Finite element modelling of pipe piles driven in low-to-medium density chalk under monotonic axial loading
File(s)COMGE-S-24-00417_Clean Version.pdf (1.15 MB)
Accepted version
Author(s)
Wen, Kai
Kontoe, Stavroula
Jardine, Richard
Liu, Tingfa
Type
Journal Article
Abstract
Percussive driving in low-to-medium density chalk creates a thin annulus of fully de-structured ‘putty’ chalk around pile shafts and an outer annular zone where fracturing is more intense than in the natural chalk. This damage and the related generation, and subsequent equalisation, of excess pore pressures impacts the piles’ time-dependent axial loading behaviour, as do other ageing processes. This paper presents finite element analyses of open steel tubular piles driven for the recent ALPACA and ALPACA Plus research projects under monotonic axial loading to failure after extended ageing periods. A nonlinear elastic stiffness model with a nonlocal deviatoric strain-based Mohr-Coulomb failure criterion was employed, with different sets of properties to represent the de-structured, fractured chalk and intact chalk. It is shown that the piles’ axial responses are mainly controlled by the puttified chalk annuli. A simplified but efficient means is adopted to impose pre-loading chalk effective stress conditions that explicitly capture the effects of installation damage and subsequent ageing. The potential for strain-softening in the brittle chalk is examined and the effective stress paths developed in representative chalk elements throughout the loading are considered in conjunction with the mobilisation of accumulated deviatoric strains. The simulations indicate 264% higher shaft capacity in compression than in tension, which is mainly attributed to the internal chalk plug.
Date Issued
2024-08
Date Acceptance
2024-05-21
Citation
Computers and Geotechnics, 2024, 172
ISSN
0266-352X
Publisher
Elsevier
Journal / Book Title
Computers and Geotechnics
Volume
172
Copyright Statement
Copyright © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Identifier
http://dx.doi.org/10.1016/j.compgeo.2024.106458
Publication Status
Published
Article Number
106458
Date Publish Online
2024-05-27