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

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