Formation and propagation of dust cloud induced by Ultar rock avalanche on April 9, 2018, in Karimabad, Hunza, Pakistan

Gardezi, Hasnain; Xing, Aiguo; Bilal, Muhammad; Zhuang, Yu; Janjua, Shahmir

doi:10.1007/s10346-023-02042-6

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Cited by 3 publications

References 52 publications

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Contribution of freefall and rock mass structure to post-fragmentation spreading of rockslides

Zhu,

Wu,

et al. 2024

Bull Eng Geol Environ

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Contribution of freefall and rock mass structure to post-fragmentation spreading of rockslides

Zhu,

Wu,

et al. 2024

Bull Eng Geol Environ

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Mechanisms of rockslide-debris avalanches and the associated air blast—insights from the Su Village rockslide-debris avalanche in Zhejiang, China

Zhuang,

Xing,

Jiang

et al. 2023

Landslides

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Machine learning-based landslide velocity prediction model: incorporating multi- expression programming and discrete element modeling

Gardezi,

Li,

Huang

2024

Preprint

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The estimation of flow parameters for gravitational flows, such as velocity, volume, and runout distance is important for disaster prevention and mitigation. In this study, we have developed a prediction model for the frontal velocity of landslides using multi-expression programming (MEP), and discrete element modeling (DEM) as a function of slope angle, slope length, volume, coefficient of energy transfer, rolling friction and static friction. Moreover, we have also determined the percentage effect of each parameter on the front velocity. The range of the values for these parameters was selected from well-documented historical cases and experimental studies. The physical modeling results indicate that the front velocity was greatly influenced by the variation in slope angle and friction parameters. The developed prediction model was validated by comparing it with various statistical indices, and by performing sensitivity analysis, which validated the experimental observations that slope angle and friction parameters control the frontal velocity by 53% and 25% respectively. Moreover, a second-level validation was carried out by comparing the predicted front velocity with the front velocity of historical rock landslide cases and found to be in good agreement. It is hoped that the proposed model will help disaster mitigation and risk assessment by effectively predicting the front velocity of the imminent slides, and also reduce the computational cost, time, and resources.

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Formation and propagation of dust cloud induced by Ultar rock avalanche on April 9, 2018, in Karimabad, Hunza, Pakistan

Cited by 3 publications

References 52 publications

Contribution of freefall and rock mass structure to post-fragmentation spreading of rockslides

Contribution of freefall and rock mass structure to post-fragmentation spreading of rockslides

Mechanisms of rockslide-debris avalanches and the associated air blast—insights from the Su Village rockslide-debris avalanche in Zhejiang, China

Machine learning-based landslide velocity prediction model: incorporating multi- expression programming and discrete element modeling

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