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A hybrid finite element and surrogate modelling approach for simulation and monitoring supported TBM steering
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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CASM: a unified state parameter model for clay and sand
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Radarinterferometrie zum Setzungsmonitoring beim Tunnelbau
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FE modelling of excavation and operation of a shield tunnelling machine
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Process‐oriented numerical simulation of shield‐supported tunnelling in soft soils /
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An interaction platform for mechanized tunnelling. Application on the Wehrhahn‐Line in Düsseldorf (Germany) / Eine Interaktionsplattform für maschinelle Tunnelvortriebe. Anwendung am Beispiel der Wehrhahn‐Linie in Düsseldorf
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An elasto‐plastic three phase model for partially saturated soil for the finite element simulation of compressed air support in tunnelling
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Back analysis of model parameters in geotechnical engineering by means of soft computing
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Three-dimensional numerical simulation for mechanized tunnelling in soft ground: the influence of the joint pattern
inferredcites →https://doi.org/10.1007/s11721-007-0002-0openalex-snapshot
Particle swarm optimization
inferredcites →https://doi.org/10.1007/s11831-010-9045-2openalex-snapshot
An Object-oriented Environment for Developing Finite Element Codes for Multi-disciplinary Applications
inferredcites →https://doi.org/10.1016/j.compgeo.2004.03.004openalex-snapshot
Selecting parameters to optimize in model calibration by inverse analysis
inferredcites →https://doi.org/10.1016/j.compgeo.2014.04.005openalex-snapshot
Robust and reliable metamodels for mechanized tunnel simulations
inferredcites →https://doi.org/10.1016/j.engstruct.2012.07.001openalex-snapshot
Parallelized computational modeling of pile–soil interactions in mechanized tunneling
inferredcites →https://doi.org/10.1016/j.tust.2005.06.006openalex-snapshot
On the influence of face pressure, grouting pressure and TBM design in soft ground tunnelling
inferredcites →https://doi.org/10.1016/j.tust.2005.06.007openalex-snapshot
Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling
inferredcites →https://doi.org/10.1016/j.tust.2014.09.013openalex-snapshot
Model update and real-time steering of tunnel boring machines using simulation-based meta models
inferredcites →https://doi.org/10.1016/s0266-352x(01)00011-8openalex-snapshot
Neural network based prediction of ground surface settlements due to tunnelling
inferredcites →https://doi.org/10.1016/s0886-7798(99)00047-4openalex-snapshot
Three-dimensional numerical modelling for TBM tunnelling in consolidated clay
inferredcites →https://doi.org/10.1016/s1365-1609(00)00077-0openalex-snapshot
Displacement back analysis for a steep slope at the Three Gorges Project site
inferredcites →https://doi.org/10.1061/(asce)gt.1943-5606.0000948openalex-snapshot
Ground Movements due to Shallow Tunnels in Soft Ground. I: Analytical Solutions
inferredcites →https://doi.org/10.1090/qam/48291openalex-snapshot
Soil mechanics and plastic analysis or limit design
inferredcites →https://doi.org/10.1139/t83-002openalex-snapshot
A method of estimating surface settlement above tunnels constructed in soft ground
inferredcites →https://doi.org/10.1139/t99-038openalex-snapshot
Characterization of geotechnical variability
inferredcites →https://doi.org/10.1186/s40323-016-0057-9openalex-snapshot
A hybrid RNN-GPOD surrogate model for real-time settlement predictions in mechanised tunnelling
inferredcites →https://doi.org/10.1504/ijrs.2015.072717openalex-snapshot
Hybrid surrogate modelling for mechanised tunnelling simulations with uncertain data
inferredcites →https://doi.org/10.3139/9783446472419.009openalex-snapshot
Simulation neuronaler Netze
inferredcites →https://doi.org/10.3139/9783446483279.009openalex-snapshot
9 Simulation neuronaler Netze
inferredcites →https://doi.org/10.3233/ica-2011-0373openalex-snapshot
Recurrent neural networks for fuzzy data
inferredcites →https://doi.org/10.4203/ccp.103.5openalex-snapshot
Simulation-Based Steering for Mechanized Tunneling using an ANN-PSO-Based Meta Model
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Development and application of a novel probabilistic back-analysis framework for geotechnical parameters in shield tunneling based on the surrogate model and Bayesian theory
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A comparative analysis of hybrid RF models for efficient lithology prediction in hard rock tunneling using TBM working parameters
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Deep Learning for Time Series Forecasting: Review and Applications in Geotechnics and Geosciences
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Future work and research scope
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Numerical analysis of ground displacement and segmental stress and influence of yaw excavation loadings for a curved shield tunnel
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