Foundation settlement statistics via finite element analysis

Nour, Ali; Slimani, A.; Laouami, Nasser

doi:10.1016/s0266-352x(02)00014-9

Cited by 56 publications

(21 citation statements)

References 18 publications

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“…When a → ∞ as shown by Figure 11, the power spectral density function is defined in a very narrow band wavenumber range (Dirac), the wavenumber step becomes very small and approaches zero, hence wavelength of the simulated field tends to infinity according to vertical direction, and all points on the field are completely correlated, which corresponds to a homogenization of the simulated medium. In this situation, elastic modulus and soil permeability tend respectively to μ E / 1 + CV 2 E and μ k / 1 + CV 2 k everywhere (Nour et al, 2002) but each realization will be different.…”

Section: Vertical Correlation Length Influencementioning

confidence: 97%

“…Thus, the desired values at the coordinates z C are obtained using the neighbor Spline interpolation technique in the g(z r ) field as stated in (Nour et al, 2002).…”

Section: Computer Implementationmentioning

confidence: 99%

“…In this context, but using different approaches, one mainly notes contributions of Koppula (1988) for the development and dissipation of excess pore pressure in soil under external loads for a random variable representation of the consolidation coefficient, Fenton and Griffiths (2002) for the probabilistic foundation settlement on spatially random soil and Nour et al (2002) for foundation settlement statistics via finite element analysis.…”

Section: Introductionmentioning

confidence: 99%

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Consolidation Statistics Investigation via Thin Layer Method Analysis

et al. 2006

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In this paper, the Thin Layer Method (TLM) is adapted for solving one-dimensional primary consolidation problems. It is also combined with a stochastic formulation integrating Monte Carlo simulations to investigate primary consolidation of a random heterogeneous soil profile. This latter is modeled as a set of superposed layers extending horizontally to infinity, and having random properties. Spatial variability of soil properties is considered in the vertical direction only. Soil properties of interest are elastic modulus and soil permeability, modeled herein as spatially random fields. Lognormal distribution is chosen because it is suitable for strictly non-negative random variables, and enables analyzing the large variability of such properties. The statistics regarding final settlement and its corresponding time are investigated by performing a parametric study, which integrates the influence of variation coefficient of both elastic modulus and soil permeability, and vertical correlation length. Obtained results indicate that heterogeneity significantly influences primary consolidation of the soil profile, generating a quite different way of soil grain rearrangement and water pressure dissipation in comparison to the homogeneous case, and causing a delay in the consolidation process.

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Section: Vertical Correlation Length Influencementioning

confidence: 97%

“…Thus, the desired values at the coordinates z C are obtained using the neighbor Spline interpolation technique in the g(z r ) field as stated in (Nour et al, 2002).…”

Section: Computer Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Consolidation Statistics Investigation via Thin Layer Method Analysis

et al. 2006

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“…In this study, the soil parameters were considered to be lognormally distributed random variables to account for the possibility of a large variation coefficient. Lognormal distribution ensures that the random variable remains positive, and has been adopted by several other researchers to represent the statistical properties of soil parameters (Parkin et al 1988;Brejda et al 2000;Gui et al 2000;Nour et al 2002;Massih et al 2008). …”

Section: Probabilistic Modeling Of a Rainfall-induced Shallow Landslidementioning

confidence: 99%

Probabilistic modeling of rainfall-induced shallow landslide using a point-estimate method

Tsai

Yang

2014

Environ Earth Sci

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The assessment of rainfall-induced shallow landslides using a physical-based model requires accounting for the uncertainty of soil parameters that cannot be accurately quantified. In this study, the modified Iverson model was integrated with the Rosenblueth point-estimate method to probabilistically model rainfall-induced shallow landslides, while simultaneously considering the uncertainty and correlation of soil parameters. Using various soil parameters, hillslope conditions, and hydrological conditions, the applicability of the Rosenblueth point-estimate method was compared with that of the Monte Carlo simulation method. The simulated results indicated that the Rosenblueth point-estimate method can accurately and efficiently assess the probability of rainfall-induced shallow landslides. A lognormal distribution of the safety factor can be assumed when the soil parameters are lognormally distributed random variables. Accounting for the correlation of soil parameters decreases the standard deviation of the safety factor, but does not affect the mean of the safety factor. Neglecting the correlation of soil parameters could substantially misestimate the probability of rainfall-induced shallow landslides.

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“…Regarding the internal friction angle (u), the coefficient of variation proposed in the literature lies between 7% and 20%. The coefficient of variation of the Young's modulus (E) is estimated between 2% and 50% (Nour et al [19], and Baecher and Christian [20]). Concerning the coefficient of variation of Poisson's ratio (m), there is no sufficient information about its interval of variation, leading some authors to suggest that the variability of this parameter in the elastic soil settlement can be neglected, while others proposed a very limited range of variability (Youssef Abdel Massih [21]).…”

Section: Quantification Of Uncertainties Of Soil Parametersmentioning

confidence: 99%

Effect of soil–structure interaction on the reliability of reinforced concrete bridges

Bezih

Chateauneuf

Kalla

et al. 2015

Ain Shams Engineering Journal

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In the design of reinforced concrete (RC) bridges, the random and nonlinear behavior of soil may lead to insufficient reliability levels. For this reason, it is necessary to take into account the variability of soil properties which can significantly affect the bridge behavior regarding ultimate and serviceability limit states. This study investigates the failure probability for existing reinforced concrete bridges due to the effects of interaction between the soil and the structure. In this paper, a coupled reliability-mechanical approach is developed to study the effect of soil-structure interaction for RC bridges. The modeling of this interaction is incorporated into the mechanical model of RC continuous beams, by considering nonlinear elastic soil stiffness. The reliability analysis highlights the large importance of soil-structure interaction and shows that the structural safety is highly sensitive to the variability of soil properties, especially when the nonlinear behavior of soil is considered. Ó 2015 Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Foundation settlement statistics via finite element analysis

Cited by 56 publications

References 18 publications

Consolidation Statistics Investigation via Thin Layer Method Analysis

Consolidation Statistics Investigation via Thin Layer Method Analysis

Probabilistic modeling of rainfall-induced shallow landslide using a point-estimate method

Effect of soil–structure interaction on the reliability of reinforced concrete bridges

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