This talk was delivered online to researchers in transportation technologies.

1. As-Encountered Prediction of Tunnel Boring Machine
Performance Parameters Using Recurrent Neural
Networks

2. Importance of Predictive Technologies in Tunnel Boring
❑ Tunnel Boring Machines are among the most important tunneling tools
❑ However, they are hindered by the fact that they largely run ‘blind’
❑ Using deep learning systems, we can predict how the machine will run as it moves
forward

3. Past Efforts to Add Predictive Capabilities
❑ Previous research works have attempted to provide predictive capabilities to TBMs
❑ Traditional statistical methods lead to unsatisfactory results
❑ Deep learning systems provide flexibility and additional accuracy in comparison to past
tests

4. Dataset and
Features
❑ Entirely based on mechanical
features – to predict mechanical
features
❑ Predicted timestep data is not used
❑ Future steps are predicted –
prescriptive and informative

5. Recurrent Neural
Networks
❑ Form of ‘Artificial Neural Network’
❑Like a ‘brain’ – neurons working together
❑ Recurrent Neural Network – the past affects the future

6. Results
RMSE: 10.78 mm/min RMSE: 1201.99 kN RMSE: 3385.84 kN

7. Additional Opportunity – Transfer Learning to Reduce Training Time
❑ Worksite preparation at present is time-consuming and expensive
❑ Transfer learning – train on one tunnel, run it on another
❑ Using this system, we can vastly improve preparation times for TBM operation

8. Results
RMSE: 11.63 mm/min RMSE: 1180.162 kN RMSE: 2483.43 kN

9. • Support from the University Transportation Center for Underground Transportation
Infrastructure (UTC-UTI) at the Colorado School of Mines for funding this research under
Grant No. 69A3551747118 from the U.S. Department of Transportation (DOT) is
gratefully acknowledged.
• Thanks to the L.A. Metro for providing the datasets used in this project.