Search

To study landslide monitoring and early warning methods in Hunan Province, and to improve the success rate of early warning,the Leijiashan landslide in Shimen County is used as a representative case. Through a detailed site investigation, this paper describes the characteristics of the landslide and provides a comprehensive analysis of its failure mechanism. The results reveal that the unique landscape and loose soil structure are internal factors contributing to the occurrence of the landslide, while heavy rainfall and the engineering excavation are external factors. By utilizing the GNSS system, complete displacement-time curves are obtained, and the types and features of these curves are analyzed to identify the evolutionary characteristics of the landslide, which include three stages: initial deformation, constant deformation, and accelerated deformation. Therefore, the landslide is classified as a gradual change type. By using the tangent angle and deformation rate thresholds as important indicators for landslide early warning, an early warning criterion is established. It is integrated with the community-based disaster prevention and early warning information platform, providing direct evidence for relevant departments to carry out disaster prevention, reduction, and emergency response. Successful mitigation of the Leijiashan landslide validates the practicality of this monitoring and early warning method. [ABSTRACT FROM AUTHOR]

Objective To reveal the deformation evolution of the Xinhua landslide in Luding County, Sichuan Province under the comprehensive effects of reservoir impoundment, reservoir water level fluctuations and seasonal rainfall, and to explore the progressive cumulative effect of the strength degradation of the landslide rock and soil mass under the coupling of multiple factors. Methods In this paper, based on a field investigation and multisource long sequence monitoring data analysis of the Xinhua landslide in the reservoir area of the hydropower project, combined with the three-stage evolution characteristics of the landslide cumulative deformation-time (S-t) curve and the measured S-t curve of the Xinhua landslide, the evolution stages of the three-step deformation cycles of the Xinhua landslide are identified and divided. At the same time, the improved tangent angle method is used for calculation and analysis, and a quantitative classification standard for Xinhua landslide classification early warning based on the improved displacement tangent angle early warning threshold is constructed. Results The results show that the short-term large increase of water level during the initial impoundment period, the rapid decrease of water level during the reservoir operation period and the influence of seasonal heavy rainfall are the main factors to accelerate the deformation of the Xinhua landslide. Conclusion According to the analysis and discussion, the improved displacement tangent angle early warning threshold can provide guidance for identifying the current stability state and potential risks of the Xinhua landslide to a certain extent, but multiple early warning indicators should still be considered, and the signs of macroscopic deformation and failure of landslides should be integrated for judgement.

Timely evaluation of landslide risk based on displacement data is crucial for the effective warning of landslide events. However, the complexity and diversity of displacement curves obtained in the field make the selection of evaluation criteria and thresholds challenging and subjective. Thus, this study proposes a new early warning criterion called the deformation probability index (DPI) to overcome this limitation. By analyzing features of landslide displacement curves obtained from the field, four linetypes were identified, and differences in velocity data between pre-failure and post-failure landslides were summarized from a probabilistic perspective. Subsequently, the DPI is defined to assess the early warning level of a landslide by converting observed velocity values into standardized velocity values using cumulative distribution function standardization. The resulting DPI values are divided into five early warning intervals with three alerting levels to indicate the relative stability of the landslide. Applying the proposed method to 16 landslides from around the world demonstrates the effectiveness of the DPI criterion in evaluating landslide risk, with a high correlation between alerting levels and observed landslide occurrences. The DPI criterion demonstrates notable sensitivity exclusively to displacement velocity and monitoring frequency, exhibiting remarkable generality and accuracy, thus offering a new method for landslide early warning. [ABSTRACT FROM AUTHOR]

Numerous engineering case studies suggest that the complexity and oscillatory of landslide displacement types, influenced by diverse deformation patterns, monitoring errors, and varied observation time frames, present challenges for the application of traditional warning criteria in assessing landslide movement in practice. As such, this study attempts to propose a data-dependent early warning criterion through the dimensionless analysis of displacement for two typical landslide displacement patterns: exponential pattern and step-like pattern. The displacement trend in exponential pattern landslides is extracted using the simple moving average (SMA) method, with the onset of acceleration (OOA) determined through the application of the Pettitt test. The Deformation Standardized Anomaly Index (DSAI) is defined to quantitatively evaluate the alert levels corresponding to each monitored displacement data of both patterns. After validating the proposed criterion in ten landslide cases, results indicate that the landslides have a high probability of losing stability when DSAI exceeds four, and the landslide tends to stabilize as DSAI decreases. Compared to displacement speed ratio (DSR), the proposed DSAI is more applicable, aligning with the actual conditions of both landslides with exponential pattern and step-like pattern and exhibiting a relatively low false alarm rate, making it a valuable criterion for landslide early warning. [ABSTRACT FROM AUTHOR]

Huanggang is one of the areas with frequent geological disasters in the flood season in Hubei Province. The main types of geological disaster are landslides, accounting for 75% of rainfall-induced landslides. Therefore, this paper statistically analyzes the correlation between rainfall and landslides in Huanggang City over the last 10 years, studies the rainfall threshold of rainfall-induced landslides in Huanggang City based on considering the prone zoning of geological disasters, establishes the effective rainfall model of landslide occurrence by using the logistic regression model and then puts forward the corresponding meteorological early warning criterion for different prone zone areas. Finally, the rationality and reliability of the early warning criterion are tested by historical rainfall and landslide events. The results show that the meteorological early warning criterion established significantly improves the accuracy of meteorological early warning for rainfall-induced landslides in Huanggang City, provides refined technical guidance for disaster transfer, and effectively reduces the loss of life and property caused by rainfall-induced landslides.

Copyright of Journal of Engineering Geology / Gongcheng Dizhi Xuebao is the property of Journal of Engineering Geology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)