Modeling tunnel boring machine performance by neuro-fuzzy methods

Grima, M. Alvarez; Bruines, P.A.; Verhoef, Peter N.W.

doi:10.1016/s0886-7798(00)00055-9

Cited by 239 publications

(38 citation statements)

References 13 publications

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“…Due to variation in geotechnical conditions and machine operational parameters in tunneling, artificial intelligence (AI) based models have been successfully employed by some researchers to develop TBM performance prediction models (Alvarez Grima et al, 2000;Ghasemi et al, 2014;Mahdevari et al, 2014). One of the disadvantages of AI systems has been the lack of access of an end user to the software for their predictions.…”

Section: Artificial Intelligence Methodsmentioning

confidence: 99%

“…Due to the complexity of TBM performance prediction, beyond mathematical and empirical solutions, artificial intelligence (AI) methods have been widely utilized by many researchers (Alvarez Grima et al, 2000;Okubo et al, 2003;Gholamnejad and Tayarani, 2010;Ghasemi et al, 2014). Mahdevari et al (2014) used a support vector regression analysis (SVR) to predict penetration rate based on data from the Queens Water Tunnel, in New York City.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

144

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Section: Artificial Intelligence Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

144

View full text Add to dashboard Cite

“…Existing prediction approaches include theoretical and empirical models (Barton 2000;Sapigni et al 2002), simple and multiple regression analyses (Delisio and Zhao 2014;Farrokh et al 2012;Khademi Hamidi et al 2010), artificial intelligence techniques such as artificial neural networks (Benardos and Kaliampakos 2004;Salimi et al 2016;Shao et al 2013), fuzzy inference systems (Acaroglu et al 2008;Alvarez Grima et al 2000;Yazdani-Chamzini et al 2013), support vector regression analysis (Mahdevari et al 2014), particle swarm optimization (Yagiz and Karahan 2011) and other advanced optimization algorithms (Yagiz and Karahan 2015). In general, these models are established on the basis of experience gained and the data compiled from the past tunneling projects in order to derive the complex and non-linear relationship between the TBM penetration rate and the influencing rock mass parameters.…”

Section: Introductionmentioning

confidence: 99%

Prediction of TBM penetration rate from brittleness indexes using multiple regression analysis

Jamshidi

2018

Model. Earth Syst. Environ.

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One of the most important aspects in the excavation of tunnels with a Tunnel Boring Machine (TBM) is the reliable prediction of its penetration rate. This affects the planning and other decision making on the organization of the construction site of the tunneling project, and, therefore, total costs. In this study, raw data obtained from the experimental works of different researchers were used to establish the new statistical models for prediction of rock TBM penetration rate from brittleness indexes, B 1 , B 2 , and B 3 . For this, correlation between the TBM penetration rate with brittleness indexes statistically was investigated using multiple regression analyses. In these analyses, the TBM penetration rate was considered to be the dependent variable, which is dependent on the independent variables of the brittleness indexes. The validity of the predictive models was validated by statistical tests. The results showed that statistical models are in good accuracy for prediction of TBM penetration rate, and thus making a rapid assessment of the TBM performance.

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“…The available studies, e.g. those analyzing the Tunnel Boring Machine (TBM) penetration rate (Alvarez Grima et al, 2000;Sapigni M. et al, 2002;Chung et al, 2006), only capture a part of the uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic assessment of tunnel construction performance based on data

Špačková

Šejnoha

Straub

2013

Tunnelling and Underground Space Technology

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A probabilistic model for estimating tunnel excavation time is learnt with data from past tunnel projects. The model is based on the Dynamic Bayesian Network technique. The model inputs are determined through an analysis of data from three tunnels built by means of the conventional tunneling method. The data motivate the development of a novel probability distribution to describe the excavation performance. In addition, the probability of construction failure events and the delay caused by such failures are estimated using databases available in the literature. The model is applied to a case study, in which it is demonstrated how observations from the tunnel construction process can be included to continuously update the prediction of excavation time.

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Modeling tunnel boring machine performance by neuro-fuzzy methods

Cited by 239 publications

References 13 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

Prediction of TBM penetration rate from brittleness indexes using multiple regression analysis

Probabilistic assessment of tunnel construction performance based on data

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