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Saadallah/etal/2019a: Active Learning for Accurate Settlement Prediction Using Numerical Simulations in Mechanized Tunneling

Bibtype Article
Bibkey Saadallah/etal/2019a
Author Saadallah, Amal and Egorov, Alexey and Cao, Ba-Trung and Freitag, Steffen and Morik, Katharina and Meschke, Günther
Ls8autor Morik, Katharina
Saadallah, Amal
Title Active Learning for Accurate Settlement Prediction Using Numerical Simulations in Mechanized Tunneling
Journal CIRP Manufacturing Systems Conference 2019
Abstract 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.
Year 2019
Url https://www.cirp-cms2019.org/



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