TBM drive it is necessary to appreciate all interactions of the rock mass to be tunnelled with the TBM. This paper deals specifically with these interactions and suggests an integrated approach in estimating hard rock TBM performance, such as the penetration rate, the advance rate and the disc cutter wear. Ganzheitlicher Ansatz zur Abschätzung von

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“…Subsequently, encountering ground conditions different from the TBM's working envelope, affect the achieved tunnelling rate [21] and can give rise to claims. Thus, the model considers the geological setting to be the most dominant factor for the TBM performance, as many researchers have also noted [22,8,10], and all possible problems and downtime are a direct effect of the geotechnical conditions. The selection of the parameters used in the model was made having in mind their capability to credibly represent the ground behaviour, hydrogeological environment and site-specific conditions [23].…”

Section: Case Study 2 -Athens Metro Tunnelmentioning

confidence: 99%

“…In order to assess the performance of TBMs many researchers have proposed various methodologies [4][5][6][7][8][9][10][11][12] in an effort to express the penetration rate using as inputs data relating to the rock mass properties and/or machine characteristics. Beyond mathematical formulae and analytical solutions, artificial intelligence systems and more particularly artificial neural networks (ANNs) have not been introduced in this issue until recently.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in underground development: a study of TBM performance

Benardos¹

2008

Underground Spaces I

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Modelling tunnel boring machine (TBM) performance is an important aspect in tunnel operations. The use of artificial intelligence techniques such as artificial neural networks has been recently introduced to this subject and the results from such applications prove their potential in making accurate prognosis. This paper presents a review of feed-forward artificial neural network (ANN) development and furthermore it illustrates their application by the use of two cases studies from Italian and Greek underground projects, where the TBM performance is modelled. The results obtained show that the developed ANNs can efficiently generalise the TBM behaviour in their respective geotechnical environment, having a reliable, effective and consistent performance.

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TBM Performance Analysis with Reference to Rock Properties

Cited by 18 publications

References 10 publications

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

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

An Integrated Approach to Penetration, Advance Rates and Disc Cutter Wear for Hard Rock TBM Drives

Artificial intelligence in underground development: a study of TBM performance

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