Periodic Displacements Research Articles

Dynamic prediction model of landslide displacement based on (SSA-VMD)-(CNN-BiLSTM-attention): a case study

Accurately predicting landslide displacement is essential for reducing and managing associated risks. To address the challenges of both under-decomposition and over-decomposition in landslide displacement analysis, as well as the low predictive accuracy of individual models, this paper proposes a novel prediction model based on time series theory. This model integrates a Convolutional Neural Network (CNN) with a Bidirectional Long-Short Term Memory network (BiLSTM) and an attention mechanism to form a comprehensive CNN-BiLSTM-Attention model. It harnesses the feature extraction capabilities of CNN, the bidirectional data mining strength of BiLSTM, and the focus-enhancing properties of the attention mechanism to enhance landslide displacement predictions. Furthermore, this paper proposes utilizing the Variational Mode Decomposition (VMD) method to decompose both landslide displacement and its influencing factors. The VMD algorithm’s parameters are optimized through the Sparrow Search Algorithm (SSA), which effectively minimizes the influence of subjective bias while maintaining the integrity of the decomposition. A fusion of the Maximal Information Coefficient (MIC) and Grey Relational Analysis (GRA) is then employed to identify the critical influencing factors. The selected sequence of factors that conforms to the criteria is used as the input variable for displacement prediction via the CNN-BiLSTM-Attention model. The cumulative displacement prediction is derived by aggregating the results from each sequence. The study reveals that the SSA-VMD-CNN-BiLSTM-Attention model introduced herein achieves superior predictive accuracy for both periodic and random term displacements than individual models. This advancement provides a dependable benchmark for forecasting displacement in similar landslide scenarios.

Landslide displacement prediction model based on multisource monitoring data fusion

This study presents a displacement prediction model that integrating various monitoring data sources to comprehensive landslide monitoring information utilization. The research focuses on the landslide in Lingwan Village, utilizing Ridge Regression (RR) to integrate diverse monitoring data. The fused displacement data undergo decomposition into trend and periodic terms using the Smoothness Priors Approach (SPA). The Autoregressive Integrated Moving Average (ARIMA) model predicts trend and periodic term displacements separately, which are then combined to forecast the overall landslide surface displacement accurately. The RR model demonstrates a strong correlation coefficient of 0.998, indicating high predictive ability. Trend term displacement prediction outperformed periodic term prediction, especially during the rainy season. The RR-SPA-ARIMA model has an average absolute error of 1.73 mm, a mean bias error of −0.08 mm, and a root mean square error of 1.87 mm. The model demonstrates high prediction accuracy.

Conformational Rearrangements of Adsorbed Polyampholytes under Periodic Changes in Polarity of a Charged Prolate Gold Nanospheroid

Conformational rearrangements of polyampholytic polypeptides adsorbed on the surface of a charged prolate gold nanospheroid with a periodic change in time of its polarity along the rotation axis have been studied using molecular dynamics simulation. The radial distributions of the density of polypeptide atoms in the equatorial region of the nanospheroid have been calculated, as well as the distributions of the linear density of polypeptide atoms along the major axis of the nanospheroid. At a low simulation temperature, a girdle polyampholytic fringe was formed in the central region of the nanospheroid and its ordering by layers, depending on the type of units, occurred with an increase in the charge of the nanospheroid with a simultaneous increase in the width of the macromolecular fringe along the rotation axis. The thickness of such a fringe along the cross section depends on the distance between the oppositely charged units in thepolyampholyte. At high temperatures and high absolute values of the total charge of the spheroidal nanoparticle, there were periodic displacements of the polyampholytic fringe toward the poles of the nanospheroid, being in antiphase for oppositely charged metallic nanospheroids. A mathematical model is presented for describing the conformational structure of a polyampholyte macromolecule on a prolate nanospheroid in an alternating electric field with the approximation of a prolate spheroid by a spherical cylinder

Periodic Atomic Displacements and Visualization of the Electron-Lattice Interaction in the Cuprate

Traditionally, X-ray scattering techniques have been used to detect the breaking of the structural symmetry of the lattice, which accompanies a periodic displacement of the atoms associated with charge density wave (CDW) formation in the cuprate pseudogap states. Similarly, the Spectroscopic Imaging Scanning Tunneling Microscopy (SI-STM) has visualized the short-range CDW. However, local coupling of electrons to the lattice in the form of a short-range CDW has been a challenge to visualize, thus a link between these measurements has been missing. Here, we introduce a novel STM-based technique to visualize the local bond length variations obtained from topographic imaging with picometer precision. Application of this technique to the high-Tc cuprate superconductor Bi2Sr2CaCu2O8+{\delta} revealed a high-fidelity local lattice distortion of the BiO lattice as large as 2%. In addition, analysis of local breaking of rotational symmetry associated with the bond lengths reveals modulations around four-unit-cell periodicity in both B1 and E representations in the C4v group of the lattice, which coincides with the uni-directional d-symmetry CDW (dCDW) previously identified within the CuO2 planes, thus providing direct evidence of electron-lattice coupling in the pseudogap state and a link between the X-ray scattering and STM measurements. Overall, our results suggest that the periodic lattice displacements in E representations correspond to a locally-frozen version of the soft phonons identified by the X-ray scattering measurements, and a fluctuation of the bond length is reflected by the fluctuation of the dCDW formation near the quantum critical point.

A Vorticity‐Divergence View of Internal Wave Generation by a Fast‐Moving Tropical Cyclone: Insights From Super Typhoon Mangkhut

AbstractTropical cyclones (TCs) are powered by heat fluxes across the air‐sea interface, which are in turn influenced by subsurface physical processes that can modulate storm intensity. Here, we use data from 6 profiling floats to recreate 3D fields of temperature (T), salinity (S), and velocity around the fast‐moving Super Typhoon Mangkhut (western North Pacific, September 2018). Observational estimates of vorticity (ζ) and divergence (Γ) agree with output from a 3D coupled model, while their relation to vertical velocities is explained by a linear theoretical statement of inertial pumping. Under this framework, inertial pumping is described as a linear coupling between ζ and Γ, whose oscillations in quadrature cause periodic displacements in the ocean thermocline and generate near‐inertial waves (NIWs). Vertical profiles of T and S show gradual mixing of the upper ocean with diffusivities as high as κ ∼ 10−1 m2 s−1, which caused an asymmetric cold wake of sea surface temperature (SST). We estimate that ∼10% of the energy used by mixing was used to mix rainfall, therefore inhibiting SST cooling. Lastly, watermass transformation analyses suggest that κ > 3 × 10−3 m2 s−1 above ∼110 m depth and up to 600 km behind the TC. These analyses provide an observational summary of the ocean response to fast‐moving TCs, demonstrate some advantages of ζ and Γ for the study of internal wave fields, and provide conceptual clarity on the mechanisms that lead to NIW generation by winds.

SPECTRUM CHARACTERISTICS IN TELEVISION SYSTEMS OBJECTS WITH QUASI-PERIODIC PHASE-TIME CONVERSIONS OF VIDEO INFORMATION

Quasiperiodic phase-time transformations of video information can be car-ried out both due to the objects corresponding displacements themselves, and through the necessary displacements organization, for example, in the signal sensor of television systems. The essence of this transformation type is rel-ative, time-periodic displacements of controlled objects in raster space on the photosensitive surface of the signal sensor, accompanied by changes in the position of the object relative to the optical axis. In this case, large-scale changes in coordinates also affect the angle of each element of the object image. Consequently, the action of the operator of “periodic circular dis-placements” is associated with the occurrence of the effect of relative sup-pression of the upper spatial frequencies of the initial influence. The article shows that in terms of the effect on the spatio-temporal spectrum of the initial impact, it is advisable to classify the operator of “periodic linear displace-ments” as operators of the differential type, and the operator of “periodic circular displacements” as operators of the integral type. Any operator of integral type can be reduced to differential type and vice versa. An example is considered where the operator of periodic circular displacements is easily converted to a differential type when introducing a return circular displace-ment along the same trajectory.

Spatial Vibration and Displacement Measurement Through Single-Point Continuous and Interval Scanning Laser Doppler Vibrometer

The scanning laser Doppler vibrometer (SLDV) is an extremely valuable method for studying the dynamic properties of vibrating objects, such as those in industrial production, entomology research, and biomedical testing. The traditional SLDV method scans samples using an inclined laser, which ignores the three-dimensional (3-D) information of the sample, resulting in a relatively complicated calculation process. In industrial processes and scientific research, the measured target may have a known 3-D regular surface. When a structure with height information moves laterally, it causes a change in longitudinal information, which is of great benefit in the analysis of the 3-D vibration and displacement information of the target. In this article, continuous and interval scanning laser Doppler methods are proposed to expand the measuring capability of SLDV methods, based on the scanning of spherical structures. First, a spherical structure is introduced to simplify the solution to spiral vibrations. The high-precision displacement can also be calculated by fitting the circular scanning curve. Finally, the scanning method for 3-D displacement measurement is experimentally validated, and the theoretical errors are demonstrated. This work provides a new methodology to realize both periodic and nonperiodic displacements in all degrees of freedom (DOF) with high accuracy using a single-point SLDV, which is expected to have a long-term impact on industry vibration analysis and biological motion research. Most importantly, setting a reflector with a spherical surface on the end-effector of the micro–nano robot can realize the 3-D cross-scale repeated localization.

Prediction Interval Estimation of Landslide Displacement Using Bootstrap, Variational Mode Decomposition, and Long and Short-Term Time-Series Network

Using multi-source monitoring data to model and predict the displacement behavior of landslides is of great significance for the judgment and decision-making of future landslide risks. This research proposes a landslide displacement prediction model that combines Variational Mode Decomposition (VMD) and the Long and Short-Term Time-Series Network (LSTNet). The bootstrap algorithm is then used to estimate the Prediction Intervals (PIs) to quantify the uncertainty of the proposed model. First, the cumulative displacements are decomposed into trend displacement, periodic displacement, and random displacement using the VMD with the minimum sample entropy constraint. The feature factors are also decomposed into high-frequency components and low-frequency components. Second, this study uses an improved polynomial function fitting method combining the time window and threshold to predict trend displacement and uses feature factors obtained by grey relational analysis to train the LSTNet networks and predict periodic and random displacements. Finally, the predicted trend, periodic, and random displacement are summed to the predicted cumulative displacement, while the bootstrap algorithm is used to evaluate the PIs of the proposed model at different confidence levels. The proposed model was verified and evaluated by the case of the Baishuihe landslide in the Three Gorges reservoir area of China. The case results show that the proposed model has better point prediction accuracy than the three baseline models of LSSVR, BP, and LSTM, and the reliability and quality of the PIs constructed at 90%, 95%, and 99% confidence levels are also better than those of the baseline models.

A Linear Elasticity Theory to Analyze the Stress State of an Infinite Layer with a Cylindrical Cavity under Periodic Load

The design of parts of machines, mechanisms, structures and foundations, particularly in the aerospace industry, is closely related to the definition of the stress state of the body. The accuracy of determining the stress state is the key to optimizing the use of materials. Therefore, it is important to develop methods to achieve such goals. In this work, the second main spatial problem of the elasticity theory is solved for a layer with a longitudinal cylindrical cavity with periodic displacements given on the surface of the layer. The solution of the problem is based on the generalized Fourier method for a layer with a cylindrical cavity. To take into account periodic displacements, an additional problem is applied with the expansion of the solution for a layer (without a cavity) in the Fourier series. The general solution is the sum of these two solutions. The problem is reduced to an infinite system of linear algebraic equations, which is solved by the reduction method. As a result, the stress-strain state of the layer on the surface of the cavity and isthmuses from the cavity to the boundaries of the layer was obtained. The conducted numerical analysis has a high accuracy for fulfilling the boundary conditions and makes it possible to assert the physical regularity of the stress distribution, which indicates the reliability of the obtained results. The method can be applied to determine the stress-strain state of structures, whose calculation scheme is a layer with a cylindrical cavity and a given periodic displacement. Numerical results make it possible to predict the geometric parameters of the future structure.

Maximizing the consistency between regional and global reference frames utilizing inheritance of seasonal displacement parameters

Although modern global geometric reference frames (GRFs) such as the International Terrestrial Reference Frame (ITRF) can be used anywhere on Earth, regional reference frames (RRFs) are still used to densify geodetic control and optimize solutions for continental-scale areas and national purposes. Such RRFs can be formed by densifying the ITRF, utilizing GPS / GNSS stations common to both the ITRF and the RRF. It is possible to attach a RRF to a GRF by ensuring that some or all of the coefficients of the trajectory models in the RRF are ‘inherited’ from the trajectory models that define the GRF. This can be done on an epoch-by-epoch basis, or (our preference) via transformations that operate simultaneously in space and time. This paper documents inconsistencies in the densification of ITRF that arise when the common stations’ trajectory models ignore periodic displacements. This results in periodic coordinate biases in the RRF. We describe a generalized procedure to minimize this inconsistency when realizing any RRF aligned to the ITRF or any other ‘primary’ frame. We show the method used to realize the Argentine national frame Posiciones Geodésicas Argentinas (POSGAR) and discuss our results. Discrepancies in the periodic motion amplitudes in the vertical were reduced from 4 mm to less than 1 mm for multiple stations after applying our technique. We also propose adopting object-oriented programming terminology to describe the relationship between different reference frames, such as a regional and a global frame. This terminology assists in describing and understanding the hierarchy in geodetic reference frames.

Finite element method based micromechanical methodology for homogenizing fiber/fabrics-reinforced composites and their progressive failure

The complex microstructure of fabrics makes it challenging to homogenize them using an analytical/semi-analytical micro-mechanics approach like the Mori-Tanaka and the method of cells. Analytical approaches developed for fabrics estimate their elastic constants and do not easily accommodate material nonlinearities. Numerical schemes such as the finite element method (FEM) have been developed to analyze failure in fabrics and not necessarily find moduli of the homogenized material. Here we use the FEM based micro-mechanics approach to both homogenize fiber-reinforced and plain/twill/satin weave, as well as braids/fabrics-reinforced composites using simplified unit cells. The homogenization is based on Hill’s averaging theorem and employs tie constraints to impose periodic displacements and anti-periodic tractions on boundaries of the unit cell. A continuum damage mechanics approach is used to study damage initiation and progression, and degradation of material properties. The predicted elastic constants, failure modes and ultimate strengths of fabrics are found to compare well with their either experimental or computed values available in the literature. We have also analyzed delamination and damage propagation in a laminate with an open hole at the centroid and uniform tensile tractions at its end faces to estimate its ultimate strength that is found to agree well with the experimental value.

Data Mining and Deep Learning for Predicting the Displacement of "Step-like" Landslides.

Landslide displacement prediction is one of the unsolved challenges in the field of geological hazards, especially in reservoir areas. Affected by rainfall and cyclic fluctuations in reservoir water levels, a large number of landslide disasters have developed in the Three Gorges Reservoir Area. In this article, the Baishuihe landslide was taken as the research object. Firstly, based on time series theory, the landslide displacement was decomposed into three parts (trend term, periodic term, and random term) by Variational Mode Decomposition (VMD). Next, the landslide was divided into three deformation states according to the deformation rate. A data mining algorithm was introduced for selecting the triggering factors of periodic displacement, and the Fruit Fly Optimization Algorithm–Back Propagation Neural Network (FOA-BPNN) was applied to the training and prediction of periodic and random displacements. The results show that the displacement monitoring curve of the Baishuihe landslide has a “step-like” trend. Using VMD to decompose the displacement of a landslide can indicate the triggering factors, which has clear physical significance. In the proposed model, the R2 values between the measured and predicted displacements of ZG118 and XD01 were 0.977 and 0.978 respectively. Compared with previous studies, the prediction model proposed in this article not only ensures the calculation efficiency but also further improves the accuracy of the prediction results, which could provide guidance for the prediction and prevention of geological disasters.

A Novel Model for Landslide Displacement Prediction Based on EDR Selection and Multi-Swarm Intelligence Optimization Algorithm.

With the widespread application of machine learning methods, the continuous improvement of forecast accuracy has become an important task, which is especially crucial for landslide displacement predictions. This study aimed to propose a novel prediction model to improve accuracy in landslide prediction, based on the combination of multiple new algorithms. The proposed new method includes three parts: data preparation, multi-swarm intelligence (MSI) optimization, and displacement prediction. In the data preparation, the complete ensemble empirical mode decomposition (CEEMD) is adopted to separate the trend and periodic displacements from the observed cumulative landslide displacement. The frequency component and residual component of reconstructed inducing factors that related to landslide movements are also extracted by the CEEMD and t-test, and then picked out with edit distance on real sequence (EDR) as input variables for the support vector regression (SVR) model. MSI optimization algorithms are used to optimize the SVR model in the MSI optimization; thus, six predictions models can be obtained that can be used in the displacement prediction part. Finally, the trend and periodic displacements are predicted by six optimized SVR models, respectively. The trend displacement and periodic displacement with the highest prediction accuracy are added and regarded as the final prediction result. The case study of the Shiliushubao landslide shows that the prediction results match the observed data well with an improvement in the aspect of average relative error, which indicates that the proposed model can predict landslide displacements with high precision, even when the displacements are characterized by stepped curves that under the influence of multiple time-varying factors.

Detection of periodic displacements of shell structures with edges using spline surfaces, meshes and point clouds

Abstract This research paper tackles the problem of determining displacements of complex-shaped shell structures, measured periodically using laser scanning. Point clouds obtained during different measurement epochs can be compared with each other directly or they can be converted into continuous models in the form of a triangle mesh or smooth patches (spline functions). The accuracy of the direct comparison of point clouds depends on the scanning density, while the accuracy of comparing the point cloud to the model depends on approximation errors that are formed during its creation. Modelling using triangle meshes flattens the local structure of the object compared to the spline model. However, if the shell has edges in its structure, their exact representation by spline models is impossible due to the undulations of functions along them. Edges can also be distorted by the mesh model by their chamfering with transverse triangles. These types of surface modelling errors can lead to the generation of pseudo-deformation of the structure, which is difficult to distinguish from real deformation. In order to assess the possibility of correct determination of deformation using the above-mentioned methods, laser scanning of a complex shell structure in two epochs was performed. Then, modelling and comparison of the results of periodic measurements were carried out. As a result of the research, advantages and disadvantages of each method were identified. It was noticed that none of the methods made it possible to correctly represent all deformations while suppressing pseudo-deformation. However, the combination of their best qualities made it possible to determine the actual deformation of the structure.

Comparison of wheel load application methods in single-wheel testbeds

This work reviews wheel load application methods in single-wheel testbeds (SWTBs), used widely to study terramechanics. Normal force oscillations have been reported in SWTBs, and visible in time-series data when provided. This has usually been attributed to grousers, but is also visible for wheels without grousers suggesting other system dynamics (e.g. friction in the vertical axis) are the cause. Furthermore, accelerations related to periodic vertical displacements of the wheel (whether caused by grousers or otherwise) cannot explain more than a small fraction of the amplitude of normal force variations. In this work, a 4-bar mechanism was developed that balances the normal load and ensures that a constant (not oscillating) normal force is achieved throughout terramechanics experiments. System behaviour with and without the 4-bar mechanism was assessed, highlighting the need for a 4-bar mechanism or other method of balancing normal loads if a constant normal force is desired. No significant effects on average experimental results were observed due to normal force oscillation, but artifacts were observed in the time-series data. Comparisons of results with a system where wheel load is applied pneumatically and characterization of friction in each testbed provide further support to the conclusion that normal force oscillations are mainly caused by friction in the vertical axis.

A new hybrid method for establishing point forecasting, interval forecasting, and probabilistic forecasting of landslide displacement

In addition to its inherent evolution trend, landslide displacement contains strong fluctuation and randomness, and omni-directional landslide displacement prediction is more scientific than single point prediction or interval prediction. In this study, a new hybrid approach composed of double exponential smoothing (DES), variational mode decomposition (VMD), long short-term memory network (LSTM), and Gaussian process regression (GPR) is proposed for the point, interval, and probabilistic prediction of landslide displacement. The proposed model includes two parts: (i) predicting the inherent evolution trend of landslide displacement through DES-VMD-LSTM; (ii) evaluating the uncertainty in the first prediction based on the GPR model. In the first part, DES is used to predict the trend displacement, and the periodic and stochastic displacement in the residual displacement is extracted by VMD and predicted by the LSTM. The triggering factors of the periodic and stochastic displacement are screened by the maximum information coefficient (MIC), and the screened factors are decomposed into low- and high-frequency components by VMD to predict the periodic and stochastic displacements, respectively. The first cumulative displacement prediction results are achieved by adding the predicted trend and the periodic and stochastic displacements. By setting the first predicted displacement as the input and the actual displacement as the expected output, the point, interval, and probabilistic predictions of landslide displacement are achieved through the GPR model. The plausibility of the proposed model is validated with data from the Bazimen (BZM) and Baishuihe (BSH) landslides in the Three Gorges Reservoir area. This model has the potential to achieve the deterministic prediction of landslide displacement and quantify the uncertainty contained in the displacement. A comparative study shows that this method has a high performance for the point, interval, and probabilistic prediction of landslide displacement.

Pressure-driven micro-poro-mechanics: A variational framework for modeling the response of porous materials

Porous materials are highly relevant in engineering and medical applications due to their enhanced properties and lightweight nature. Current micromechanical models of porous materials can accurately predict the response under the assumptions of small deformations and drained conditions, typically driven by imposed deformations. However, the theoretical framework for the micromechanical modeling of porous material driven by pore pressure in the large-deformation range has been understudied. In this work, we develop a finite-deformation variational framework for pressure-driven foams, i.e., materials where the pore pressure in the cavities produces the deformation. We further consider different kinematical constraints in the formulation of boundary conditions: kinematic uniform displacements, periodic displacements and uniform traction. We apply the proposed model in the numerical simulation of lung porous tissue using a spherical alveolar geometry and an image-based geometry obtained from micro-computed-tomography images of rat lung. Our results show that the stress distributions in the spherical alveolar model are highly dependent on the kinematical constraints. In contrast, the stress distribution in the image-based alveolar model is not affected by the choice of boundary conditions. Further, when comparing the response of pressure-driven versus deformation-driven models, we conclude that hydrostatic stresses experience a marked shift in their distribution, whereas the deviatoric stresses remain unaffected. Our findings of how stresses are affected by the choice of boundary conditions and geometry take particular relevance in the simulation of the lungs, where the pressure-driven and deformation-driven cases are related to mechanical ventilation and spontaneous breathing.

Eigenmodes and resonance vibrations of graphene nanomembranes

Natural and resonant oscillations of suspended circular graphene and hexagonal boron nitride (h-BN) membranes (single-layer sheets lying on a flat substrate having a circular hole of radius $R$) have been simulated using full-atomic models. Substrates formed by flat surfaces of graphite and h-BN crystal, hexagonal ice, silicon carbide 6H-SiC and nickel surface (111) have been used. The presence of the substrate leads to the forming of a gap at the bottom of the frequency spectrum of transversal vibrations of the sheet. The frequencies of natural oscillations of the membrane (oscillations localized on the suspended section of the sheet) always lie in this gap, and the frequencies of oscillations decrease by increasing radius of the membrane as $(R+R_i)^{-2}$ with nonezero effective increase of radius $R_i>0$. The modeling of the sheet dynamics has shown that small periodic transversal displacements of the substrate lead to resonant vibrations of the membranes at frequencies close to eigenfrequencies of nodeless vibrations of membranes with a circular symmetry. The energy distribution of resonant vibrations of the membrane has a circular symmetry and several nodal circles, whose number $i$ coincides with the number of the resonant frequency. The frequencies of the resonances decrease by increasing the radius of the membrane as $(R+R_i)^{\alpha_i}$ with exponent $\alpha_i<2$. The lower rate of resonance frequency decrease is caused by the anharmonicity of membrane vibrations.

Total finite element tearing and interconnection method for computationally efficient micromechanical analysis

Micromechanical analysis of a representative volume element (RVE) is commonly performed to estimate the material’s effective/homogenized properties in a multiscale analysis of deformation of materials. Typically numerical analysis techniques such as the finite element (FE) method are used for such an analysis. A highly refined FE mesh is required to capture microstructure features accurately for the analysis of RVE. However, this increases the number of degrees of freedom and affects computational time adversely. In this contribution a total finite element tearing and interconnection (TFETI) domain decomposition method based approach is presented for a computationally efficient micromechanical analysis. Two critical aspects of the micromechanical analysis, namely, a) computationally efficient solution of the boundary value problem and b) ease of computation of effective properties, are addressed in this work. This work focuses on the displacement driven micromechanical analysis where the boundary conditions are available in terms of displacements over the entire boundary. Two types of displacement boundary conditions, viz uniform or proportional displacements and periodic displacements corresponding to the state of uniaxial extension and simple shear are considered. The performance of the adapted TFETI with commonly used preconditioners, namely Dirichlet and lumped, for such displacement driven analysis is investigated. An efficient algorithm that exploits the structure of the TFETI method is proposed to calculate the effective properties. The proposed method’s efficacy is demonstrated by analyzing some representative model problems of composite materials. It is observed that the method’s performance depends on various problem parameters such as volume fraction, the shape of inclusion, the distance between the inclusions, and the contrast between the material properties of matrix and inclusion. The performance also depends on the numerical method parameters, such as the number of subdomains, shape of subdomains, and preconditioners. Therefore, a systematic study is carried out to study the influence of these parameters on the method’s performance.

Recharge response and kinematics of an unusual earthflow in Liechtenstein

Understanding landslide behavior over medium and long timescales is crucial for predicting landslide hazard and constructing accurate landscape evolution models. The behavior of landslides in soil that undergo periodic displacements, termed earthflows or compound soil slides, is especially difficult to forecast at these timescales. This is because velocities can increase by orders of magnitude over annual to decadal timescales, due to processes such as changing recharge conditions, erosion of the landslide toe, and retrogression of the landslide head. In this paper, we provide a detailed analysis of the Schlucher landslide, an unusual earthflow that is perched above the village of Malbun, Liechtenstein. This landslide had been displacing by 10 to 20 cm/year until 2015, when displacements on the order of 2 m/year occurred from 2016 to 2018. These large displacements damaged landslide mitigation measures, caused numerous surface deformation features, and threatened the local population downstream of the earthflow. This landslide has an unusually long monitoring record, with accurate displacement and climatic data available since 1983. We analyze this nearly 40-year monitoring time series to estimate recharge from snowmelt and rainfall, and its correlation with displacement. We also analyze recently collected, high-resolution surface and subsurface data in order to understand landslide response to recharge, landslide kinematics through time, and catastrophic failure potential. We find that interannual displacements can be explained with variations in recharge; however, periodic surges with recurrence times of tens of years must be explained by other mechanisms. In particular, recharge into the landslide during the recent acceleration (2016 to 2018) was not anomalously high. Instead, we argue that loss of internal strength is responsible for this recent acceleration period, and that this mechanism should be considered when forecasting the surge potential for certain earthflows and soil slides.