System reliability analysis of foundation stability of gravity dams considering anisotropic seepage and multiple sliding surfaces

He, Jianan; Yang, Di; Wu, Zhenyu

doi:10.1108/ec-08-2021-0488

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…The above studies show that the random variable model is usually adopted in the risk or reliability analysis of concrete gravity dams and foundations [19,20], where the spatial variability of material parameters is homogenized by statistical characteristics. However, the rock mass in the foundation is generally natural material, the engineering characteristics of which are significantly affected by deposition, chemical weathering, physical degradation, hydrothermal change, and loading history, performing obvious spatial variability in the material parameters [21].…”

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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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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show abstract

Hypoplastic constitutive model of inherently anisotropic sand

Fan,

2024

Computers and Geotechnics

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A double-loop adaptive relevant vector machine combined with Harris Hawks optimization-based importance sampling

Fan,

Liu,

et al. 2024

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PurposeEnsuring the safety of structures is important. However, when a structure possesses both an implicit performance function and an extremely small failure probability, traditional methods struggle to conduct a reliability analysis. Therefore, this paper proposes a reliability analysis method aimed at enhancing the efficiency of rare event analysis, using the widely recognized Relevant Vector Machine (RVM).Design/methodology/approachDrawing from the principles of importance sampling (IS), this paper employs Harris Hawks Optimization (HHO) to ascertain the optimal design point. This approach not only guarantees precision but also facilitates the RVM in approximating the limit state surface. When the U learning function, designed for Kriging, is applied to RVM, it results in sample clustering in the design of experiment (DoE). Therefore, this paper proposes a FU learning function, which is more suitable for RVM.FindingsThree numerical examples and two engineering problem demonstrate the effectiveness of the proposed method.Originality/valueBy employing the HHO algorithm, this paper innovatively applies RVM in IS reliability analysis, proposing a novel method termed RVM-HIS. The RVM-HIS demonstrates exceptional computational efficiency, making it eminently suitable for rare events reliability analysis with implicit performance function. Moreover, the computational efficiency of RVM-HIS has been significantly enhanced through the improvement of the U learning function.

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System reliability analysis of foundation stability of gravity dams considering anisotropic seepage and multiple sliding surfaces

Cited by 3 publications

References 34 publications

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

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

Hypoplastic constitutive model of inherently anisotropic sand

A double-loop adaptive relevant vector machine combined with Harris Hawks optimization-based importance sampling

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