TBM performance prediction model with a linear base function and adjustment factors obtained from rock cutting and indentation tests

2019 ◽  
Vol 93 ◽  
pp. 103085 ◽  
Author(s):  
Martin Entacher ◽  
Jamal Rostami
2020 ◽  
Vol 53 (12) ◽  
pp. 5473-5487 ◽  
Author(s):  
Andrea Rispoli ◽  
Anna Maria Ferrero ◽  
Marilena Cardu

AbstractTunnel boring machine (TBM) performance prediction is often a critical issue in the early stage of a tunnelling project, mainly due to the unpredictable nature of some important factors affecting the machine performance. In this regard, deterministic approaches are normally employed, providing results in terms of average values expected for the TBM performance. Stochastic approaches would offer improvement over deterministic methods, taking into account the parameter variability; however, their use is limited, since the level of information required is often not available. In this study, the data provided by the excavation of the Maddalena exploratory tunnel were used to predict the net and overall TBM performance for a 2.96 km section of the Mont Cenis base tunnel by using a stochastic approach. The preliminary design of the TBM cutterhead was carried out. A prediction model based on field penetration index, machine operating level and utilization factor was adopted. The variability of the parameters involved was analysed. A procedure to take into account the correlation between the input variables was described. The probability of occurrence of the outcomes was evaluated, and the total excavation time expected for the tunnel section analysed was calculated.


2021 ◽  
Author(s):  
Marco Aurélio Oliveira ◽  
Luiz V. O. Dalla Valentina ◽  
André Hideto Futami ◽  
Osmar Possamai ◽  
Carlos Alberto Flesch

2021 ◽  
Vol 30 (1) ◽  
pp. 511-523
Author(s):  
Ephrem Admasu Yekun ◽  
Abrahaley Teklay Haile

Abstract One of the important measures of quality of education is the performance of students in academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insight on how students are learning and to improve their performance ahead of time using data mining techniques. In this paper, we developed a student performance prediction model that predicts the performance of high school students for the next semester for five courses. We modeled our prediction system as a multi-label classification task and used support vector machine (SVM), Random Forest (RF), K-nearest Neighbors (KNN), and Multi-layer perceptron (MLP) as base-classifiers to train our model. We further improved the performance of the prediction model using a state-of-the-art partitioning scheme to divide the label space into smaller spaces and used Label Powerset (LP) transformation method to transform each labelset into a multi-class classification task. The proposed model achieved better performance in terms of different evaluation metrics when compared to other multi-label learning tasks such as binary relevance and classifier chains.


Author(s):  
Filippo Giannetti ◽  
Ivan Stupia ◽  
Vincenzo Lottici ◽  
Riccardo Andreotti ◽  
Aldo N. D'Andrea ◽  
...  

2018 ◽  
Vol 144 (4) ◽  
pp. 2269-2280 ◽  
Author(s):  
Yunke Huang ◽  
Hong Hou ◽  
Selda Oterkus ◽  
Zhengyu Wei ◽  
Shuai Zhang

2018 ◽  
Vol 15 (2) ◽  
pp. 170-179 ◽  
Author(s):  
Roman Paratscha ◽  
Alfred Strauss ◽  
Roman Smutny ◽  
Thomas Lampalzer ◽  
Hans Peter Rauch ◽  
...  

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