A comparative study of Bayesian inverse analyses of spatially varying soil parameters for slope reliability updating

Jiang, Shui‐Hua; Wang, Lei; Ouyang, Su; Huang, Jinsong; Liu, Yuan

doi:10.1080/17499518.2021.2010098

Cited by 7 publications

(4 citation statements)

References 44 publications

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“…However, incorporating the spatial variability of the soil into Bayesian updating introduces a significant increase in parameter dimensions, increasing, subsequently, the associated computational cost. 33 Conventional Bayesian methods often struggle to effectively address this issue 34 ; especially, maintaining the stationarity and achieving convergence of Markov chains in highdimensional spaces presents a formidable challenge. 35 The intricacy of Bayesian updating tends to increase when handling discrete outputs in high-dimensional spaces.…”

Section: Introductionmentioning

confidence: 99%

Experimental model updating of slope considering spatially varying soil properties and dynamic loading

Wang,

Ouyang,

Fragkoulis

et al. 2024

Earthquake Engineering and Resilience

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The widespread threat posed by slope structure failures to human lives and property safety is widely acknowledged. Additionally, natural soil often displays spatial variability due to geological deposition and other factors. Therefore, predicting the seismic response of slopes subjected to ground motions and inversely analyzing the spatial distribution of soils remains an unresolved issue. In the present work, a shaking table experimental test is first designed and carried out, in which a soft‐soil slope dynamic system is established. To capture the seismic response of the soft‐soil slope, specifically the experimental characteristic of acceleration and soil pressure response in both spatial domain and time domain, a series of sensors were pre‐embedded in the slope. Subsequently, a model updating approach is proposed for slope seismic analysis that incorporates spatial variability of soil. In addition, to enhance computational efficiency, the dimensionality reduction of Karhunen–Loève expansion method is introduced to reduce inverse analysis parameters. On the basis of 34 samples collected from experimental data, it is shown that near‐fault pulse‐like ground motions deliver greater concentrated energy, causing more severe damage to slope structures, especially the topsoil layer. Furthermore, using data obtained from a shaking table test subjected to ground motion Recorded Sequence Number 158H1 from the Pacific Earthquake Engineering Research Center NGA‐West2 database as an example, it is also shown that the proposed approach demonstrates high accuracy in predicting the spatial distribution of the maximum shear modulus in soil slope dynamic systems. The present work not only addresses the challenges posed by mainshock–aftershock effects but also highlights the potential of model updating approaches to enhance the understanding of slope behavior under seismic loading conditions.

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Section: Introductionmentioning

confidence: 99%

Experimental model updating of slope considering spatially varying soil properties and dynamic loading

Wang,

Ouyang,

Fragkoulis

et al. 2024

Earthquake Engineering and Resilience

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“…Reliability analysis has proved to be a useful tool for solving the challenging geotechnical problems. [17][18][19][20][21][22][23][24][25] Among a series of available methods, the straightforward and simple method is Monte Carlo simulation method (MCS) which involves the establishment of probabilistic models, (for example, a repetitive execution of Newmark sliding block model). [26][27][28][29][30] However, the MCS requires a large number of executions of deterministic model to obtain the rational result at small target failure probability, and the calculation efficiency deteriorates dramatically.…”

Section: Introductionmentioning

confidence: 99%

“…Reliability analysis has proved to be a useful tool for solving the challenging geotechnical problems 17–25 . Among a series of available methods, the straightforward and simple method is Monte Carlo simulation method (MCS) which involves the establishment of probabilistic models, (for example, a repetitive execution of Newmark sliding block model) 26–30 .…”

Section: Introductionmentioning

confidence: 99%

Seismic reliability analysis of soil slope based on Newmark sliding block model with representative slip surfaces and response surfaces

et al. 2023

Engineering Reports

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This paper presents a framework combining Monte Carlo Simulation (MCS) and the Newmark sliding block model with representative slip surfaces (RSS) (model II) and multiple response surfaces method (MRSM) to conduct seismic reliability analysis and risk assessment of soil slopes. An empirical threshold is introduced to define the limit state function to identify the failure samples in MCS and the sliding area and Newmark sliding displacement are multiplied to quantify the failure consequence. The proposed methodology is illustrated through a soil slope with multiple layers. The calculation results demonstrate that traditional Newmark sliding block model (model I) tend to underestimate the variations of yield acceleration. Both the failure probability and landslide risk exhibit decreasing trends with the increase of threshold. Significant discrepancy in failure probability and landslide risk between two models is found even for a small threshold. Therefore, the proposed methodology is highly recommended in seismic reliability analysis and risk assessment. The contributions of RSSs to the failure probability and landslide risk are insensitive to the variation of displacement thresholds.

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“…In addition, some scholars have applied the Bayesian method to spatially varying parameter inverse analyses [28,29]. On the basis of the estimation results of parameter fields, the deformation field can be simulated.…”

Section: Introductionmentioning

confidence: 99%

Ordinary Kriging Interpolation Method Combined with FEM for Arch Dam Deformation Field Estimation

Shao

Chen

et al. 2023

Mathematics

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The deformation characteristic of the arch dam can directly reflect its service performance, which can be analyzed on the basis of the dam deformation field. However, restricted by the limited number of dam monitoring points and the inhomogeneity of materials, an accurate measurement of arch dam deformation field is difficult to estimate by using the existing common methods, such as the spatial interpolation methods and the finite element method (FEM). With the aim of ensuring arch dam structure safety, the ordinary kriging interpolation method, combined with FEM, is proposed for arch dam deformation field estimation, in this study. Given the inversion of the computation parameters of the arch dam, FEM is used to calculate the basic arch dam deformation. Subsequently, the ordinary kriging interpolation method is introduced to estimate the spatial variance deformation at each point of the arch dam. One superhigh arch dam in China is selected as a case study; two additional methods are introduced as comparisons that are based on a numerical experiment and the actual monitoring data. The experimental results show that the proposed method considerably improves the accuracy and computational efficiency of the arch dam deformation field estimation and that it is of great practical importance for characterizing the deformation behavior of arch dams.

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A comparative study of Bayesian inverse analyses of spatially varying soil parameters for slope reliability updating

Cited by 7 publications

References 44 publications

Experimental model updating of slope considering spatially varying soil properties and dynamic loading

Experimental model updating of slope considering spatially varying soil properties and dynamic loading

Seismic reliability analysis of soil slope based on Newmark sliding block model with representative slip surfaces and response surfaces

Ordinary Kriging Interpolation Method Combined with FEM for Arch Dam Deformation Field Estimation

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