Developing new equations for TBM performance prediction in carbonate-argillaceous rocks: a case history of Nowsood water conveyance tunnel

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

doi:10.1080/17486020903174303

Cited by 110 publications

(34 citation statements)

References 2 publications

(1 reference statement)

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“…During the construction phase and through back-mapping of the tunnel, predicted geological and geomechanical properties of rock mass along the tunnels were examined by detailed investigation of tunnel face (Hassanpour et al, 2009;Khademi Hamidi et al, 2010). In this stage, information such as rock type, rock mass fracturing, joint condition, characteristics of fault zones, weathering/alteration characteristics, ground water condition and rock stability information were recorded on mapping sheets.…”

Section: Tbm Performance Databasementioning

confidence: 99%

“…To obtain the required data for analysis of TBM performance at Zgaros lot 1B & 2, results of studies performed during the preconstruction phase and construction phase have been compiled into a database (Hassanpour et al, 2009;Khademi Hamidi et al, 2010;Shahriar et al, 2009). During the construction phase and through back-mapping of the tunnel, predicted geological and geomechanical properties of rock mass along the tunnels were examined by detailed investigation of tunnel face (Hassanpour et al, 2009;Khademi Hamidi et al, 2010).…”

Section: Tbm Performance Databasementioning

confidence: 99%

“…Research works by Graham (1976), Farmer and Glossop (1980) Büchi (1984), Hughes (1986), Rostami and Ozdemir (1993), Gehring (1995), Bruland (1998), Barton (2000), Bieniawski von Preinl et al, (2006), Gong and Zhao (2009), Hassanpour et al (2009Hassanpour et al ( , 2010Hassanpour et al ( , 2011, Khademi Hamidi et al (2010), Benato and Oreste (2015) are the most common and recent works on this topic as summarized in Table 1. Barton (2000) reviewed a wide range of TBM tunnels to establish a database for proposing a new model based on Q rock mass classification system for TBM applications.…”

Section: Introductionmentioning

confidence: 95%

“…Also, the most important performance parameters including average rate of penetration (ROP), penetration per revolution (P), and Field Penetration Index (FPI, Tarkoy and Marconi, 1991;Hassanpour et al 2009) have been calculated using formula (1)-(3) as listed below:…”

Section: Tbm Performance Databasementioning

confidence: 99%

See 3 more Smart Citations

Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs

Salimi

Rostami

Moormann

et al. 2016

Tunnelling and Underground Space Technology

142

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Section: Tbm Performance Databasementioning

confidence: 99%

Section: Tbm Performance Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

Section: Tbm Performance Databasementioning

confidence: 99%

See 2 more Smart Citations

Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs

Salimi

Rostami

Moormann

et al. 2016

Tunnelling and Underground Space Technology

142

View full text Add to dashboard Cite

“…Yagiz (2008) used rock mass properties in predicting TBM performance under the hard-rock condition. Hassanpour et al (2009) developed new equations for predicting the performance of hard-rock TBMs in carbonate-argillaceous rocks. Gehring (2009) carried out research on the influence of TBM design and machine features on performance and tool wear in rock.…”

mentioning

confidence: 99%

Applying Optimized Support Vector Regression Models for Prediction of Tunnel Boring Machine Performance

Fattahi

Babanouri

2017

Geotech Geol Eng

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One of the main factors in the effective application of a tunnel boring machine (TBM) is the ability to accurately estimate the machine performance in order to determine the project costs and schedule. Predicting the TBM performance is a nonlinear and multivariable complex problem. The aim of this study is to predict the performance of TBM using the hybrid of support vector regression (SVR) and the differential evolution algorithm (DE), artificial bee colony algorithm (ABC), and gravitational search algorithm (GSA). The DE, ABC and GSA are combined with the SVR for determining the optimal value of its user defined parameters. The optimization implementation by the DE, ABC and GSA significantly improves the generalization ability of the SVR. The uniaxial compressive strength (UCS), average distance between planes of weakness (DPW), the angle between tunnel axis and the planes of weakness (a), and intact rock brittleness (BI) were considered as the input parameters, while the rate of penetration was the output parameter. The prediction models were applied to the available data given in the literature, and their performance was assessed based on statistical criteria. The results clearly show the superiority of DE when integrated with SVR for optimizing values of its parameters. In addition, the suggested model was compared with the methods previously presented for predicting the TBM penetration rate. The comparative results revealed that the hybrid of DE and SVR yields a robust model which outperforms other models in terms of the higher correlation coefficient and lower mean squared error.

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TBM performance and disc cutter wear prediction based on ten years experience of TBM tunnelling in Iran

Hassanpour

Rostami

Zhao

et al. 2015

Geomechanics and Tunnelling

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Accurate estimation of rate of penetration (ROP), daily rate of advance (AR), and cutter cost/life is necessary for the justification and successful use of a TBM on any tunneling project. Various models have been introduced over the years to provide such estimates. In many cases, these models have been successful with pin point accuracy, and in other instances off by a wide margin. Improving the accuracy of performance prediction models for hard rock TBMs has been the subject of many studies, some yielding project-based models and some more universal models that can be used elsewhere. This paper will discuss the latest version of a new empirical model for estimating penetration rate and disc cutter life for hard rock TBM tunneling based on recent tunneling projects, constructed during the period 2002-2009. The new model was then applied to predict TBM performance and disc cutter wear in different mechanized tunneling projects under construction since 2009. Accuracy and validity of the model for prediction of ROP were checked by comparing predicted and measured values of TBM performance in 24 km of newly bored tunnel. The results indicate that despite some minor deviations in estimates in some tunnel sections, reasonable agreement can be observed between predicted and actual ROP of TBMs.

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Developing new equations for TBM performance prediction in carbonate-argillaceous rocks: a case history of Nowsood water conveyance tunnel

Cited by 110 publications

References 2 publications

Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs

Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs

Applying Optimized Support Vector Regression Models for Prediction of Tunnel Boring Machine Performance

TBM performance and disc cutter wear prediction based on ten years experience of TBM tunnelling in Iran

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