Stochastic seepage analysis in embankment dams using different types of random fields

Chi, Fengdong; Breul, Pierre; Carvajal, Claudio; Peyras, Laurent

doi:10.1016/j.compgeo.2023.105689

Cited by 9 publications

(3 citation statements)

References 41 publications

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“…The study found that the probabilities of failure for different cutoff wall configurations are similar when considering isotropic permeability, but there are noticeable differences in anisotropic situations. Chi et al (2023) compared the effects of three types of permeability RFs (stationary, conditional, and non-stationary RFs) on hydraulic parameters such as flow velocity, hydraulic gradient, pore water pressure, and flow rate in stochastic seepage analysis. The results showed that the different types of permeability RFs had a significant impact on flow velocity, a moderate impact on hydraulic gradient, and a minor impact on pore water pressure.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic analysis of backward erosion piping in an embankment dam considering the spatial variability of soil properties

CHI,

CARVAJAL,

BREUL

et al. 2024

Preprint

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The spatial variability of soil seepage parameters is an essential component of the uncertainty of earth dams. This paper presents a method for probabilistic backward erosion analysis that considers the spatial variability of seepage parameters in earth dams to assess the distribution of the Factor of Safety (FoS) and the failure probability (Pf). In this study, the Karhunen-Loeve method is used to generate Random Fields (RFs) of soil hydraulic properties, and Monte Carlo simulations (MCs) are used to generate realisations of these RFs, which are then introduced into the numerical model to assess the spatial variability of the seepage analysis results. The hydraulic gradient distribution is compared with the critical hydraulic gradient to assess the FoS and the Pf due to backward erosion at each point of the dam. This study also presents the effect of water level H, permeability anisotropy coefficient ξ, and RFs parameters (permeability covariance COVKs, correlation length Lh & Lv) on the FoS and Pf of Backward Erosion Piping (BEP). The results illustrate that the failure probability Pf increases with H, COVKs and ξ. The failure probability Pf is not sensitive to the vertical correlation length, but may decrease with the horizontal correlation length. Additionally, the study discusses on the potential location of the initiation of internal erosion as well as the results obtained with criteria based on the local and global hydraulic gradient.

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Section: Introductionmentioning

confidence: 99%

Probabilistic analysis of backward erosion piping in an embankment dam considering the spatial variability of soil properties

CHI,

CARVAJAL,

BREUL

et al. 2024

Preprint

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“…Lu et al [23] and Li et al [24] determined the influence laws of concrete material parameters on the dynamic response characteristics of gravity dams based on the aspects of damage area distribution, crack length statistics, displacement in the crest, energy dissipation, and parameter sensitivity. Chi et al [25] performed a stochastic analysis of seepage using three types of random fields, and the application showed that the spatial variability of hydraulic conductivity induces differences using different random fields. However, it should be noted that there are very few studies on dam risk analysis considering the spatial variability of the mechanical parameters of rock masses and concrete.…”

Section: Introductionmentioning

confidence: 99%

A Method for Evaluating Systematic Risk in Dams with Random Field Theory

Ran,

Zhou,

Pei

et al. 2024

Applied Sciences

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The parameters of gravity dams and foundation materials objectively exhibit spatial variability due to environmental and load influences, which significantly affect the safety status of dam structures. Therefore, a safety risk analysis method for a gravity dam–foundation system based on random field theory is proposed in this paper. Spatial variabilities in materials are particularly considered by using the finite element method. Then, composite response surface equations for the performance function (PF) of strength and stability failure are established, and then, the system failure risk is obtained using the Monte Carlo method. The proposed method solves the problem wherein the effect of spatial variability on failure risk cannot be reflected accurately by the performance function of multi-element sliding paths, and the difficulties in solving the failure risk of the series–parallel system due to multiple failure paths and their complex correlations. The application of a gravity dam shows that the developed method overcomes the disadvantages of the traditional method, such as the homogenization of the spatially random characteristics of parameters and the overestimation of failure risk in the system due to large variance estimation.

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“…The probabilistic analysis method is useful for evaluating the reliability and fragility of hydraulic structures because it can quantitatively consider the uncertainty and spatial variability of soil hydraulic conductivity in the analysis and design of seepage stability. Several researchers have conducted studies on probabilistic seepage analysis, which considered the spatial variability of soil properties [16][17][18][19][20][21][22][23][24][25][26]. Concerning seepage flow underneath a retaining structure, Griffiths and Fenton [27,28] considered the effect of spatial variability of hydraulic conductivity by combining the finite element method with the random field theory.…”

Section: Introductionmentioning

confidence: 99%

Fragility Analysis for Water-Retaining Structures Resting on Spatially Random Soil

Cho

2023

Water

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The maintenance of water-retaining structures involves evaluating their performance against current and future operating water levels. Fragility curves are commonly used for this purpose, as they indicate the conditional probability of failure for various load conditions and accurately characterize a structure’s performance. Monte Carlo simulation (MCS) can be used to determine the fragility curve of water-retaining structures by calculating the probability of failure as the water level changes. However, performing repetitive MCS involves extensive calculations, thus making it inefficient for practical applications. Therefore, it is essential to develop efficient methods that require a minimum number of MCS runs to estimate the fragility curve. This study proposed two methods to estimate the fragility curves of water-retaining structures, thereby allowing for the assessment of failure probabilities related to important quantities such as the steady-state seepage rate, exit gradient, and uplift force, which make them suitable for practical applications. The fragility curves obtained using the proposed methods are valuable for risk assessment, design, and decision-making purposes, as they offer information to evaluate the performance of the water-retaining structure under various water level conditions.

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Stochastic seepage analysis in embankment dams using different types of random fields

Cited by 9 publications

References 41 publications

Probabilistic analysis of backward erosion piping in an embankment dam considering the spatial variability of soil properties

Probabilistic analysis of backward erosion piping in an embankment dam considering the spatial variability of soil properties

A Method for Evaluating Systematic Risk in Dams with Random Field Theory

Fragility Analysis for Water-Retaining Structures Resting on Spatially Random Soil

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