scholarly journals A Novel Intelligent Method for Predicting the Penetration Rate of the Tunnel Boring Machine in Rocks

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yan Zhang ◽  
Mingdong Wei ◽  
Guoshao Su ◽  
Yao Li ◽  
Jianbin Zeng ◽  
...  
Keyword(s):  
Influencing Factors ◽  
Penetration Rate ◽  
Back Propagation ◽  
Tunnel Boring Machine ◽  
Relevance Vector Machine ◽  
Vital Role ◽  
Support Vector ◽  
Tunnel Boring ◽  
Rock Tunnels ◽  
Tbm Penetration Rate

In the construction of rock tunnels, the penetration rate of the tunnel boring machine (TBM) is influenced by many factors (e.g., geomechanical parameters), some of which are highly uncertain. It is difficult to establish a precise model for predicting the penetration rate on the basis of the influencing factors. Thus, this work proposed a useful method, based on the relevance vector machine (RVM) and particle swarm optimization (PSO), for the prediction of the TBM penetration rate. In this method, the RVM played a vital role in establishing a nonlinear mapping relationship between the penetration rate and its influencing factors through training-related samples. Then, the penetration rate could be predicted using some collected data of the influencing factors. As for the PSO, it helped to find the optimum value of a key parameter (called the basis function width) that was needed in the RVM model. Subsequently, the validity of the proposed RVM-PSO method was checked with the data monitored from a rock tunnel. The results showed that the RVM-PSO method could estimate the penetration rate of the TBM, and it proved superior to the back-propagation artificial neural network, the least-squares support vector machine, and the conventional RVM methods, in terms of the prediction performance. Moreover, the proposed RVM-PSO method could be applied to identify the difference in the importance of the various factors affecting the TBM penetration rate prediction for a tunnel.

scholarly journals Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate

2019 ◽  
Vol 9 (18) ◽  
pp. 3715 ◽  
Author(s):  
Hai Xu ◽  
Jian Zhou ◽  
Panagiotis G. Asteris ◽  
Danial Jahed Armaghani ◽  
Mahmood Md Tahir
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Uniaxial Compressive Strength ◽  
Predictive Models ◽  
Penetration Rate ◽  
Tunnel Boring Machine ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Tunnel Boring ◽  
Learning Techniques

Predicting the penetration rate is a complex and challenging task due to the interaction between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the use of empirical and theoretical techniques in predicting TBM performance. However, reliable performance prediction of TBM is of crucial importance to mining and civil projects as it can minimize the risks associated with capital costs. This study presents new applications of supervised machine learning techniques, i.e., k-nearest neighbor (KNN), chi-squared automatic interaction detection (CHAID), support vector machine (SVM), classification and regression trees (CART) and neural network (NN) in predicting the penetration rate (PR) of a TBM. To achieve this aim, an experimental database was set up, based on field observations and laboratory tests for a tunneling project in Malaysia. In the database, uniaxial compressive strength, Brazilian tensile strength, rock quality designation, weathering zone, thrust force, and revolution per minute were utilized as inputs to predict PR of TBM. Then, KNN, CHAID, SVM, CART, and NN predictive models were developed to select the best one. A simple ranking technique, as well as some performance indices, were calculated for each developed model. According to the obtained results, KNN received the highest-ranking value among all five predictive models and was selected as the best predictive model of this study. It can be concluded that KNN is able to provide high-performance capacity in predicting TBM PR. KNN model identified uniaxial compressive strength (0.2) as the most important and revolution per minutes (0.14) as the least important factor for predicting the TBM penetration rate.

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