4.6 Article

Dynamic analysis of a long-runout, flow-like landslide at Areletuobie, Yili River valley, northwestern China

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出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s10064-018-1322-6

关键词

Flow-like landslide; Heavy rainfall; Yili River valley; Loess

资金

  1. National Natural Science Foundation of China [41502305]
  2. geological disaster detailed investigation project of China Geological Survey [DD20179609]

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In the analysis reported here, the catastrophic landslide at Areletuobie town in Xinyuan county is used as an example to investigate the formation mechanism and dynamics of a flow-like landslide in Yili River valley. At 0:30a.m. on July 31, 2012, a catastrophic landslide occurred in Xinyuan, Xinjiang, northwestern China, resulting in the deaths of 28 people when the temporary sheds in the downstream adit mining area were destroyed and buried directly in the path of the landslide. The runout distance of the landslide was about 1800m, and the accumulative volume was about 600,000m(3). The landslide can be categorized into three regions: the sliding source, impact-shoveling, and flow-accumulative regions. The stability of the landfill slope in the sliding source region was analyzed at two seepage stages of a long period of rain and a sudden heavy rainfall. To improve the accuracy of further hazard assessments of similar types of potential landslides in the same area, we simulated the kinematic and dynamic processes of the impact-shoveling region and the debris flow using the numerical model DAN-W and a combination of the frictional model and Voellmy model. The landslide moved for approximately 139s downward along the valley, with a maximum velocity of 30m s(-1). This is a typical long-runout flow-like landslide. Finally, we conclude that the identification of the potential geological hazards at Yili River valley area is crucial to prevent any catastrophic flow-like landslide triggered by heavy rainfall. The identified indicators should be properly used as a scientific basis for classifying hazard areas of flow-like landslides as well as for monitoring and early recognition.

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