Experimental study on tilting deformation and a new method for landslide prediction with retaining-wall locked segment

Liu, Handong; Liu, Jingjing; Chen, Jiaxing; Guo, Zhifei; Qiu, Lei

doi:10.1038/s41598-023-32477-9

Cited by 4 publications

(4 citation statements)

References 39 publications

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“…Deformation is the most important external manifestation of slope failure, and the type and characteristics of slope deformation over time are the most effective means for early landslide warnings [42,43,47,48]. For the slope without any mitigation measures (Figure 5), after 6 min of rainfall, the water began to flow on the slope surface, and then the surface began to be eroded.…”

Section: Deformationmentioning

confidence: 99%

Failure Process of High-Loess-Filled-Slopes (HLFSs) during Precipitation under Different Mitigation Measures

Zhu,

Zhuang,

Zhao

2024

Applied Sciences

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The problems of gully and soil erosion caused by large-scale urban construction and agricultural development in China have become more and more serious in recent years. In an effort to solve this problem, a series of gully stabilization and highland protection projects have been carried out on the Loess Plateau, and this has resulted in a large number of high-loess-filled-slopes (HLFSs). Although these filled slopes uses several different mitigation measures, the HLFSs have been eroded and destroyed under the action of water. In order to study the influence of different mitigation measures on the stability of HLFSs and their failure process, this paper uses a flume test of the effects of various mitigation measures on this failure process. The results show that: (1) the failure processes of slopes with different mitigation measures are obviously different. Slope deformation u with a declining gradient mitigation mainly occurs on the surface of the slope body, and although slope erosion is quite serious, the slope does not fail as a whole. Slopes with a stepwise drainage channel mitigation show little erosion, but material can easily slide along the horizontal drainage channels. (2) The slope deformation process is correlated with changes in pore-water pressure. When local instability occurs, there is always a pre-process of continuously rising pore-water pressure. When a failure occurs, the pore-water pressure of the soil at each position of the slope body suddenly fluctuates under instantaneous excitation. (3) The response of soil pore pressure and the development characteristics of tension cracks affect the deformation of the slopes, which is also the cause of the differences slope instability caused by different mitigation measures. These research results provide reference for the protection of HLFS engineering projects from heavy rains.

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Section: Deformationmentioning

confidence: 99%

Failure Process of High-Loess-Filled-Slopes (HLFSs) during Precipitation under Different Mitigation Measures

Zhu,

Zhuang,

Zhao

2024

Applied Sciences

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“…That is a milestone and successfully applied in multiple landslides, especially loess landslides (Xu et al, 2020). Liu et al (2023) suggested a tilting angle that is 0.34°, 1.89°, and 4.38°, respectively, when the modeled landslide enters the initial, medium, and fast creeping stages. However, fixed thresholds may not be applicable to all the landslides.…”

Section: Introductionmentioning

confidence: 99%

Landslide Deformation Prediction and Automatic Warning by Coupling Machine Learning and Physical Models

Wang,

Diao,

Guo

et al. 2024

Earth and Space Science

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Automatic deformation forecast and warning of a catastrophic landslide can effectively avoid significant casualties and economic losses. However, currently it has not come to a comprehensive forecast framework covering all the deformation stages of a landslide. Moreover, landslide deformation prediction possesses high error and false alarm rates. This work suggests a novel integrated framework of landslide deformation forecast and warning by coupling machine learning and physical models. The framework can relatively accurately predict all the deformation stages from creeping deformation to critical sliding and features 4 advantages. (a) The forecast indices are established by combining deformation and disaster‐triggering characteristics to improve the prediction accuracy. (b) It leverages the advantage of C5.0 decision tree algorithm in knowledge interpretability to automatically extract deformation forecast criteria. (c) It capitalizes on the precision superiority of a graph convolutional network in time‐series data learning to predict the four deformation stages from creeping deformation to rapidly accelerated deformation. (d) It utilizes the physical and mechanical bases of Morgenstern‐Price method to forecast the critical sliding stage. Zhujiatang Landslide is a large‐scale deep‐seated soil landslide with significant deformation. It is selected as a case study because it has endangered 1,131 persons and may cause a direct financial loss of 100 million RMB. The validating and predicting Accuracy values attain 97.39% and 95.72%, respectively, and the Kappa values reach 0.91 and 0.93, respectively. The landslide will run out when it suffers from a rainstorm with cumulative rainfall of 79.57 mm and an earthquake with a horizontal coefficient of 0.04.

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“…One of the most dominant approaches is to predict landslides by comprehending the physical mechanisms of landslide formation using physical equations and numerical simulation methods. Liu et al [2] utilize physical modeling and various instruments to study the evolution and instability of a locked segment landslide under rainfall conditions and identify tilting deformation as a standard for landslide instability. Capparelli et al [3] use a physical model, SUSHI, to simulate the role of subsurface hydrology in rain-induced landslides in Campania, Italy.…”

Section: Introductionmentioning

confidence: 99%

“…This paper has the following main contributions: (1) We propose a workflow for landslide susceptibility evaluation combining spatio-temporal knowledge graphs and machine learning model. (2) We propose a method for organizing remote sensing environmental data based on semantic structure, and improve the efficiency of remote sensing data usage by constructing schema. (3) We define inference rules for candidate model selection and environmental similarity analyses to reduce the impact of sample scarcity on landslide prediction results.…”

Section: Introductionmentioning

confidence: 99%

An Improved Multi-Source Data-Driven Landslide Prediction Method Based on Spatio-Temporal Knowledge Graph

Chen

Yang

et al. 2023

Remote Sensing

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Landslides pose a significant threat to human lives and property, making the development of accurate and reliable landslide prediction methods essential. With the rapid advancement of multi-source remote sensing techniques and machine learning, remote sensing data-driven landslide prediction methods have attracted increasing attention. However, the lack of an effective and efficient paradigm for organizing multi-source remote sensing data and a unified prediction workflow often results in the weak generalization ability of existing prediction models. In this paper, we propose an improved multi-source data-driven landslide prediction method based on a spatio-temporal knowledge graph and machine learning models. By combining a spatio-temporal knowledge graph and machine learning models, we establish a framework that can effectively organize multi-source remote sensing data and generate unified prediction workflows. Our approach considers the environmental similarity between different areas, enabling the selection of the most adaptive machine learning model for predicting landslides in areas with scarce samples. Experimental results show that our method outperforms machine learning methods, achieving an increase in F1 score by 29% and an improvement in processing efficiency by 93%. Furthermore, by comparing the susceptibility maps generated in real scenarios, we found that our workflow can alleviate the problem of poor prediction performance caused by limited data availability in county-level predictions. This method provides new insights into the development of data-driven landslide evaluation methods, particularly in addressing the challenges posed by limited data availability.

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Experimental study on tilting deformation and a new method for landslide prediction with retaining-wall locked segment

Cited by 4 publications

References 39 publications

Failure Process of High-Loess-Filled-Slopes (HLFSs) during Precipitation under Different Mitigation Measures

Failure Process of High-Loess-Filled-Slopes (HLFSs) during Precipitation under Different Mitigation Measures

Landslide Deformation Prediction and Automatic Warning by Coupling Machine Learning and Physical Models

An Improved Multi-Source Data-Driven Landslide Prediction Method Based on Spatio-Temporal Knowledge Graph

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