Stochastic Failure of a Double Eccentricity Footing Settlement on Cohesive Soils with a Modified Cam Clay Yield Surface

Savvides, Ambrosios-Antonios

doi:10.1007/s11242-021-01731-x

Transp Porous Med

2022

DOI: 10.1007/s11242-021-01731-x

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Stochastic Failure of a Double Eccentricity Footing Settlement on Cohesive Soils with a Modified Cam Clay Yield Surface

Ambrosios-Antonios Savvides

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2024

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Cited by 2 publications

References 52 publications

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A neural network approach for the reliability analysis on failure of shallow foundations on cohesive soils

Savvides,

Papadopoulos

2024

Geo-Engineering

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A collection of feed forward neural networks (FNN) for estimating the limit pressure load and the according displacements at limit state of a footing settlement is presented. The training procedure is through supervised learning with error loss function the mean squared error norm. The input dataset is originated from Monte Carlo simulations for a variety of loadings and stochastic uncertainty of the material of the clayey soil domain. The material yield function is the Modified Cam Clay model. The accuracy of the FNN’s is in terms of relative error no more than $$10^{-5}$$ 10 - 5 and this applies to all output variables. Furthermore, the epochs of the training of the FNN’s required for construction are found to be small in amount, in the order of magnitude of 90,000, leading to an alleviated data cost and computational expense. The input uncertainty of Karhunen Loeve random field sum appears to provide the most detrimental values for the displacement field of the soil domain. The most unfavorable situation for the displacement field result to limit displacements in the order of magnitude of 0.05 m, that may result to structural collapse if they appear to the founded structure. These series can provide an easy and reliable estimation for the failure of shallow foundation and therefore it can be a useful implement for geotechnical engineering analysis and design.

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A neural network approach for the reliability analysis on failure of shallow foundations on cohesive soils

Savvides,

Papadopoulos

2024

Geo-Engineering

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A Neural Network Model for Estimation of Failure Stresses and Strains in Cohesive Soils

Savvides

Papadopoulos

2022

Geotechnics

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In this article, a set of neural networks for the prediction of the stresses and the corresponding strains at failure of cohesive soils when subjected to a load of a shallow foundation are presented. The data are acquired via Monte Carlo analyses for different types of loadings and stochastic input material variabilities, and by adopting the clayey soil domain and modified Cam Clay material yield function. The mathematical functions for the estimation of the failure stresses and strains are computed with the feed forward neural network method (FNN). It is demonstrated that the accuracy of the derived relations is in the order of a maximum relative error of 10−5 in all monitored output variables. In addition, the number of training epochs required for convergence is relatively low and this means that the computational and data costs for the construction of the FNN are low. The critical input variable for the estimation of the most unfavorable situations is the Karhunen Loeve series expansion for porous analyses, while for non-porous analyses the constant distribution over depth is the one that provides more critical estimations for the monitored output variables of stresses and strains at failure. This set of functions can estimate the aforementioned variables of the footing settlement in clays with high accuracy; consequently, it can be an important tool for geotechnical engineering design, especially in providing the largest stress allowed from the foundation.

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Stochastic Failure of a Double Eccentricity Footing Settlement on Cohesive Soils with a Modified Cam Clay Yield Surface

Cited by 2 publications

References 52 publications

A neural network approach for the reliability analysis on failure of shallow foundations on cohesive soils

A neural network approach for the reliability analysis on failure of shallow foundations on cohesive soils

A Neural Network Model for Estimation of Failure Stresses and Strains in Cohesive Soils

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