Abstract
Realistic 3D simulations of the tunnelling process are increasingly required to investigate the interactions between machine-driven tunnel construction and the surrounding soil in order to provide reliable estimates of the expected settlements and associated risks of damage for existing structures, in particular in urban tunnelling projects. To accomplish the step from large-scale computational analysis to real-time predictions of expected settlements during tunnel construction, the focus of this paper is laid on the generation of a numerically efficient hybrid surrogate modelling strategy, combining Gappy proper orthogonal decomposition (GPOD) and recurrent neural networks (RNN). In this hybrid RNN-GPOD surrogate model, the RNN is employed to extrapolate the time variant settlements at several monitoring points within an investigated surface area and GPOD is utilised to predict the whole field of surface settlements based on the RNN predictions and a POD radial basis functions approximation. Both parts of the surrogate model are created based on results of finite element simulations from geotechnical and process parameters varied within the range of intervals given in the design stage of a tunnel project. In the construction stage, the hybrid surrogate model is applied for real-time reliability analyses of the mechanised tunnelling process to support the machine operator in steering the tunnel boring machine.
Highlights
Mechanised tunnelling is a widely used construction method for underground infrastructure in particular in urban areas due to its effectiveness in controlling the advancement process and to limit the construction induced ground deformations
To accomplish the step from large-scale computational analysis to real-time predictions of expected settlements during tunnel construction, the focus of this paper is laid on the generation of a numerically efficient hybrid surrogate modelling strategy, combining Gappy proper orthogonal decomposition (GPOD) and recurrent neural networks (RNN)
Real-time simulation software for TBM steering support Based on the enhanced IGPOD-Extended RBF (ERBF) algorithm described in ‘Hybrid surrogate model’, a real-time simulation software is developed with the aim to support the steering of the tunnel boring machine during mechanised tunnelling
Summary
Mechanised tunnelling is a widely used construction method for underground infrastructure in particular in urban areas due to its effectiveness in controlling the advancement process and to limit the construction induced ground deformations. An approach combining ERBF and POD as a surrogate model for application in mechanised tunnelling has been proposed in [8] This modified version of POD-RBF method, which has shown improvements in prediction accuracy of the generated surrogate models for both linear and nonlinear functions in multi dimensional spaces will be utilised in this paper. According to the POD-ERBF, the approximation is performed by a linear combination of radial and non-RBFs. The following equation provides the approximated amplitude value of an arbitrary point using the ERBF approach: N. i=1 i=1 j=1 with S as the dimension of input parameters. The proposed hybrid surrogate modelling approach is employed to predict the complete surface displacement field in the subsequent excavation step (step 23) from input parameter selected within a specific y coordinates [m]
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