Next Article in Journal
Deep Reinforcement Learning Algorithms in Intelligent Infrastructure
Next Article in Special Issue
Hydro-Thermo-Mechanical Analysis of an Existing Gravity Dam Undergoing Alkali–Silica Reaction
Previous Article in Journal
An Integrated Uncertainty-Based Bridge Inspection Decision Framework with Application to Concrete Bridge Decks
Previous Article in Special Issue
Probabilistic Identification of Seismic Response Mechanism in a Class of Similar Arch Dams
Open AccessArticle

A Probabilistic Approach to the Spatial Variability of Ground Properties in the Design of Urban Deep Excavation

1
Department of Computing and Engineering, University of East London, London E16 2RD, UK
2
Fairhurst, Newcastle Upon Tyne NE4 6DB, UK
3
bei AECOM, 45127 Essen, Germany
4
MESLEC Ltd., Elstree WD6 3DP, UK
5
Faculty of Engineering, Civil Engineering Department, Urmia University, Urmia 5756151818, Iran
*
Author to whom correspondence should be addressed.
Infrastructures 2019, 4(3), 51; https://doi.org/10.3390/infrastructures4030051
Received: 30 June 2019 / Revised: 29 July 2019 / Accepted: 8 August 2019 / Published: 12 August 2019
(This article belongs to the Special Issue Advances in Dam Engineering)
Uncertainty in ground datasets often stems from spatial variability of soil parameters and changing groundwater regimes. In urban settings and where engineering ground interventions need to have minimum and well-anticipated ground movements, uncertainty in ground data leads to uncertain analysis, with substantial unwelcomed economical and safety implications. A probabilistic random set finite element modelling (RSFEM) approach is used to revisit the stability and serviceability of a 27 m deep submerged soil nailed excavation built into a cemented soil profile, using a variable water level and soil shear strength. Variation of a suite of index parameters, including mobilized working loads and moments in facing and soil inclusion elements, as well as stability and serviceability of facing and the integrated support system, are derived and contrasted with field monitoring data and deterministic FE modelling outputs. The validated model is then deployed to test the viability of using independent hydraulic actions as stochastic variables as an alternative to dependent hydraulic actions and soil shear strength. The achieved results suggest that utilizing cohesion as a stochastic variable alongside the water level predicts system uncertainty reasonably well for both actions and material response; substituting the hydraulic gradient produces a conservative probability range for the action response only. View Full-Text
Keywords: stochastic; probabilistic; excavation; movement; field; groundwater; soil nail; spatial; variability stochastic; probabilistic; excavation; movement; field; groundwater; soil nail; spatial; variability
Show Figures

Graphical abstract

MDPI and ACS Style

Herridge, J.B.; Tsiminis, K.; Winzen, J.; Assadi-Langroudi, A.; McHugh, M.; Ghadr, S.; Donyavi, S. A Probabilistic Approach to the Spatial Variability of Ground Properties in the Design of Urban Deep Excavation. Infrastructures 2019, 4, 51.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop