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Application of modified discontinuous deformation analysis to dynamic modelling of the Baige landslide in the Jinsha River

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摘要 Accurate dynamic modeling of landslides could help understand the movement mechanisms and guide disaster mitigation and prevention.Discontinuous deformation analysis(DDA)is an effective approach for investigating landslides.However,DDA fails to accurately capture the degradation in shear strength of rock joints commonly observed in high-speed landslides.In this study,DDA is modified by incorporating simplified joint shear strength degradation.Based on the modified DDA,the kinematics of the Baige landslide that occurred along the Jinsha River in China on 10 October 2018 are reproduced.The violent starting velocity of the landslide is considered explicitly.Three cases with different violent starting velocities are investigated to show their effect on the landslide movement process.Subsequently,the landslide movement process and the final accumulation characteristics are analyzed from multiple perspectives.The results show that the violent starting velocity affects the landslide motion characteristics,which is found to be about 4 m/s in the Baige landslide.The movement process of the Baige landslide involves four stages:initiation,high-speed sliding,impact-climbing,low-speed motion and accumulation.The accumulation states of sliding masses in different zones are different,which essentially corresponds to reality.The research results suggest that the modified DDA is applicable to similar high-level rock landslides.
出处 《Journal of Mountain Science》 SCIE CSCD 2024年第7期2305-2319,共15页 山地科学学报(英文)
基金 supported by the National Natural Science Foundations of China(grant numbers U22A20601 and 52209142) the Opening fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection(Chengdu University of Technology)(grant number SKLGP2022K018) the Science&Technology Department of Sichuan Province(grant number 2023NSFSC0284) the Science and Technology Major Project of Tibetan Autonomous Region of China(grant number XZ202201ZD0003G)。
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