Deformation Characteristics of Tianjiaba Landslide Induced by Surcharge

Abstract

The Tianjiaba landslide on the Hang Rui expressway was located in the south of Sinan County, Tongren City, Guizhou Province. From the night of 2 July to the morning of 3 July 2011, numerous house cracks, foundation subsidence, and surface cracks suddenly occurred in the Tudiwan and Tianjiaba villages on the north and south sides of the management area. Based on the field geological survey, drilling, pit exploration, surface displacement, fracture observation, and deep displacement monitoring data analysis the deformation characteristics and causes of the Tianjiaba landslide were comprehensively analyzed. In this study, the landslide was divided into three areas, in which fracture group 3 was the most severe. Among the ten inclinometer holes, the sliding surface displacement of No.8 inclinometer hole was the largest, i.e., 25 mm. The sliding surface slopes of fracture groups 1, 2, and 3 were small, i.e., 12.25°, 4.97° and 6.62° to 4.64°, respectively. The results showed that the displacement values of different positions of the landslide were different, and the ground displacement value was larger than the sliding surface displacement value. The maximum displacement of the ground was 242.68 mm, which is larger than the maximum displacement of the inclinometer hole. Because the displacement of the upper part was greater than that of the lower part, bulging cracks formed in the lower part of the landslide. When the displacement of the upper part was less than that of the lower part, tension cracks formed in the lower part of the landslide. According to the surface cracks and displacement values of inclinometer holes on the sliding surface, the landslide was in the initial sliding stage and filling the platform of the management area on the hillside was the main cause of the landslide.

The Impact of Probability Density Functions Assessment on Model Performance for Slope Stability Analysis

“…Therefore, the link between the reliability of the results and the pdfs definition for the input random variables represents a crucial aspect in stochastic PB models, even if the attention of the scientific community has been mainly paid to the comparison between the results of the statistical and deterministic analysis [11,12,55,56].…”

“…Considering the negative impact of landslides on society [11,12], different approaches have been developed to protect and safeguard the territory; for example, geomorphological mapping, analysis of landslide inventories, heuristic terrain, and statistically based classification methods [13,14] have often been used to evaluate the landslide susceptibility of an area.…”

Ubicación y Profundidad de la Grieta de Tracción en Taludes con el Empleo de Modelos Numéricos

“…En la práctica de la ingeniería, se ha demostrado la presencia de grietas en la parte superior del talud o terraplén antes de producirse la falla, las cuales facilitan la penetración de agua de lluvia, aumentando la tensión lateral y las fuerzas desestabilizadoras. Antes del deslizamiento se producen repentinamente numerosas grietas en viviendas, hundimiento de los cimientos y grietas en la superficie, las cuales se desarrollaron rápidamente (Zhang et al, 2021). A pesar de la influencia potencialmente significativa de las grietas de tracción, casi todos los métodos existentes no las toman en cuenta o las tratan de una manera simplista; la formación de grietas de tracción aumentará significativamente el empuje de la masa de tierra (Abdollahi et al, 2021) y por tanto la inestabilidad del talud.…”

Early Warning Indicators of Landslides Based on Deep Displacements: Applications on Jinping Landslide and Wendong Landslide, China

“…In contrast, the deep displacement method (i.e., monitoring real-time cumulative displacement along depth) has been widely used, for example, to track deformation features of a sliding body (Yin et al, 2010;Bennett et al, 2015), improve understanding of the deformation mechanisms (Simeoni and Mogiovi, 2007), and reveal slip surfaces (Carla et al, 2017) over the last decade. Relevant studies have helped to assess stability of slopes (Tao and Zhou, 2014;Zhang et al, 2021) and provided guidelines for design and construction of supporting devices (Zhang Y. Q. et al, 2018). Thanks to the state-of-the-art techniques on microelectronics, high-performance computers and communication (Bennett et al, 2017;Ha et al, 2018), realtime monitoring of deep displacement has become rather applicable for landslide evaluations and mitigations.…”