Sensitivity Analysis of Soil Liquefaction Potential

Ayad, Fatima; Bekkouche, Abdelmalek; Houmadi, Youcef

doi:10.5539/esr.v3n1p14

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…The mean value of A was set to 2060, in line with the work conducted by Liu and Zhang 53 . A coefficient of variation (COV) of 0.2 is adopted for A in agreement with the COV values reported by Schevenels et al 57 and Ayad et al, 58 who also modeled G max as a random field. It is important to note that the mean effective normal stress

{p}^{^{\prime} }

in Equation () varies with depth, typically increasing, which leads to additional variability in G max .…”

Section: Numerical Implementation Of Model Updating Approachmentioning

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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{p}^{^{\prime} }

in Equation () varies with depth, typically increasing, which leads to additional variability in G max .…”

Section: Numerical Implementation Of Model Updating Approachmentioning

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

View full text Add to dashboard Cite

show abstract

“…For instance, Hamdia et al [46] carried out a sensitivity analysis to determine the key input parameters impacting the relation between tissue structure and mechanics. Ayad et al [47] performed a sensitivity analysis and showed that soil's parameter variability has a significant effect on soil liquefaction probability.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Evaluating Seismic Soil Liquefaction Potential Using Bayesian Belief Network and C4.5 Decision Tree Approaches

et al. 2019

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Liquefaction is considered a damaging phenomenon of earthquakes and a major cause of concern in civil engineering. Therefore, its predictory assessment is an essential task for geotechnical experts. This paper investigates the performance of Bayesian belief network (BBN) and C4.5 decision tree (DT) models to evaluate seismic soil liquefaction potential based on the updated and relatively large cone penetration test (CPT) dataset (which includes 251 case histories), comparing them to a simplified procedure and an evolutionary-based approach. The BBN model was developed using the K2 machine learning algorithm and domain knowledge (DK) with data fusion methodology, while the DT model was created using a C4.5 algorithm. This study shows that the BBN model is preferred over the others for evaluation of seismic soil liquefaction potential. Owing to its overall performance, simplicity in practice, data-driven characteristics, and ability to map interactions between variables, the use of a BBN model in assessing seismic soil liquefaction is quite promising. The results of a sensitivity analysis show that ‘equivalent clean sand penetration resistance’ is the most significant factor affecting liquefaction potential. This study also interprets the probabilistic reasoning of the robust BBN model and most probable explanation (MPE) of seismic soil liquefied sites, based on an engineering point of view.

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Liquefaction hazard mapping and relationship between probability of liquefaction and factor of safety for Kolkata city, India

2023

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Sensitivity Analysis of Soil Liquefaction Potential

Cited by 5 publications

References 23 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

Evaluating Seismic Soil Liquefaction Potential Using Bayesian Belief Network and C4.5 Decision Tree Approaches

Liquefaction hazard mapping and relationship between probability of liquefaction and factor of safety for Kolkata city, India

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