Recurrent neural networks and proper orthogonal decomposition with interval data for real-time predictions of mechanised tunnelling processes

Freitag, S.; Cao, B. T.; Nini?, J.; Meschke, G.

Abstract

© 2017 Elsevier Ltd A surrogate modelling strategy for predictions of interval settlement fields in real time during machine driven construction of tunnels, accounting for uncertain geotechnical parameters in terms of intervals, is presented in the paper. Artificial Neural Network and Proper Orthogonal Decomposition approaches are combined to approximate and predict tunnelling induced time variant surface settlement fields computed by a process-oriented finite element simulation model. The surrogate models are generated, trained and tested in the design (offline) stage of a tunnel project based on finite element analyses to compute the surface settlements for selected scenarios of the tunnelling process steering parameters taking uncertain geotechnical parameters by means of possible ranges (intervals) into account. The resulting mappings of time constant geotechnical interval parameters and time variant deterministic steering parameters onto the time variant interval settlement field are solved offline by optimisation and online by interval analyses approaches using the midpoint-radius representation of interval data. During the tunnel construction, the surrogate model is designed to be used in real-time to predict interval fields of the surface settlements in each stage of the advancement of the tunnel boring machine for selected realisations of the steering parameters to support the steering decisions of the machine driver.

Citation

Freitag, S., Cao, B. T., Nini?, J., & Meschke, G. (2018). Recurrent neural networks and proper orthogonal decomposition with interval data for real-time predictions of mechanised tunnelling processes. Computers and Structures, 207, 258-273. https://doi.org/10.1016/j.compstruc.2017.03.020

• Journal Article Type: Article
• Acceptance Date: Mar 27, 2017
• Online Publication Date: Apr 12, 2017
• Publication Date: Sep 1, 2018
• Deposit Date: Apr 19, 2017
• Publicly Available Date: Apr 19, 2017
• Journal: Computers and Structures
• Electronic ISSN: 0045-7949
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 207
• Pages: 258-273
• DOI: https://doi.org/10.1016/j.compstruc.2017.03.020
• Public URL: https://nottingham-repository.worktribe.com/output/855432
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S0045794917302195
• Additional Information: This article is maintained by: Elsevier; Article Title: Recurrent neural networks and proper orthogonal decomposition with interval data for real-time predictions of mechanised tunnelling processes; Journal Title: Computers & Structures; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.compstruc.2017.03.020; Content Type: article; Copyright: © 2017 Elsevier Ltd. All rights reserved.