New design equations for estimation of ultimate bearing capacity of shallow foundations resting on rock masses

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@Article{Alavi:2014:GF,
  author =       "Amir H. Alavi and Ehsan Sadrossadat",
  title =        "New design equations for estimation of ultimate
                 bearing capacity of shallow foundations resting on rock
                 masses",
  journal =      "Geoscience Frontiers",
  year =         "2014",
  keywords =     "genetic algorithms, genetic programming, Rock mass
                 properties, Ultimate bearing capacity, Shallow
                 foundation, Prediction, Evolutionary computation",
  ISSN =         "1674-9871",
  DOI =          "doi:10.1016/j.gsf.2014.12.005",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1674987114001625",
  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