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Civil-Comp Proceedings
ISSN 1759-3433 CCP: 105
PROCEEDINGS OF THE NINTH INTERNATIONAL CONFERENCE ON ENGINEERING COMPUTATIONAL TECHNOLOGY Edited by:
Paper 45
Modeling of Soil Structure Interaction during Tunnel Excavation considering Uncertainties T. Janda, J. Šejnoha, V. Srnec and M. Šejnoha
Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republi , "Modeling of Soil Structure Interaction during Tunnel Excavation considering Uncertainties", in , (Editors), "Proceedings of the Ninth International Conference on Engineering Computational Technology", Civil-Comp Press, Stirlingshire, UK, Paper 45, 2014. doi:10.4203/ccp.105.45
Keywords: soil-structure interaction, 2D3D method, excavation, convergence measurements, Bayesian update, likelihood function, Markov chain Monte Carlo method.
Summary
Several advances in the modeling of soil-structure interaction in the light of tunnel
excavation are discussed. The theoretical formulation as well as numerical implementation
are presented such as to meet the following requirements: simplicity, reliability
and practical applicability to make the resulting software product available for a direct
use on the construction site. To that end a recently introduced 2D3D modeling of
tunnel excavation incorporating the in-situ measurements and two-dimensional computational
framework of an underground structure is effectively combined with a fully
three-dimensional (3D) beam-like representation of a typical urban structure made of
masonry or precast concrete. Assuming this model provides an "exact solution" we
propose a new reduced model of an underground structure to be yet considerably more
efficient since based only on a limited number of model parameters and despite of that
providing useful results as it can easily be calibrated against the 2D3D model such as
to match the underground settlement as close as possible to reality. The efficiency of
the latter model is subsequently exploited in the stochastic analysis of the excavation
process utilizing the elements of Bayesian statistical methods in improving the distributions
of model parameters. While details of the calibration step of the reduced
model are provided, the stochastic analysis is addressed in this paper only methodologically.
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