Failure mechanism, existing constitutive models and numerical modeling of landslides in sensitive clay: a review

Geoenvironmental Disasters

Review

Ali Saeidi¹  Rama Vara Prasad Chavali¹  Zinan Ara Urmi¹  Alba Yerro² 

[1] Université du Québec à Chicoutimi, 555 Bd de L’Université, Chicoutimi, G7H 2B1, Saguenay, QC, Canada;Virginia Tech, 24061, Blacksburg, VA, USA;

关键词: Progressive landslide;    Sensitive clay;    Numerical modeling;    Strain-softening;    Constitutive soil model;    Large deformation;   

DOI  :  10.1186/s40677-023-00242-9

 received in 2022-12-25, accepted in 2023-05-08,  发布年份 2023

来源: Springer

【 摘 要 】

Landslides involving sensitive clays are recurrent events in the world's northern regions and are especially notorious in eastern Canada. The two critical factors that separate sensitive clay landslides from traditional slope stability analysis are the highly brittle behavior in undrained conditions (strain-softening) characteristic of progressive or retrogressive failures and the large deformations associated with them. Conventional limit equilibrium analysis has numerous shortcomings in incorporating these characteristics when assessing landslides in sensitive clays. This paper presents an extensive literature review of the failure mechanics characteristics of landslides in sensitive clays and the existing constitutive models and numerical tools to analyze such slopes' stability and post-failure behavior. The advantages and shortcomings of the different techniques to incorporate strain-softening and large deformation in the numerical modeling of sensitive clay landslides are assessed. The literature review depicts that elastoviscoplastic soil models with non-linear strain-softening laws and rate effects represent the material behavior of sensitive clays. Though several numerical models have been proposed to analyze post-failure runouts, the amount of work performed in line with sensitive clay landslides is very scarce. That creates an urgent need to apply and further develop advanced numerical tools for better understanding and predicting these catastrophic events.

【 授权许可】

CC BY   
© The Author(s) 2023

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