New design equations for estimation of ultimate bearing capacity of shallow foundations resting on rock masses
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- @Article{Alavi:2014:GF,
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author = "Amir H. Alavi and Ehsan Sadrossadat",
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title = "New design equations for estimation of ultimate
bearing capacity of shallow foundations resting on rock
masses",
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journal = "Geoscience Frontiers",
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year = "2014",
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keywords = "genetic algorithms, genetic programming, Rock mass
properties, Ultimate bearing capacity, Shallow
foundation, Prediction, Evolutionary computation",
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ISSN = "1674-9871",
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DOI = "doi:10.1016/j.gsf.2014.12.005",
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URL = "http://www.sciencedirect.com/science/article/pii/S1674987114001625",
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abstract = "Rock masses are commonly used as the underlying layer
of important structures such as bridges, dams and
transportation constructions. The success of a
foundation design for such structures mainly depends on
the accuracy of estimating the bearing capacity of rock
beneath them. Several traditional numerical approaches
are proposed for the estimation of the bearing capacity
of foundations resting on rock masses to avoid
performing elaborate and expensive experimental
studies. Despite this fact, there still exists a
serious need to develop more robust predictive models.
This paper proposes new nonlinear prediction models for
the ultimate bearing capacity of shallow foundations
resting on non-fractured rock masses using a novel
evolutionary computational approach, called linear
genetic programming. A comprehensive set of rock
socket, centrifuge rock socket, plate load and
large-scaled footing load test results is used to
develop the models. In order to verify the validity of
the models, the sensitivity analysis is conducted and
discussed. The results indicate that the proposed
models accurately characterise the bearing capacity of
shallow foundations. The correlation coefficients
between the experimental and predicted bearing capacity
values are equal to 0.95 and 0.96 for the best LGP
models. Moreover, the derived models reach a notably
better prediction performance than the traditional
equations.",
- }
Genetic Programming entries for
A H Alavi
Ehsan Sadrossadat
Citations