Evaluation of common TBM performance prediction models based on field data from the second lot of Zagros water conveyance tunnel (ZWCT2)

Hassanpour, Jafar; Vanani, Ali Asghar Ghaedi; Rostami, Jamal; Cheshomi, Akbar

doi:10.1016/j.tust.2015.12.006

Cited by 52 publications

(10 citation statements)

References 7 publications

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“…For traditional TBMs, there are several methods to elaborate this calcu-lus. A detail study about can be found in (Hassanpour, Vanani et al 2016), which concluded that the accuracy of the main performance prediction method is reasonable. According to (Zare, Bruland et al 2016), one of the most accepted and widely used is the performance method prediction proposed by the Norwegian University of Science and Technology -NTNU.…”

Section: Prediction Performancementioning

confidence: 99%

Water jet tunneling: a theoretical advanced rate evaluation

et al. 2018

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Tunnel Boring Machines play an important role in the underground infrastructure execution of modern cities. They weigh thousands of tons and measure hundreds of meters besides utilizing high powered energy in the excavation process. Although being well established, they are based on a last century design approach and they are not compatible anymore with the sustainable concept that characterizes current society. An alternative is looking for news technologies capable of replacing the traditional cutter disc in the excavation process. This is the approach of Tunnels Laboratory-LabTun-of Santa Catarina University. In this context, one of the lastest developments is a water jet tunnel boring machine (WJTBM). It utilizes a high power water jet (hydrodemolition) combined with diamond wire to execute the excavation process in a lighter, smart and less powerfull way. Therefore, it is just as important to compare the proposed new concept with the alternatives. This study deals with this necessity by analysing its technological performance. The advanced rate index was chosen for this task. It was calculated by the NTNU prediction model for traditional TBMs and by a proposed method for LabTun's concept. This method envolves experimental results of volumetric removal rate for high power water jet and geometrical characteristics of water jet TBM. The analysis utilized four types of rocks (sandstone, slate, meta-sandstone and granite) as geologic scenarium. The results show a better performance of WJTBM for soft and porous rock and an inexpressive performance for hard rock.

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Section: Prediction Performancementioning

confidence: 99%

Water jet tunneling: a theoretical advanced rate evaluation

et al. 2018

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“…In 2008, Yagiz developed a relationship between rock mass properties and PR. Hassanpour et al (2009Hassanpour et al ( , 2010Hassanpour et al ( , 2011Hassanpour et al ( , 2016 investigated TBM performance based on the Field Penetration Index (FPI). Zhao (2007, 2009) and Gong et al (2007) introduced the index of boreability and rock brittleness to investigate the rock mass characteristics that affect the performance of TBM.…”

Section: Introductionmentioning

confidence: 99%

A New Model for Predicting the Advance Rate of a Tunnel Boring Machine (Tbm) in Hard Rock Conditions

Arbabsiar

Farsangi

Mansouri

2020

MGPB

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The prediction of the advance rate of a Tunnel Boring Machine (TBM) in hard rock conditions is one of the most important concerns for estimating the time and costs of a tunnel project. In this paper, in the first step, a model based on Rock Engineering Systems (RES) is proposed to predict geotechnical risks (representing media characteristics) in rock TBM tunnelling. Fifteen main parameters that influence the geotechnical hazards were used in the modelling. In establishing an interaction matrix and also a parameter rating, the views of five experts were taken into account. The Vulnerability Index (VI) (geotechnical risk levels) for 2058 datasets out of 2168 sets of data from 53 geological zones in 11 km of the Zagros long tunnel was obtained. In the second step, based on the machine operating parameters such as torque, cutter head rotation per minute, cutter normal force and media characteristics (represented by VIs), which were used as input parameters and advance rate was used as an output parameter, while using 2058 datasets, linear and non-linear multiple regression analyses were carried out. 110 datasets (out of 2168 datasets), which were not used in the modelling, were applied to evaluate the performance of regression models and other models in literature and the results were compared. The obtained results showed that the new linear model proposed with R 2 =0.83 and RMSE=0.12 has a better performance than the other models.

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“…There are various methods and equations to predict PR [9,10], each has its own characteristics [11,12] due to site specific rock mass parameters and machine specifications [13,14]. TBMs penetration rate is the first and most important step in predicting the time of tunneling project [15,16].…”

Section: Introductionmentioning

confidence: 99%