Mechanism analysis and partition characteristics of a recent highway landslide in Southwest China based on a 3D multi-point deformation monitoring system

Li, Changdong; Long, Jingjing; Liu, Yong; Li, Qun; Liu, Wenqiang; Feng, Pengfei; Li, Bingchen; Xian, Jin-ye

doi:10.1007/s10346-021-01698-2

Cited by 26 publications

(5 citation statements)

References 36 publications

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“…Deformation monitoring is an important means to ensure the construction safety 5 , 37 . The monitoring data are also an index to evaluate the effectiveness of reinforcement measures.…”

Section: Methodsmentioning

confidence: 99%

Evolution process and failure mechanism of a large expressway roadside landslide

Zhang

Ding

et al. 2023

Sci Rep

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Site investigation, deformation monitoring, laboratory test, and theoretical calculations were used to analyze the evolution details of a large expressway roadside landslide during the start-up sliding process. The monitoring results show that the initial deformation and failure occurred on the protective wall at the slope toe, then gradually developed to the upper part of the slope, and finally led to tensile cracks at the slope trailing edge. Accelerated deformation of the slope support structures, such as the protective wall at the slope toe, the anti-slide pile, and the anchor cable, were observed during the continuous extreme rainfall. The infiltrated rainwater can change the weight, the osmotic pressure, the anti-sliding force, the sliding force of the sliding mass, and further soften the fully weathered tuff soil and reduce its strength, resulting in the landslide occurrence. Block the slope surface runoff is an effective measure to reduce the landslide risk. The current analysis will be helpful to the prevention, control, and emergency disposal of similar landslides.

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“…Deformation monitoring is an important means to ensure the construction safety 5 , 37 . The monitoring data are also an index to evaluate the effectiveness of reinforcement measures.…”

Section: Methodsmentioning

confidence: 99%

Evolution process and failure mechanism of a large expressway roadside landslide

Zhang

Ding

et al. 2023

Sci Rep

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“…A single technique alone only reflects distinct deformation features in different parts of the landslide [54]. A multi-source monitoring dataset can reduce uncertainty and supplement missing information from a single source [73]. For example, the combination of InSAR measurements and a borehole inclinometer enables the simultaneous acquisition of surface and internal landslide displacement data.…”

Section: Further Application Of Multi-source Monitoring Data In Defor...mentioning

confidence: 99%

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

Guo

Xiao³

et al. 2022

Remote Sensing

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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.

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“…Slope surface displacement is one of the most intuitive demonstrations of the development and stability of the landslide (Cheng et al, 2021; Ge, Liu, et al, 2021; Wei et al, 2020). Dependable prediction of displacement is essential for early warning of landslides and contributes to protecting the population and reducing economic losses (Li, Long, et al, 2021; Zhang, Zhang, Liao, et al, 2022). However, it is still challenging to build a precise forecast model for landslide displacement, considering that a landslide is a complex natural phenomenon and varies with geological conditions and mechanisms (Ali et al, 2021; Jiang et al, 2022; Liu et al, 2022; Wu et al, 2022; Youssef & Pourghasemi, 2021; Zhang, Yuan, Lu, & Zhang, 2022), especially for the landslides with step‐like deformation features in the Three Gorges Reservoir (TGR) region (Tang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A data‐driven intelligent model for landslide displacement prediction

Sun

Liu

et al. 2023

Geological Journal

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Landslides with step‐like deformation features are widely distributed in the Three Gorges Reservoir area (TGR) of China, posing a severe hazard to the inhabitants of this region. This paper proposes a multi‐input and multi‐output intelligent integrated displacement prediction model for landslides with step‐like displacement patterns. In this new model, three interconnected and information‐transmitted functional sub‐models are integrated. Unsupervised learning is used to identify different landslide deformation states automatically, and the imbalance classification and explainable artificial intelligence techniques are introduced for qualitative prediction and information filtering. Probability theory and deep machine learning are adopted to provide deterministically predicted values and quantify their uncertainty. The case study of the Baijiabao landslide in the TGR region proves that the proposed model performs satisfactorily in both point and interval predictions. The intelligent integrated model can also provide the forecast of landslide deformation states, visual input information filtering and back analysis of influencing factors, which are valuable to landslide early warning and risk management.

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Mechanism analysis and partition characteristics of a recent highway landslide in Southwest China based on a 3D multi-point deformation monitoring system

Cited by 26 publications

References 36 publications

Evolution process and failure mechanism of a large expressway roadside landslide

Evolution process and failure mechanism of a large expressway roadside landslide

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

A data‐driven intelligent model for landslide displacement prediction

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