A hybrid finite element and surrogate modelling approach for simulation and monitoring supported TBM steering
- Submitting institution
-
University of Nottingham, The
- Unit of assessment
- 12 - Engineering
- Output identifier
- 1324342
- Type
- D - Journal article
- DOI
-
10.1016/j.tust.2016.12.004
- Title of journal
- Tunnelling and Underground Space Technology
- Article number
- -
- First page
- 12
- Volume
- 63
- Issue
- -
- ISSN
- 0886-7798
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
G - Centre for Structural Engineering & Informatics
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper presents a new hybrid steering algorithm combining recurrent neural networks trained with simulation models and optimization algorithms, validating these steering methods using real project data taken from the Wehrhahn-Line metro, Germany (contact: Prof. Dr. techn. Günther Meschke) It was also the first journal paper to propose a semi-automated link between information and simulation models for tunnelling, an important step towards BIM maturity level 3. This idea was instrumental for obtaining Dr Ninic’s Marie Curie individual fellowship “SATBIM” (grant 702874, 196kEUR).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
