The effect of hydraulic stiffness on tunnel boring machine performance

1983 ◽  
Vol 20 (5) ◽  
pp. 203-214 ◽  
Author(s):  
R.A. Snowdon ◽  
M.D. Ryley ◽  
J. Temporal ◽  
G.I. Crabb
Keyword(s):  
Tunnel Boring Machine ◽  
Tunnel Boring ◽  
Machine Performance

Predicting tunnel boring machine performance through a new model based on the group method of data handling

2018 ◽  
Vol 78 (5) ◽  
pp. 3799-3813 ◽  
Author(s):  
Mohammadreza Koopialipoor ◽  
Sayed Sepehr Nikouei ◽  
Aminaton Marto ◽  
Ahmad Fahimifar ◽  
Danial Jahed Armaghani ◽  
...  
Keyword(s):  
Tunnel Boring Machine ◽  
Group Method ◽  
Data Handling ◽  
New Model ◽  
Tunnel Boring ◽  
Model Based ◽  
Machine Performance

As-Encountered Prediction of Tunnel Boring Machine Performance Parameters using Recurrent Neural Networks

2020 ◽  
Vol 2674 (10) ◽  
pp. 241-249
Author(s):  
Kabir Nagrecha ◽  
Luis Fisher ◽  
Michael Mooney ◽  
Tonatiuh Rodriguez-Nikl ◽  
Mehran Mazari ◽  
...  
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Precast Concrete ◽  
Earth Pressure ◽  
Tunnel Boring Machine ◽  
Operational Parameters ◽  
Pressure Balance ◽  
Tunnel Boring ◽  
Machine Performance ◽  
Earth Pressure Balance

The earth pressure balance tunnel boring machine (TBM) is advanced excavation machinery used to efficiently drill through subsurface ground layers while placing precast concrete tunnel segments. They have become prevalent in tunneling projects because of their adaptability, speed, and safety. Optimal usage of these machines requires information and data about the soil of the worksite that the TBM is drilling through. This paper proposes the utilization of artificial intelligence and machine learning, particularly recurrent neural networks, to predict the operational parameters of the TBM. The proposed model utilizes only performance data from excavation segments before the location of the machine as well as its current operating parameters to predict the as-encountered parameters. The proposed method is evaluated on a dataset collected during a tunneling project in North America. The results demonstrate that the model is effective in predicting operation parameters. To address the potential issue of gathering sufficient data to retrain the model, the possibility of transferring the trained model from one tunnel to another is tested. The results suggest that the model is capable of performing accurately with minimal or even no re-training.

Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance

2019 ◽  
Vol 36 (1) ◽  
pp. 345-357 ◽  
Author(s):  
Mohammadreza Koopialipoor ◽  
Ahmad Fahimifar ◽  
Ebrahim Noroozi Ghaleini ◽  
Mohammadreza Momenzadeh ◽  
Danial Jahed Armaghani
Keyword(s):  
Tunnel Boring Machine ◽  
Tunnel Boring ◽  
Machine Performance ◽  
Geotechnical Problem

scholarly journals Influence of geology on tunnel boring machine performance: A review

2020 ◽  
Vol 56 (1) ◽  
pp. 1-14
Author(s):  
P.K. Pandey ◽  
A.K. Raina ◽  
S. Deshmukh ◽  
R. Trivedi ◽  
R. Vajre ◽  
...  
Keyword(s):  
Dynamic Properties ◽  
Circular Cross Section ◽  
Tunnel Boring Machine ◽  
Intact Rock ◽  
Joint Spacing ◽  
Tunnel Boring Machines ◽  
Tunnel Boring ◽  
Machine Performance ◽  
Geological Conditions ◽  
Boring Machines

Tunnel boring machines are used for excavating a variety of soils and rocks for circular cross-section tunnels. Several published studies examined the role of rockmass in determining the cutting and advance rate of tunnel boring machines. A comprehensive review of literature was conducted to ascertain the influence of geological conditions on the performance of tunnel boring machines and revealed that different rock characteristics were used to define the tunnel boring machine performance. The progress of the tunnel boring machine was ascribed to the inherent properties of the rockmass, intact rock properties, and surrounding geological conditions. Several authors found that extreme geological conditions strongly influence the advance of the machine. The review revealed that joint spacing, angle between plane of weakness and tunnel axis, rock quality designation, and number of joint sets were the most important variables that influenced the advance rates of the tunnel boring machine. At least 12 intact rock variables were used to define tunnel boring machine performance with one to seven such variables used in combination. The compressive strength, tensile strength, and brittleness index emerged as most crucial intact properties. Rockmass classifications or indices of tunnel boring machine performance were used by different authors to predict their performance and even to define their selection methodology. Use of dynamic properties of rock/rockmass was identified as a grey area for future research by scientists.

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