A Comparative Study of Ensemble Methods for Prediction of Surface Settlement Induced by TBM Tunneling
Publication: Geo-Risk 2023
ABSTRACT
The purpose of this study is to apply ensemble methods to predict surface settlement induced by earth pressure balance tunnel boring machine. Random forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms are applied on 1,101 settlement measurements collected from the Grand Paris Express project. The results are compared with the performance of the back-propagation artificial neural networks (BPNN). Finally, the results show that both ensemble methods XGBoost and RF are better than BPNN based on R² and RMSE indicators.
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Information & Authors
Information
Published In
Geo-Risk 2023
Pages: 211 - 219
History
Published online: Jul 20, 2023
ASCE Technical Topics:
- Algorithms
- Artificial intelligence and machine learning
- Boring
- Comparative studies
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction methods
- Drilling
- Ecosystems
- Engineering fundamentals
- Environmental engineering
- Forests
- Geomechanics
- Geotechnical engineering
- Mathematics
- Methodology (by type)
- Neural networks
- Research methods (by type)
- Soil dynamics
- Soil mechanics
- Soil pressure
- Tunneling
- Tunnels
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