Active Learning for Accurate Settlement Prediction Using Numerical Simulations in Mechanized Tunneling

Finite Element simulation is a possible tool to investigate interactions between the Tunnel Boring Machine and the surrounding soil. Surface settlements can be predicted in real-time based on simulation results by machine learning surrogate models. However, to train such models, large amounts of com...

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Bibliographic Details
Published in:Procedia CIRP 2019, Vol.81, p.1052-1058
Main Authors: Saadallah, Amal, Egorov, Alexey, Cao, Ba-Trung, Freitag, Steffen, Morik, Katharina, Meschke, Günther
Format: Article
Language:English
Subjects:
Active learning
FE-simulations
mechanised tunnelling
risk analysis
surrogate model
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Summary:Finite Element simulation is a possible tool to investigate interactions between the Tunnel Boring Machine and the surrounding soil. Surface settlements can be predicted in real-time based on simulation results by machine learning surrogate models. However, to train such models, large amounts of computationally intensive simulations are required. To accomplish this step with minimal costs, we propose a hybrid active learning approach to select the minimal amount of simulations necessary to build an accurate model. During the tunnel construction, the real-time settlements prediction model will be used to analyze associated risks to ensure safe and sustainable constructions in urban areas.
ISSN:2212-8271
2212-8271
DOI:10.1016/j.procir.2019.03.250