Probabilistic seismic slope stability analysis using swarm response surfaces and rotational Newmark sliding model with primary sliding direction

Li, Liang; Li, Chunli; Wen, Jiahui; Yu, Guangming; Cheng, Yungming; Xu, Liang

doi:10.1016/j.compgeo.2023.105754

Cited by 5 publications

(4 citation statements)

References 32 publications

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“…Numerous studies have been conducted offering valuable insights into the analysis of slope seismic stability [1][2][3][4][5][6]. However, certain limitations and shortcomings persist, which will be discussed in the following sections.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, DUBLA is utilized for slope seismic stability analysis to overcome the limitations of traditional methods and provide accurate and efficient results. (3) The probabilistic analysis of slope seismic stability employs various methods, including Monte Carlo Simulation (MCS) [2][3][4], Subset Simulation (SS) [12], and the First Order Reliability Method (FORM) [20]. However, sampling methods like MCS and SS are time-consuming as they require multiple simulations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Efficient Solution for Probabilistic Slope Seismic Stability Analysis Based on Polynomial Chao Kriging Metamodel

Zhang,

Dias

2024

GeoHazards

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Slope stability analysis plays a crucial role in geotechnical engineering, particularly in regions susceptible to seismic activity. The inherent non-homogeneity and uncertainty of soil properties pose significant challenges in assessing slope stability under seismic conditions. To address these complexities, a novel and efficient methodology named DUBLA-PDM-PCK is proposed. In this methodology, the effects of soil non-homogeneity and uncertainty, along with the time and spatial variations of seismic loading, are systematically considered. The deterministic framework integrates discretized upper bound limit analysis (DUBLA) to accommodate soil non-homogeneous characteristics, and the pseudo-dynamic method (PDM) to model seismic loading variability. Then, a robust and efficient probabilistic analysis method, PCK-MA, is implemented utilizing adaptive Polynomial Chaos Kriging metamodeling, Monte Carlo Simulation, and Analysis of Covariance to investigate the uncertainty of the parameters. This approach treats nine key parameters, including soil cohesion, friction angle, non-homogeneous coefficients, horizontal and vertical seismic coefficients, period, and amplification factor, as random variables to assess their uncertainty effects on failure probability (stability level) and sensitivity indices. The DUBLA-PDM-PCK methodology offers a streamlined and reliable tool tailored for assessing slope stability in seismic environments, demonstrating notable efficiency in addressing soil variability and seismic loading uncertainties. Its application holds promise for guiding engineering practices and enhancing understanding of slope behavior in regions prone to seismic hazards.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Efficient Solution for Probabilistic Slope Seismic Stability Analysis Based on Polynomial Chao Kriging Metamodel

Zhang,

Dias

2024

GeoHazards

View full text Add to dashboard Cite

show abstract

“…Despite the merits, a large number of calculations are mandatory for the small target failure probability problem. An alternative method is the use of surrogate models in MCS, such as Response Surface Method (RSM) (Li et al, 2023;Liu et al, 2023b), Support Vector Machine (SVM) (Tan et al, 2011;Chen et al, 2009), Artificial Neural Network (ANN) (L€ u et al, 2012), Convolutional Neural Network (CNN) (Jiang et al, 2023, Wang andGoh, 2021), Random Forest (RF) (Baudron et al, 2013;Liu et al, 2021b;Nouri et al, 2021), to enhance the computational inefficiency in MCS. The implementation of surrogate models into MCS has proven to be an efficient tool in slope reliability analysis.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Seismic reliability analysis of slope in spatially variable soils using multiple response surfaces

Li,

Cheng

et al. 2023

Self Cite

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PurposeThis paper aims to develop an efficient algorithm combining straightforward response surface functions with Monte Carlo simulation to conduct seismic reliability analysis in a systematical way.Design/methodology/approachThe representative slip surfaces are identified and based on to calibrate multiple response surface functions with acceptable accuracy. The calibrated response surfaces are used to determine the yield acceleration in Newmark sliding displacement analysis. Then, the displacement-based limit state function is adopted to conduct seismic reliability analysis.FindingsThe calibrated response surface functions have fairly good accuracy in predicting the yield acceleration in Newmark sliding displacement analysis. The seismic reliability is influenced by such factors as PGA, spatial variability and threshold value. The proposed methodology serves as an effective tool for geotechnical practitioners.Originality/valueThe multiple sources of a seismic slope response can be effectively determined using the multiple response surface functions, which are easily implemented within geotechnical engineering.

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Rainfall Induced Slope Reliability Analysis Using Radial Degraded Random Fields

Wang,

Li,

Kou

et al. 2024

Geotech Geol Eng

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Probabilistic seismic slope stability analysis using swarm response surfaces and rotational Newmark sliding model with primary sliding direction

Cited by 5 publications

References 32 publications

An Efficient Solution for Probabilistic Slope Seismic Stability Analysis Based on Polynomial Chao Kriging Metamodel

An Efficient Solution for Probabilistic Slope Seismic Stability Analysis Based on Polynomial Chao Kriging Metamodel

Seismic reliability analysis of slope in spatially variable soils using multiple response surfaces

Rainfall Induced Slope Reliability Analysis Using Radial Degraded Random Fields

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