A Neural Network Approach for Estimation of Air Losses in Compressed Air Tunnelling with Shotcrete Lining

This paper explores the capabilities of neural networks to predict the air losses in compressed air tunnelling. A back-propagation neural network has been trained and used to predict the air losses from the Feldmoching tunnel in Munich. In this project, compressed air was used to retain the groundwater and stabilize the tunnel face. Shotcrete was used as the temporary lining while the final permanent lining was installed in free air. The tunnel passed through variable ground conditions ranging from coarse gravel to sand and clay. Grouting, an additional layer of shotcrete and a layer of mortar were occasionally used to control the air losses. The results of the prediction of the air losses from the tunnel using the neural network have been compared with the field measurements. Comparison of the results shows that artificial neural network can be an efficient engineering tool in prediction of air losses from tunnels in compressed air tunnelling using field measurements and data from previous case studies. This can be of considerable value to tunnel engineers in control of tunnelling operations and help them in preparation for possible changes in air losses with tunnel advance, physical conditions and time.

• INTRODUCTION

• CASE STUDY

• Results

• Conclusion

• Acknowledgement

• Referencres