Performance Prediction of Hard Rock TBM Based on Extreme Learning Machine

Shao, Chengjun; Li, Xiuliang; Su, Hongye

doi:10.1007/978-3-642-40849-6_40

Cited by 19 publications

(2 citation statements)

References 8 publications

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“…Yagiz [10] proposed a valuable dataset containing the rock mass properties and the penetration rate in Queens Water Tunnel. Data were used for the prediction of penetration rate (PR=AR/rotation speed of cutterhead) via fuzzy inference system [11], ANN [12], particle swarm optimization (PSO) [13], extreme learning machine (ELM) [14], and support vector machine (SVR) [15]. Armaghani et.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Model for Shield Tunneling Advance Rate Prediction in Mixed Ground Condition Considering Past Operations

2020

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The advance rate (AR) is a significant parameter in shield tunneling construction, which has a major impact on construction efficiency. From a practical perspective, it's helpful to establish a predictive model of the AR, which takes into account the instantaneous parameters as well as the past operations. However, for shield tunneling in mixed ground conditions, most researches focused on the average values of AR per ring and neglect the influence of past operations. This paper presents a long short-term memory (LSTM) recurrent neural network model, which was developed for the slurry shield tunneling in a mixed ground of round gravel and mudstone in Nanning metro. A temporal aggregated random forest is employed to rank the importance of the explanatory features. The model performances in different ground conditions are investigated. The results show that the LSTM model can be effectively implemented for the AR prediction. A high correlation is observed between predicted and measured AR with a correlation coefficient (R 2) of 0.93. The LSTM based AR predictive model is compared with the random forest (RF) model, the deep feedforward network (DFN) model, and the support vector regression (SVR) model. The comparison shows that the LSTM model has the best performances compared to other models. With onefourth features, we can achieve a 95% prediction accuracy measured by the R 2 in the proposed model.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Model for Shield Tunneling Advance Rate Prediction in Mixed Ground Condition Considering Past Operations

2020

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“…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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Empirical, Statistical, and Intelligent Techniques for TBM Performance Prediction

Armaghani

Azizi

2021

Applications of Artificial Intelligence in Tunnelling and Underground Space Technology

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Performance Prediction of Hard Rock TBM Based on Extreme Learning Machine

Cited by 19 publications

References 8 publications

Deep Learning Model for Shield Tunneling Advance Rate Prediction in Mixed Ground Condition Considering Past Operations

Deep Learning Model for Shield Tunneling Advance Rate Prediction in Mixed Ground Condition Considering Past Operations

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

Empirical, Statistical, and Intelligent Techniques for TBM Performance Prediction

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