Probabilistic Analysis of Earthquake-induced Failure and Runout Behaviors of Rock Slopes with Discrete Fracture Network

Xing, Lei; Gong, Wenping; Li, Bing; Zhao, Chao; Tang, Huiming; Wang, Lei

doi:10.1016/j.compgeo.2023.105487

Cited by 9 publications

(3 citation statements)

References 65 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%

“…However, it is predominantly used for deterministic analyses, often overlooking parameter uncertainties such as variations in amplification factor or seismic period, aspects that will be explored in this study. (2) Deterministic methods commonly employed for slope seismic analysis include the limit equilibrium method (LEM) [14,15], numerical simulation [2,6], and upper bound limit analysis (UBLA) [1,16,17]. However, the LEM lacks the capability to consider the soil stress-strain relationship, which can lead to biased outcomes due to assumptions regarding failure surfaces and inter-slice forces [18].…”

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%

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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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