Step-like Displacement Research Articles

Analyzing failure mechanisms and predicting step-like displacement: Rainfall and RWL dynamics in lock-unlock landslides

Lock-unlock landslides have thick sliding zones that store a lot of energy. This makes them start quickly, happen suddenly, and have serious consequences. Therefore, it becomes urgent to study the deformation and failure mechanisms of such landslides and develop rational predictive models. Taking the Jiuxianping landslide as an example, this study investigates the regularity of landslide displacement changes using multi-source data, focusing on the abrupt displacement patterns in the unlock phase. Furthermore, employing Transient Release and Inhalation Method tests combined with Geo-Studio’s SEEP/W and SIGMA/W modules for fluid–solid coupled simulation calculations, the evolution process of landslide failure mechanisms and deformation characteristics is analyzed and discussed. Lastly, utilizing data mining analysis of multi-source data, a hybrid optimized machine learning predictive model is established for model prediction comparison. The study reveals that: (1) The rise in infiltration line elevates pore water pressure, affecting the stability of the sliding zone, leading to “unlock effects” and step-like displacement deformation; (2) Simulation shows that YY208 is closer to the actual situation, located at the far bank position, while YY210 is greatly influenced by the “buoyancy effect”, resulting in a slowdown in deformation velocity; (3) After data preprocessing, overall actual displacement prediction performs better than simulation displacement prediction in terms of Mean Absolute Error, Mean Squared Error and Correlation Coefficient, but noise reduction processing can improve the periodic prediction effect of simulation displacement.

Deformation mechanism-assisted deep learning architecture for predicting step-like displacement of reservoir landslide

Reservoir landslides in the Three Gorges Reservoir, China, exhibit prolonged slow motion and the potential for catastrophic events due to fluctuations in reservoir levels and intense rainfall episodes. Their distinct step-like deformation characteristics, involving rapid transformation processes of different states, pose challenges for accurate early warning and prediction. Previous forecasting models have often struggled with limited accuracy. This study introduces a mechanism-assisted deep learning model, leveraging the Informer architecture, to predict prolonged step-like reservoir landslide displacement. Utilizing a 15-year continuous monitoring dataset of the Baishuihe landslide, this model investigates the landslide mechanism, identifies influencing conditions underlying the step-wise behavior, and customizes input features for the prediction model by integrating optimized variational mode decomposition and wavelet analysis. Additionally, the dynamic correlation and hysteresis analysis between triggering factors and displacement offer valuable physical insights into the model and enhance the interpretability of the model. The model is further tailored to accommodate features of the monitoring dataset associated with landslide evolution by integrating a global multi-head attention mechanism and pooling layers, enabling the capture of both globe dependencies and local critical features of the model inputs. Through rigorous model training, performance evaluation, and tuning, the proposed model efficiently predicts step-wise landslide displacement, particularly during short-term rapid transitions between creep-mutation states.

Step-like displacement prediction of reservoir landslides based on a metaheuristic-optimized KELM: a comparative study

Step-like displacement prediction of reservoir landslides based on a metaheuristic-optimized KELM: a comparative study

Step-like displacement prediction and failure mechanism analysis of slow-moving reservoir landslide

Landslides triggered by extreme rainfall due to global climate change are becoming more frequent. The Earth surface processes activity and landform evolution caused by landslide movement seriously affect human survival and the environment. In recent years, the reservoir slope located in the Three Gorges reservoir (TGR) area has been subject to the combined effects of extreme rainfall and reservoir regulation, which have been highly susceptible to inducing landslides. Reservoir landslides usually show step-like slow movement characteristics. The causes of slow-moving landslides are complex, the mechanisms behind them are difficult to grasp, and it may evolve into violent landslide disasters. Therefore, accurately predicting step-like displacement in slow-moving landslides is an effective solution for risk reduction. To achieve the accurate prediction of the step-like displacement of slow-moving landslides and the analysis of the failure mechanism, this study establishes a prediction model applicable to step-like displacement prediction of slow-moving reservoir landslides is developed based on 16 years of continuously monitored cumulative displacement of the Jiuxianping landslide, combined with the Empirical Mode Decomposition (EMD) method and the Improved Particle Swarm Optimization (IPSO) optimized the Long short-term memory (LSTM) neural network. This approach can effectively improve step-like displacement prediction accuracy. In particular, this approach can better represent the association between the step displacement of slow-moving landslides with environmental characteristics (e.g., rainfall, reservoir level). The study reveals that there are differences in the mechanisms and influencing conditions that cause the deformation of different areas of the landslide. Additionally, the reservoir water level fluctuation and seasonal rainfall are identified as the primary reasons contributing to the stepped displacement of the Jiuxianping landslide. The method provides a basis for the step-like displacement prediction, instability mechanism and landform evolution study of slow-moving reservoir landslides and provides valuable guidance for the prevention and control of similar reservoir geomorphic hazards worldwide.

Prediction of landslide step-like displacement using factor preprocessing-based hybrid optimized SVR model in the Three Gorges Reservoir, China

Artificial intelligence (AI) is gaining popularity in landslide displacement modeling due to its strong generalization capacity and ability to accurately represent complex, nonlinear laws. However, few studies have examined the validity of factor selection and the impact of data noise on the model. To address these issues, we employ various analyses, including Pearson correlation coefficient and grey correlation degree analyses to diagnose redundant and highly correlated factors, respectively. Singular spectrum analysis (SSA) is employed for denoising the data, leveraging the benefits of the hankel matrix and minimal parameters. This technique facilitates the decomposition of displacement into trend and periodic terms. The trend term displacement is fitted with a polynomial, and the periodic term displacement is predicted using a support vector regression (SVR) model. The hybrid-optimized SVR by particle swarm optimization-gravitational search algorithm (PSO-GSA) is used to improve the model's performance and prediction ability. We apply the proposed method to the Bazimen and Baishuihe landslides in Zigui County for model validation. The results show that denoising the input factor data reduces the root mean square error (RMSE) and mean absolute error (MAE) and increases the correlation coefficient (R). Furthermore, the PSO-GSA algorithm exhibits excellent generalization ability and fast optimization speed in optimizing parameters. The correlation coefficient increase to 0.99 when using the hybrid-optimized SVR model for displacement prediction.

Response of Step-Like Landslide to Pore-Water Pressure under the Action of Typhoon and Rainstorm

Step-like landslides have been attracting more and more attentions because of their displacement with step-like characteristics. At present, there are few studies about the mechanism of a step-like landslide from the perspective of its response to pore-water pressure. This paper considers the Yaoshan landslide disaster mainly affected by rainfall and groundwater in Anxi County, Quanzhou City, Fujian Province, as an example. Based on an in situ monitoring test, we explored the infiltration pathways of typhoons and rainstorms, the main influencing factors, and characteristics of pore-water pressure and determined the relationship between landslide displacement and pore-water pressure in the study area. The results show the following: (1) the pore-water pressure within the slope varies with fluctuation in groundwater level which is influenced by vertical infiltration of rainwater through the slope surface and dominant channels in the study area. (2) The landslide exactly produces a step-like displacement when the pore-water pressure near the slip surface increases from the minimum point to the maximum and then dissipates to the vicinity of the starting point. (3) The correlation between pore-water pressure change and landslide displacement is revealed, and the empirical formula that can characterize slope displacement generated by increment in per unit of pore-water pressure in unit time has been proposed. This study is crucial as it offers important theoretical and practical significance for the analysis of disaster-causing mechanism and monitoring and early warning of the same type of landslide.

Adaptive hybrid machine learning model for forecasting the step-like displacement of reservoir colluvial landslides: a case study in the three Gorges reservoir area, China

Adaptive hybrid machine learning model for forecasting the step-like displacement of reservoir colluvial landslides: a case study in the three Gorges reservoir area, China

Possible mechanism of the formation of the Jichechang ground fissure in Datong, China, based on in-situ observations

The Datong region of China suffers from severe ground fissure (GF) disasters. The Jichechang ground fissure (JGF) is typical among the GFs in Datong and is the most active. To provide scientific guidance for disaster mitigation, understanding the mechanisms governing GF activity in Datong needs to be improved. Here, long-term monitoring data (> 10 years) for the JGF are used to study the characteristics of its activity. The results show that the formation of GFs is mainly controlled by concealed faults. The JGF is mainly active in the vertical direction, with a differential vertical displacement 2.5 times greater than the horizontal displacement. The GF activity is periodic, with a periodicity of 320–420 days, which corresponds to the cycle of local agricultural irrigation. The JGF is especially active in June and July. The vertical activity of this fissure also displays a distinct quasi-periodic step-like displacement acceleration with a duration of 18–38 days. Numerical simulations show that irrigation pumping within 10 km of the JGF has a significant effect on the vertical movement of GFs. These results provide a better understanding of the mechanisms governing GF activity in this area and provide a valuable reference for the study of GFs in other regions.

A novel approach for displacement interval forecasting of landslides with step-like displacement pattern

ABSTRACT Quantifying the uncertainties in the prediction of landslide displacement is important for making reliable predictions and for managing landslide risk. This study develops a novel approach for the interval prediction (i.e. uncertainty) of landslide with step-like displacement pattern in the Three Gorges Reservoir (TGR) area using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Synthetic Minority Oversampling Technique and Edited Nearest Neighbor (SMOTEENN) based Random Forest (RF) and bootstrap-Multilayer Perceptron (MLPs). DBSCAN was employed to carry out clustering analysis for different deformation states of the landslide with step-like displacement pattern. The SMOTEENN based RF classifier was trained to deal with imbalanced classification problems. A dynamic switching prediction scheme to construct high-quality Prediction Intervals (PIs) using bootstrap-MLPs was established. The concepts of Pareto front and Knee point were adopted to select the PIs that could provide the best compromise between reliability and accuracy. The proposed DBSCAN-RF-bootstrap-MLP method is illustrated and verified with one typical landslide with step-like displacement pattern, the Bazimen landslide from the TGR area in China. The method showed to perform well and provides the uncertainties associated with landslide displacement prediction for decision making.

A Predictive, Two-Parameter Model for the Movement of Reservoir Landslides

Monitoring data show that many landslides in the Three Gorges region, China, undergo step-like displacements in response to the managed, quasi-sinusoidal annual variations in reservoir level. This behavior is consistent with motion initiating when the reservoir water level falls below a critical level that is intrinsic to each landslide, with the subsequent displacement rate of the landslide being proportional to the water depth below that critical level. Most motion terminates when the water level rises back above the critical level, so the annual step size is the time integral of the instantaneous displacement rate. These responses are incorporated into a differential equation that is easily calibrated with monitoring data, allowing prediction of landslide movement from actual or anticipated reservoir level changes. Model successes include (1) initiation and termination of the annual sliding steps at the critical reservoir level, producing a series of steps; (2) prediction of variable step size, year to year; and (3) approximate prediction of the shape and size of each annual step. Annual rainfall correlates poorly with step size, probably because its effect on groundwater levels is dwarfed by the 30 m annual variations in the level of the Three Gorges Reservoir. Viscous landslide behavior is suggested.

Deforming tendency prediction study on typical accumulation landslide with step-like displacements in the Three Gorges Reservoir, China

Lots of accumulation landslides with step-like displacements occurred in the Three Gorges Reservoir due to periodic reservoir operation. This type of landslide has continued to deform for a long time, with large cumulative deformation, which may influence the land use in the reservoir and landslide control risk judgment. The accumulation landslides with four typical step-like displacement-time curves during more than 10 years, namely Baijiabao landslide, Laoshewo landslide, Heishiban landslide, and Shuping landslide, are demonstrated. The cumulative displacement-time curve of Laoshewo landslide obviously shows that its deforming tendency is slowing down, while that of Heishiban landslide clearly indicates the increasingly serious deformation. When the step-like displacement increments get close to each other, it is harder to predict the landslide tendency based on displacement, velocity, inverse velocity, or acceleration in a whole continuous period. A step-like displacement-time curve can be considered as a curve of a piecewise function, varying as time goes. Therefore, the piecewise function is proposed to analyze the tendency by different time periods. As the environment of rapid deforming period is similar, the deforming tendency of a landslide with step-like displacements can be better demonstrated by collecting data in the rapid deforming period to analyze its tendency. For Shuping landslide, the fluctuating decline in velocity and acceleration in the rapid deforming period shows that the landslide tends to become more and more stable. This well matches the status quo of Shuping landslide, as it has been treated during August 2014 to March 2015, which also verifies the effectiveness of this proposed method. For Baijiabao landslide, the inverse velocity-time curve in the rapid deforming period features fluctuating decline, indicating that the landslide tends to be increasingly unstable. Meanwhile, the discussion on failure prediction found that the watch window for critical prediction of Baijiabao landslide may open in the flood season of several years’ future. The method proposed is effective for predicting the deforming tendency of landslides with step-like displacements, provides a time window for critical prediction, and offers technical support for landslide risk control, alarm, and forecasting.

Damage evolution of tunnel lining with steel reinforced rubber joints under normal faulting: An experimental and numerical investigation

The zones where tunnels through the active faults are proved to be seriously damaged because of the fault dislocation which could be due to strong earthquakes. A steel reinforced rubber joint with flexibility and ductility is proposed in this paper. An experimental investigation of the responses and the damage patterns of the tunnel lining with the joints under normal faulting is presented. The results showed the damage of the lining with the joints in the model test was mainly concentrated in the fault areas because of the joints’ adjustment. No obvious shear failure was found in this test. Then, a nonlinear three-dimensional finite element (FE) model, using concrete damaged plasticity model, was performed to simulate the tunnel linings with the proposed joints and without joints under normal faulting. The numerical results show that the tunnel with the joints had step-like displacements without big relative displacement compared to the tunnel without joints. The joints efficiently decreased the maximum relative displacement between lining segments. The extent of the tensile damage area of the lining affected by normal fault was longer than that of the compressive damage area. And the tensile damage area was larger than the compressive damage area for either the lining with the joints or the lining without joints. There was no obvious difference in damage to the lining with and without joints in areas under compression loading. The joints efficiently decreased the extent of the damage in the zone with tensile stresses. The comparisons between the numerical predictions and experimental results show a good agreement.

Using a kernel extreme learning machine with grey wolf optimization to predict the displacement of step-like landslide

Landslide displacement prediction is an important aspect of landslide hazard research. In this paper we assess the characteristics of landslide deformation in the Three Gorges Reservoir Area of China and propose and apply a step-like displacement prediction model based on a kernel extreme learning machine with grey wolf optimization (GWO-KELM) to the Baishuihe landslide. In this model, the cumulative displacement is first decomposed into trend displacement and periodic displacement by time series. The trend displacement is then predicted by a cubic polynomial model, and the periodic displacement is predicted by the proposed model after the displacement data have been statistically analyzed. A hybrid model is then established for the prediction of landslide displacement. We then compare the performance of this hybrid model with that of the extreme learning machine with GWO (GWO-ELM), support vector machine with GWO (GWO-SVM) and extreme learning machine (ELM) models. The results show that the proposed hybrid model outperforms the other models and that the GWO-KELM model achieves excellent performance in predicting landslide displacement with a step-like behavior.

Dissecting fat-tailed fluctuations in the cytoskeleton with active micropost arrays

The ability of animal cells to crawl, change their shape, and respond to applied force is due to their cytoskeleton: A dynamic, cross-linked network of actin protein filaments and myosin motors. How these building blocks assemble to give rise to cells' mechanics and behavior remains poorly understood. Using active micropost array detectors containing magnetic actuators, we have characterized the mechanics and fluctuations of cells' actomyosin cortex and stress fiber network in detail. Here, we find that both structures display remarkably consistent power law viscoelastic behavior along with highly intermittent fluctuations with fat-tailed distributions of amplitudes. Notably, this motion in the cortex is dominated by occasional large, step-like displacement events, with a spatial extent of several micrometers. Overall, our findings for the cortex appear contrary to the predictions of a recent active gel model, while suggesting that different actomyosin contractile units act in a highly collective and cooperative manner. We hypothesize that cells' actomyosin components robustly self-organize into marginally stable, plastic networks that give cells' their unique biomechanical properties.

Probing Fluctuations and Avalanches in the Cytoskeleton with Active Micropost Arrays

The cellular actomyosin network plays an important role in a wide range of cell behavior, including motility, morphology and mechanotransduction. Although the building blocks of actomyosin networks are well understood at the molecular level, the picture connecting these microscale units to cell-scale behavior remains incomplete. Here we present results using active micropost array detectors (AMPADS) to characterize the dynamical fluctuations and local rheology of cellular actomyosin networks in detail. AMPADS are poly(dimethylsiloxane) (PDMS) micropillar arrays with embedded magnetic nanowires that enable mechanical actuation of an adherent cell. We classified the cell-associated microposts based on their traction force, and identified two distinct populations, one containing posts coupled to stress fibers and the other containg posts coupled to the actomyosin cortex. Both groups show weak power law rheology, but we find that the fluctuations of stress fiber-associated posts are more active and highly anisotropic in comparison to the cortically-associated posts. Notably, the fluctuations of both populations are highly heterogenous and resemble a Lévy process. Specifically, the mean-squared displacements of both types of posts display broadly distributed amplitudes and super-diffusive behavior. We find that the amplitude distribution is fat-tailed, and is dominated by a corresponding distribution of intermittent, large step-like displacements, resembling avalanches and earthquakes in physical systems. Our regular array of detectors also allows us to determine the spatial extent and average symmetry of the largest rearrangements in the cortex, which are both spatially and temporally complex, resembling those in plastic solids. These results suggest that the dynamics of the cellular actomyosin network resembles what is seen in soft matter systems such as sandpiles and foams, where constituent components self-organize into marginally stable, plastic networks, with significant implications for future models of the cortex.

Step-like displacements of a deep seated gravitational slope deformation observed during the 2016–2017 seismic events in Central Italy

Deep Seated Gravitational Slope Deformations are characterized by low deformation rates although they can experience partial-collapse phases or more rapid movements, especially in presence of active tectonic structures. In the Central Italy, considering the high seismicity rate, seismic activity must be considered to be an important trigger of deep slope movements. We aim to contribute to the research in this field by reporting the results of a monitoring program on a Deep Seated Gravitational Slope Deformation in this region that involves marly calcareous rocks. We documented the pre-earthquakes evolution of the phenomenon and measured its displacements during the seismic sequence in 2016 and 2017 in Central Italy, which largest events were Mw 5.0-to-6.5.A multidisciplinary approach that combines a field geomorphological survey, installation of permanent GPS stations, and InSAR elaborations was adopted for this study.The average ground motion rate of the slope deformation before the earthquakes was very low (<3 mm/y) and not spatially homogenous, as detected by GPS and InSAR. In detail, the uppermost area of the slope instability likely moves faster than the lowest sector.On the other hand, GPS and InSAR recorded significant step-like movements, one to ten times higher than the normal activity rate, triggered by the Mw 5.0-to-6.5 earthquakes. In detail, the movement mainly depended on the magnitude of the earthquake and the distance from the epicenter, and only secondarily on the number of larger magnitude earthquakes on a given day.In conclusion, we furnished monitoring data on the activity rate of a Deep Seated Gravitational Slope Deformation in seismic context, we indicated two sectors of the investigated deformation that resulted more unstable and we proved that the combination of InSAR and GPS data is a useful monitoring system for earthquake-activated, slow-moving slope instabilities.

Formation of linear momentum in a rod during a laser pulse–matter interaction

Recently, we developed an optodynamic experimental technique that makes it possible to measure the linear momentum obtained by a metal target sample in the shape of a rod during a nanosecond laser pulse interaction in the ablative regime. The height of the rod’s rear end axial step-like displacement, caused by the first reflection of the laser-generated ultrasonic wave, is proportional to the linear momentum acquired by the rod. In comparison with commonly used ballistic methods, we can determine the acquired momentum on a much shorter time scale corresponding to the wave transition time, from the front to the rear end of the rod. Using this method we investigated the ambient air pressure dependence on the formation of linear momentum over a laser intensity range, from the ablation threshold to values about ten times higher. Steel rods of various diameters were used to demonstrate the effect of an expanding blast wave which delivers additional momentum to the target, when the laser beam on the target surface is smaller than the target itself. The typical value of the acquired target momentum is on the order of μN s and 10 μN s/J for the momentum coupling coefficient.

Mechanical Characterization of Brain Tissue in High-Rate Extension

Mechanical properties of brain tissue characterized in high-rate loading regime are indispensable for the analysis of traumatic brain injury (TBI). However, data on such properties are very limited. In this study, we measured transient response of brain tissue subjected to high-rate extension. A series of uniaxial extension tests at strain rates ranging from 0.9 to 25 s-1 and stress relaxation tests following a step-like displacement to different strain levels (15-50%) were conducted in cylindrical specimens obtained from fresh porcine brains. A strong rate sensitivity was found in the brain tissue, i.e., initial elastic modulus was 4.2 ± 1.6, 7.7 ± 4.0, and 18.6 ± 3.6 kPa (mean ± SD) for a strain rate of 0.9, 4.3, and 25 s-1, respectively. In addition, the relaxation function was successfully approximated to be strain-time separable, i.e., material response can be expressed as a product of time-dependent and strain-dependent components as:K(t) = G(t)σe(e), where G(t) is a reduced relaxation function, G(t) = 0.416e-t/0.0096+0.327e-t/0.0138+0.256e-t/1.508, and σe(e) is the peak stress following a step input of e. Results of the present study will improve biofidelity of computational models of a human head and provide useful information for the analysis of TBI under injurious environments with strain rates greater than 10 s-1.

Approximate step response of a nonlinear hydraulic mount using a simplified linear model

A simplified dynamic stiffness type linear model is used to analytically find the step responses of a nonlinear hydraulic mount in terms of the transmitted force and top chamber pressure. The closed form solution could be efficiently implemented with effective mount parameters, and peak value and the decay curve predictions could provide some insight into the nonlinear behavior. The analytical solutions to an ideal step input correlate well with both numerical simulations (of the same linear model) and measurements when a step-like displacement excitation is applied to fixed and free decoupler mounts.

Propagation of the 2001–2002 silent earthquake and interplate coupling in the Oaxaca subduction zone, Mexico

Abstract The aseismic slow slip event of 2001–2002 in Guerrero, Mexico, with an equivalent magnitude MW ~ 7.5, is the largest silent earthquake (SQ) among many recently recorded by GPS in different subduction zones (i.e. Japan, Alaska, Cascadia, New Zealand). The sub-horizontal and shallow plate interface in Central Mexico is responsible for specific conditions for the ~100 km long extended transient zone where the SQs develop from ~80 to ~190 km inland from the trench. This wide transient zone and relatively large slow slips of 10 to 20 cm displacements on the subduction fault result in noticeable surface displacements of 5–6 cm during the SQs. Continuous GPS stations allow one to trace the propagation of SQs, and to estimate their arrival time, duration and geometric attenuation. These propagation parameters must be accounted in order to locate source of slow slips events and to understand the triggering effect that they have on large subduction earthquakes. We use long-baseline tiltmeter data to define new time limits (onset and duration) for the SQs and continuous records from 8 GPS stations to determine the propagation of the 2001–2002 SQ in Central Mexico. Data from the CAYA and IGUA GPS stations, separated by ~170 km and located along the profile perpendicular to the trench, are used to determine that the surface deformation from the 2001–2002 SQ started almost instantaneously. It propagated parallel to the coast at ~2 km/day with an exponential attenuation of the horizontal surface displacement and a linear decrease of its duration with distance. Campaign data obtained yearly from 2001 to 2005 at the Oaxaca GPS network have been modeled according to a propagation of the 2001–2002 SQ step-like displacement anomaly. This modeling shows that the SQ ceased gradually in the central part of the Oaxaca segment of the subduction zone (west of Puerto Angel, PUAN) and then it apparently triggered another SQ in SE Oaxaca (between PUAN and Salina Cruz, SACR). The estimated horizontal velocities for inter-event epochs at each GPS site are used to assess an average interplate coupling in the Central Oaxaca subduction zone.