Failure characteristics and instability mechanism of the Hengshanbei catastrophic high-locality landslide in Guangdong, China

Triggered by continuous heavy rainfall, a catastrophic large-scale high-locality landslide occurred in Hengshanbei mountain slope of Shangxi Village, Longchuan County, Guangdong Province, China, on June 14, 2022, at 12:10 (UTC + 8). The landslide had an estimated volume of about 1.45 × 105 m3 and resulted in severe damage to the region. To investigate the causative mechanisms of this landslide, a comprehensive study was conducted, involving geological and hydrological surveys of the research area, combined with field investigations, satellite imagery, drone photography, data analysis of rainfall and landslide displacement monitoring, and laboratory experiments. The research focused on analyzing the process of landslide formation and development, trigger factors, destruction characteristics, and instability mechanisms. Additionally, the study employed the Mohr-Coulomb strength theory to explain stress variations during the landslide process. Findings indicated that: (1) the slope soil structure was loose with well-developed pores, mainly composed of kaolinite with strong water absorption properties, causing softening and disintegration of the soil when encountering water, resulting in reduced cohesion and internal friction angle, and overall poor soil properties; (2) continuous heavy rainfall infiltrated the slope through soil pores and eroded channels, increasing pore water pressure and reducing effective stress, subsequently reducing anti-sliding force and increasing sliding force; as well as (3) unfavorable terrain conditions, such as high landslide starting point and high-locality, significant height, and steep slope, lead to landslides running farther and being of larger scale. The study further highlighted that the intrinsic properties of the slope soil were the decisive internal cause of the landslide, while continuous heavy rainfall and adverse terrain were external triggering factors. These findings provide essential insights for understanding and preventing similar landslide disasters.