Displacement Back Analysis of Reservoir Landslide Based on Multi-Source Monitoring Data: A Case Study of the Cheyiping Landslide in the Lancang River Basin, China

Abstract

Landslides that occur in the littoral zone of a reservoir can directly damage the hydraulic structures and threaten the lives and property around the reservoir. Due to the spatial variability and heterogeneities of rock mass, a limited amount of data obtained from laboratory and in situ tests cannot comprehensively characterize the mechanical properties of rock and soil masses. Therefore, displacement back analysis is often performed to determine the mechanical parameters of rock and soil masses. The spaceborne Interferometric synthetic aperture radar (InSAR) has proved to be a powerful tool for geodesy in the measurement of landslide movement. However, InSAR can only measure the surface motion of the landslide without the subsurface information. This study uses multi-source monitoring data in the landslide displacement back analysis, including surface InSAR and an internal borehole inclinometer. The identified material parameters and finite element simulation are used to predict the landslide deformation. The case study of the Cheyiping landslide located in the Lancang River basin demonstrates the necessity and feasibility of using multi-source monitoring data in landslide displacement back analysis. The Cheyiping landslide is currently in the creep deformation stage. The decrease in shear strength of rock masses due to the rheological deformation and the change in reservoir water level are the internal and external factors leading to excessive landslide deformation. The numerical modeling can accurately simulate the landslide movement using the identified material parameters. By combing multi-source monitoring data and numerical modeling, the reservoir landslide deformation analysis can help evaluate the landslide deformation state and stability, which is vital for reservoir risk mitigation and the sustainable development of hydropower resources.

Development Pattern of Toppling Deformation Slopes in Western China

“…Hydropower toppling deformation slopes comprise the largest proportion and are distributed widely along the Yalong River basin [14,[39][40][41][42][43][44][45], the Lancang River basin [8,[46][47][48][49][50][51][52][53][54][55][56][57], the Yellow River basin [19,[58][59][60][61][62][63][64], the Dadu River basin [65][66][67][68], the Nu River basin [69], the Min River basin [70], the Bailong River basin [71,72], the Tao River basin [73,74], the Kezier River basin [75,76], the Xixi River basin and a river basin in Xinjiang [77].…”

The Use of Shape Accel Array for Deformation Monitoring and Parameter Inversion of a 300 m Ultrahigh Rockfill Dam

“…Xu and Wu combine an unsaturated seepage model, principal component analysis (PCA), and NSGA-II to obtain more accurate parameters [22]. In addition, many scholars have conducted inverse analysis on various model parameters of concrete dams, including the formulation of objective functions, optimization of surrogate models, selection of multiobjective optimization algorithms, and development of multiobjective decision criteria [23][24][25][26][27]. Tese innovative works have also inspired the progress of our research.…”

Assessing the Susceptibility of the Xiangka Debris Flow Using Analytic Hierarchy Process, Fuzzy Comprehensive Evaluation Method, and Cloud Model

“…With the deepening of the research, more and more mathematical methods such as fuzzy evaluation and regression analysis have been applied to the assessment of debris flow susceptibility and gradually moved toward the quantitative assessment of debris flow susceptibility. The aim is to identify the main influencing factors of induced debris flow and use various mathematical methods to determine the importance and weight of each influencing factor, to build a mathematical model to assess the susceptibility of debris flow [12][13][14][15][16]. Nowadays, there are more widely used and mature methods in China, such as the fuzzy mathematics evaluation method, regression analysis method, gray system evaluation method, etc.…”