Effect of soil variability on the bearing capacity of footings on multi-layered soil.
Date
2009
Authors
Kuo, Yien Lik
Editors
Advisors
Jaksa, Mark
Kaggwa, William
Kaggwa, William
Journal Title
Journal ISSN
Volume Title
Type:
Thesis
Citation
Statement of Responsibility
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Abstract
Footings are often founded on multi-layered soil profiles. Real soil profiles are often
multi-layered with material constantly varying with depth, which affects the footing
response significantly. Furthermore, the properties of the soil are known to vary with
location. The spatial variability of soil can be described by random field theory and
geostatistics. The research presented in this thesis focuses on quantifying the effect of
soil variability on the bearing capacity of rough strip footings on single and two layered,
purely-cohesive, spatially variable soil profiles. This has been achieved by
using Monte Carlo analysis, where the rough strip footings are founded on simulated
soil profiles are analysed using finite element limit analysis. The simulations of
virtual soil profiles are carried out using Local Average Subdivision (LAS), a
numerical model based on the random field theory. An extensive parametric study
has been carried out and the results of the analyses are presented as normalized means
and coefficients of variation of bearing capacity factor, and comparisons between
different cases are presented. The results indicate that, in general, the mean of the
bearing capacity reduces as soil variability increases and the worst case scenario
occurs when the correlation length is in the range of 0.5 to 1.0 times the footing width.
The problem of estimating the bearing capacity of shallow strip footings founded on
multi-layered soil profiles is very complex, due to the incomplete knowledge of
interactions and relationships between parameters. Much research has been carried
out on single- and two-layered homogeneous soil profiles. At present, the inaccurate
weighted average method is the only technique available for estimating the bearing
capacity of footing on soils with three or more layers. In this research, artificial
neural networks (ANNs) are used to develop meta-models for bearing capacity
estimation. ANNs are numerical modelling techniques that imitate the human brain
capability to learn from experience. This research is limited to shallow strip footing
founded on soil mass consisting of ten layers, which are weightless, purely cohesive
and cohesive-frictional.
A large number of data has been obtained by using finite element limit analysis.
These data are used to train and verify the ANN models. The shear strength (cohesion
and friction angle), soil thickness, and footing width are used as model inputs, as they
are influencing factors of bearing capacity of footings. The model outputs are the
bearing capacities of the footings. The developed ANN-based models are then
compared with the weighted average method. Hand-calculation design formulae for
estimation of bearing capacity of footings on ten-layered soil profiles, based on the
ANN models, are presented. It is shown that the ANN-based models have the ability
to predict the bearing capacity of footings on ten-layered soil profiles with a high
degree of accuracy, and outperform traditional methods.
School/Discipline
School of Civil, Environmental and Mining Engineering
Dissertation Note
Thesis (Ph.D.) - University of Adelaide, School of Civil, Environmental and Mining Engineering, 2009
Provenance
Copyright material removed from digital thesis. See print copy in University of Adelaide Library for full text.