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

Geoscience Frontiers 2016年第1期7卷 91-99页

作者： Amir H.Alavi Ehsan Sadrossadat Department of Civil and Environmental Engineering Michigan State University Department of Civil Engineering Mashhad BranchIslamic Azad University

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 characterize 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.

关键词： Rock mass properties Ultimate bearing capacity Shallow foundation Prediction Evolutionary computation linear genetic programming

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Energy-based numerical models for assessment of soil liquefaction

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Geoscience Frontiers 2012年第4期3卷 541-555页

作者： Amir Hossein Alavi Amir Hossein Gandomi School of Civil Engineering lran University of Science and TechnologyTehranIran College of Civil Engineering Tafresh UniversityTafreshlran

This study presents promising variants of genetic programming （GP）, namely linear genetic programming （LGP） and multi expression programming （MEP） to evaluate the liquefaction resistance of san- dy soils. Generalized LGP and MEP-based relationships were developed between the strain energy density required to trigger liquefaction （capacity energy） and the factors affecting the liquefaction characteristics of sands. The correlations were established based on well established and widely dispersed experimental results obtained from the literature. To verify the applicability of the derived models, they were employed to estimate the capacity energy values of parts of the test results that were not included in the analysis. The external validation of the models was verified using statistical criteria recommended by researchers. Sensitivity and parametric analyses were performed for further verification of the correlations. The results indicate that the proposed correlations are effectively capable of capturing the liquefaction resistance of a number of sandy soils. The developed correlations provide a significantly better prediction performance than the models found in the literature. Furthermore, the best LGP and MEP models perform superior than the optimal traditional GP model. The verification phases confirm the efficiency of the derived correlations for their general application to the assessment of the strain energy at the onset of liquefaction.

关键词： Soil liquefaction Capacity energy linear genetic programming Multi expression programming Sand Formulation

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