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A full-scale landslide-triggering experiment was conducted on a natural sandy slope subjected to an artificial rainfall event, which resulted in mobilisation of 130 m3 of soil mass. Novel slope deformation sensors (SDSs) were applied to monitor the subsurface pre-failure movements and the precursors of the artificially triggered landslide. These fully automated sensors are more flexible than the conventional inclinometers by several orders of magnitude and therefore are able to detect fine movements (< 1 mm) of the soil mass reliably. Data from high-frequency measurements of the external bending work, indicating the transmitted energy from the surrounding soil to these sensors, pore water pressure at various depths, horizontal soil pressure and advanced surface monitoring techniques, contributed to an integrated analysis of the processes that led to triggering of the landslide. Precursors of movements were detected before the failure using the horizontal earth pressure measurements, as well as surface and subsurface movement records. The measurements showed accelerating increases of the horizontal earth pressure in the compression zone of the unstable area and external bending work applied to the slope deformation sensors. These data are compared to the pore water pressure and volumetric water content changes leading to failure.

The accuracy of landslide displacement prediction can effectively prevent casualties and economic losses. To achieve accurate prediction of the Majiagou landslide displacement in the Three Gorges Reservoir (TGR), China, a hybrid machine learning prediction model considering the deformation hysteresis effect is proposed. The real-time deep displacement measurements were captured by using in-place inclinometers with Fiber Bragg grating (FBG) sensors. The time series method was adopted to divide the total displacement into a trend term and periodic term. Trend displacement was determined by the geological condition and predicted by the fitting method. Periodic displacement was controlled by external factors such as rainfall and fluctuation of reservoir water level. Before making the prediction, the grey correlation analysis was adopted to confirm that the fluctuation of the reservoir water level was the main influence factor. In view of the deficiency that current prediction methods could not quantitatively determine the lag time of landslide deformation and thus select the influencing factors empirically, the dynamic analysis of the correlation between periodic influence factors and periodic displacement was carried out in this paper, and the deformation lag time was identified to be 18 days by using set pair analysis (SPA) method. Finally, the optimal influence factors were selected and the prediction model of Majiagou landslide based on support vector machine optimized by particle swarm optimization (SPA-PSO-SVM) was established. Results showed that the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the proposed SPA-PSO-SVM prediction model are 0.28 and 12.8, respectively. Compared with the PSO-SVM model, the prediction accuracy of the proposed model had been improved significantly. The reliability and effectiveness of the SPA-PSO-SVM prediction model is verified and it has apparent advantages while predicting landslide displacement with deformation hysteresis effect involved.

This paper presents the analysis and results of a 14-year monitoring of slow-moving landslide behavior along a 100 m high slope at Serra do Mar , Brazil. The slope is located near a roadway and an industrial area and was suffering from creep movements triggered by an excavation at its foot. Movements were especially severe during the rainy periods due to water table fluctuation. Inclinometer readings from 2009 to 2011 showed that the sliding involved a soil mass of 15 to 20 m thick and was in the so-called tertiary phase, with relatively high acceleration. Prediction models showed that the slope failure would probably occur in another two to three years, which required immediate implementation of mitigation actions. By the end of 2011, several deep horizontal drains were installed along the slope to reduce the water table level. Since then, the inclinometers showed that acceleration was eliminated and velocity was substantially reduced, bringing the slope back to primary and secondary, stable movements. Monitoring results of deep horizontal drains shows that flow volumes increase substantially during the rainy seasons, showing that the solution efficiently stabilizes the slope. With monitoring results for both secondary and tertiary creep phases, and comparisons to other monitored slopes in the region, benchmark parameters related to slope velocity and acceleration for Serra do Mar slopes are discussed and presented. This constitutes the first organized study on slope movement velocities at Serra do Mar and presents an important contribution to researchers and designers.

Transportation corridors such as roadways are often subjected to both natural instability and cut-slope failures, with substantial physical damage to the road infrastructure and threats to the circulating vehicles and passengers. In the early 2000s, the Gipuzkoa Provincial Council of the Basque Country in Spain noted the need for assessing the risk related to the geotechnical hazards of its road network, in order to assess and monitor their safety for road users. The quantitative risk assessment (QRA) was selected as a tool for comparing the risk of different hazards on an objective basis. Few examples of multi-hazard risk assessment along transportation corridors exist. The methodology presented here consists of the calculation of risk, in terms of probability of failure and its respective consequences, and it was applied to 84 selected points of risk (PoR) over the entire road network managed by the Gipuzkoa Provincial Council. The types of encountered slope instabilities that are examined are rockfalls, retaining-wall failures, and slow-moving landslides. The proposed methodology includes the calculation of the probability of failure for each hazard based on an extensive collection of field data, and its association with the expected consequences. Instrumentation data from load cells and inclinometers were used for the anchored walls and the slow-moving landslides, respectively. The expected road damage was assessed for each hazard level in terms of a fixed unit cost (UC). The results indicate that the risk can be comparable for the different hazards. A total of 21 % of the PoR in the study area were found to be of very high risk.

Rainfall-induced landslides are a significant hazard in areas covered by granite residual soil in northern Guangdong Province. To study the response of granite residual soil landslides to rainfall, the most severely affected area during the floods in June 2022 and April 2024 was chosen as the study area. Geological investigations and field artificial rainfall tests were conducted to explore the deformation evolution characteristics of granite residual soil slopes under continuous heavy rainfall and to reveal the failure mechanism of rainfall-induced landsliding events. The results indicate that the granite residual soil can be divided into two layers, and the slope structure can be subdivided into three models from the geological point of view. Given that the deformation and failure characteristics of the surficial landslides are highly similar across the three models, the three models can be consolidated into a single model composed of granite residual soil and weathered granite. The intensity and persistence of rainfall are the main triggering factors of landslides in this area. The landslides are primarily characterized by surficial sliding with a traction sliding failure mode, mainly involving a granite residual soil layer thickness of about 1–3 m. The increased rate of water content and the range of pore water pressure can be used as primary indicators for slope deformation and failure. Additionally, shear dilatancy deformation during slope movement effectively mitigates deformation rates. Furthermore, debris flow is identified as a secondary disaster resulting from landslides, with landslide deposits serving as potential sources for debris flow.

The stability of the left-bank slope is a crucial geological engineering problem at the Baihetan hydropower station, China. Due to continuous excavations on the rock slope, different regions of the surrounding rock mass undergo varying degrees of unloading deformation. It is important to assess the stability of the rock slope from a macroscopic viewpoint by investigating its deformation characteristics and mechanisms. Therefore, in this work, microseismic (MS) monitoring was first employed to detect the progressive rock mass damage in the rock slope subjected to excavation, including the initiation, propagation, coalescence, and interaction of rock microfractures. Numerical modeling was subsequently performed to understand the deformation and failure mechanism of the rock slope. Moreover, traditional surveying approaches (i.e., multiple-point extensometers and inclinometers) and field observations were also used to analyze the deformation and failure characteristics of the rock slope. The MS monitoring results showed that spatiotemporal regularities in the evolution of seismic source locations were indicators of deformation failure and potential sliding surfaces. MS event clustering can be used to delineate activated pre-existing geological structures (i.e., LS331 and LS337). The simulation results show that the deformation and failure characteristics of the rock slope are mainly controlled by pre-existing weak structural planes (i.e., the intraformational faulted zones LS3319, LS331, and LS337 and fault F17). These results agree well with the results of geological data and conventional monitoring data. Our study reveals that an integrated approach combining MS monitoring, numerical modeling, traditional surveying, and field observations leads to a better understanding of the behavior of the rock slope under the influence of excavation as well as greater control of the working faces, ensuring safety under complex geological and excavation conditions.

Landslide deformation monitoring is crucial for early warning and disaster prevention. This study presents a comprehensive landslide monitoring approach using global navigation satellite systems (GNSS) and automated inclinometers through a case study in Qinghai, China. Numerical modeling utilizing smoothed particle hydrodynamics (SPH) predicted potential hazards from dynamic landslide movements. The results demonstrate that the combination of GNSS and automated inclinometer systems provides accurate and real-time data on landslide movements, enabling timely responses to mitigate the risks associated with landslides. Monitoring results demonstrate active deformation of Zone III with sliding velocities up to 2.5 mm/day and deep subsurface displacement along bedrock shear planes at 14–26 m depths. SPH simulations predict a maximum run out of 115 m over 50 s for potential slope failure under a Ms 7.0 earthquake, with debris flows capable of blocking the river channel and inducing secondary hazards. Additionally, the numerical modeling using SPH provides useful insights into the potential hazard of landslides, which can be used to develop more effective mitigation strategies in the future. The findings of this study contribute to the development of a more holistic and effective approach to landslide monitoring and management.

In-place automatic inclinometers are typical devices used to monitor displacements of extremely slow to slow-moving landslides. The significance of these measurements requires methodologies able to distinguish real measures from anomalous ones, to quantify significant moments of acceleration in deformation trends and to determine the main factors that influence the kinematic behavior measured by an automatic inclinometer. This work aimed at developing a novel method, which allows to cover all the steps of analysis of data acquired by automatic inclinometers. The methodology is composed by five steps: (I) evaluation of the reliability of the instruments; (II) identification and elimination of anomalous measures from displacement time-series; (III) recognition of significant moments of acceleration in the rate of displacement, through thresholds based on the mean rate of displacement and on the cumulated amount of the deformation; (IV) clustering of the events of significant acceleration, to characterize different typologies of events according to different landslides kinematic behaviors; (V) identification of the main meteorological and groundwater parameters influencing the deformation pattern measured by an automatic inclinometer. The methodology was developed and tested using displacement time-series of 89 automatic inclinometers, belonging to the regional monitoring network of Piemonte region (northern Italy), managed by Arpa Piemonte. Two representative inclinometric time-series were selected to validate all the steps of the methodology for different types of monitored slow-moving landslides. The developed method is reliable in the estimation of anomalous measures and in the identification of significant accelerations, helping in the comprehension of the response of displacement trends during activity phases. Moreover, it is able to identify the factors which influence more the deformation pattern measured in correspondence of an automatic inclinometer.

Deep gravitational slope deformations (DsGSDs) are a geological and engineering challenge with important implications for slope stability, the reliability of existing infrastructures, land use and, above all, the safety of settlements. This paper focuses on the DsGSD phenomenon that affects a large part of the Borrano hamlet, located in the municipality of Civitella del Tronto (Abruzzo Region, Central Italy). This instability is characterized by slow movements of large volumes of material. The main factors initiating deformations are a combination of geological and hydrogeological aspects. These factors include the complex local stratigraphy, composed of pelitic and arenaceous facies at high slope dip angles, and extreme natural events such as heavy rainfall and earthquakes. This study employs a multidisciplinary approach integrating in field activities such as remote-controlled surface monitoring (clinometers and strain gauges), in-depth monitoring (inclinometers and piezometers), aero-photogrammetric analysis and numerical modelling. These techniques permitted us to characterize the evolution of the slope and to identify both the critical sliding surfaces and the mechanisms governing the ground movements. Soil deformations were mainly observed in the central zone of the hamlet. Significant deformations were recorded along planes of weakness at depth between arenaceous and pelitic materials. These planes represent contact zones between the clayey–marly facies, characterized by low strength, and the arenaceous facies, characterized by higher stiffness, creating a mechanical contrast that favours the development of large deformations. The numerical analyses confirmed good correlation with the monitoring data, revealing in detail the instability of both local and territorial processes. The 3D numerical analysis showed how the movements are controlled by planes of weakness, highlighting the key rule of geological discontinuities.

Surface speeds in Greenland's ablation zone undergo substantial variability on an annual basis which are presumed to mainly be driven by changes in sliding. Yet, meltwater-forced changes in ice–bed coupling can also produce variable deformation motion, which impacts the magnitude of sliding changes inferred from surface measurements and provides important context to flow dynamics. We examine spatiotemporal changes in deformation, sliding and surface velocities over a 2-year period using GPS and a dense network of inclinometers installed in borehole grid drilled in western Greenland's ablation zone. We find time variations in deformation motion track sliding changes through the summer and entire measurement period. A distinct spatial deformation and sliding pattern is also observed within the borehole grid which remains similar during winter and summer flow. We suggest that positively covarying sliding and deformation across seasonal timescales is characteristic of passive areas that are coupled to regions undergoing transient forcing, and the spatial patterns are consistent with variations in the local bed topography. The covarying deformation and sliding result in a 1.5–17% overestimate of sliding changes during summer compared to that inferred from surface velocity changes alone. This suggests that summer sliding increases are likely overestimated in many locations across Greenland.