Ground Forecasting in Mechanized Tunneling

DOI

10.2991/978-94-6463-104-3_21How to use a DOI?

Keywords

Ground Prediction; TBM; Machine Learning

Abstract

The construction of TBM tunnels is associated with high uncertainty due to the unknown ground conditions surrounding the TBM. Recently, there have been several attempts to make use of the large amount of TBM data recorded during construction to predict the ground conditions and automate the tunneling process. This study presents an implementation of supervised learning models to the Porto metro dataset (Sousa and Einstein 2012) and showcases an alternative method of predicting the ground class. The results of several machine learning (ML) models are reported and compared to each other. These ML models use the same algorithm but with different sets of input features (i.e., TBM parameters) to investigate the effect of different TBM parameters on predicting the geology of the tunnel. The results show that the learned model achieved high accuracy when predicting ground classes. Also, it indicates that input feature selection process is a crucial step to build a robust model since it eliminates ambiguous data thus increasing modeling accuracy while reducing training time. Moreover, the confusion matrices of different models showed that the rock ground class had higher scores and consistency under different sets of TBM features. This suggests that other ground classes need more refinement to attain a better model performance.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Saadeldin Mostafa
AU  - Rita L. Sousa
AU  - Herbert H. Einstein
AU  - Beatriz G. Klink
PY  - 2023
DA  - 2023/03/01
TI  - Ground Forecasting in Mechanized Tunneling
BT  - Proceedings of the TMIC 2022 Slope Stability Conference (TMIC 2022)
PB  - Atlantis Press
SP  - 240
EP  - 252
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-104-3_21
DO  - 10.2991/978-94-6463-104-3_21
ID  - Mostafa2023
ER  -