Prediction of strain energy-based liquefaction resistance of sand-silt mixtures: An evolutionary approach
Created by W.Langdon from
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- @Article{Baziar2011,
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author = "Mohammad H. Baziar and Yaser Jafarian and
Habib Shahnazari and Vahid Movahed and
Mohammad Amin Tutunchian",
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title = "Prediction of strain energy-based liquefaction
resistance of sand-silt mixtures: An evolutionary
approach",
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journal = "Computer \& Geosciences",
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volume = "37",
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number = "11",
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pages = "1883--1893",
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year = "2011",
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ISSN = "0098-3004",
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DOI = "doi:10.1016/j.cageo.2011.04.008",
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URL = "http://www.sciencedirect.com/science/article/B6V7D-52R9DF5-2/2/08fa46566f649fc2348af34aa83ebbb2",
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keywords = "genetic algorithms, genetic programming, Liquefaction,
Capacity energy, Sand, Silt, Wildlife",
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abstract = "Liquefaction is a catastrophic type of ground failure,
which usually occurs in loose saturated soil deposits
under earthquake excitations. A new predictive model is
presented in this study to estimate the amount of
strain energy density, which is required for the
liquefaction triggering of sand-silt mixtures. A
wide-ranging database containing the results of cyclic
tests on sand-silt mixtures was first gathered from
previously published studies. Input variables of the
model were chosen from the available understandings
evolved from the previous studies on the strain
energy-based liquefaction potential assessment. In
order to avoid over training, two sets of validation
data were employed and a particular monitoring was made
on the behaviour of the evolved models. Results of a
comprehensive parametric study on the proposed model
are in accord with the previously published
experimental observations. Accordingly, the amount of
strain energy required for liquefaction onset increases
with increase in initial effective overburden pressure,
relative density, and mean grain size. The effect of
nonplastic fines on strain energy-based liquefaction
resistance shows a more complicated behavior.
Accordingly, liquefaction resistance increases with
increase in fines up to about 10-15percent and then
starts to decline for a higher increase in fines
content. Further verifications of the model were
carried out using the valuable results of some down
hole array data as well as centrifuge model tests.
These verifications confirm that the proposed model,
which was derived from laboratory data, can be
successfully used under field conditions.",
- }
Genetic Programming entries for
Mohammad Hassan Baziar
Yaser Jafarian
Habib Shahnazari
Vahid Movahed
Mohammad Amin Tutunchian
Citations