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An improved version of the solid–liquid coupled material point method (MPM) is proposed to simulate ground collapses with fluidization involving transition processes from soil structures to flowing mixture. A water-saturated soil is assumed based on porous media theory, and the physical quantities of the soil and water phases are assigned to two separate sets of particles (material points). The main contribution of this study is the introduction of the fractional step projection method for the time discretization of the momentum equation of the water phase on the assumption of incompressibility. Thanks to this, the proposed solid–liquid coupled MPM is capable of suppressing the pressure oscillations caused by the weak incompressibility of water, which is commonly assumed in the previous studies, and of representing the wide range of behavior of the soil–water mixture at relatively low computational cost. Also, B-spline basis functions are utilized for the spatial discretization to suppress the cell-crossing errors caused by particles crossing element (cell) boundaries. Several numerical tests are conducted to examine the performance of the proposed method that inherits the beneficial features of MPM and demonstrate the capability of reproducing a model experiment of wave collision to sandpile that exhibits the water flow-induced fluidization process of soil involving scouring, transportation and sedimentation.

•Contributions of DEM parameters on run-out distance are quantified.•Contribution ratio of spring coefficient and friction angle between elements are relatively low.•The bottom friction and coefficient of restitution are the main factors affecting the run-out distance.•The coefficient of restitution has a significantly greater effect in the front part.

We characterize seepage flow in porous media via a series of three-dimensional (3D) direct numerical simulations (DNSs) of Navier-Stokes flows in representative volume elements (RVEs) at the pore level. The immersed boundary method with a fixed staggered grid is employed for calculations based on the finite difference method in the pore domain formed with spatially arranged rigid particles in RVEs. The numerical results of the DNSs with different particle sizes and different seepage flow velocities are volume-averaged over the RVEs to evaluate the permeability coefficients of the seepage flows and are ordered according to the Reynolds numbers for porous media. After the numerical scheme is validated with a simple RVE model, the transition from Darcy to non-Darcy flow, for which experimental results have reported in the literature, is demonstrated in terms of the calculated permeability coefficients and examined based on the pore-level flow characteristics to clarify the mechanism of the permeability change, i.e. the macroscopic behaviour of RVEs related to the Reynolds number.

The round robin test (the simultaneous analysis of the same problem) is a method to investigate the variance and sensitivity of results provided by different analysts for a given problem and the reliability of the particular software used by each group participating in the test. A round robin test has been conducted for the traditional numerical method (e.g., finite difference method), but not yet for the discrete element method (DEM). This paper presents the results of the first ever round robin test on the DEM simulation for the angle of repose, involving 16 groups from around the world using different softwares. Within the scope of this round robin test, most groups reported similar simulation results for the angle of repose that differed only by a few degrees from the average of the experimental values, which was initially concealed from participants. There was also good agreement on the degree of variance of the angle of repose. In addition, this paper revealed the recent trends on the interparticle constitutive models and DEM softwares by considering the reports obtained from the participants.

This study presents a method for the detection of the most likely tsunami scenario among a set of possible scenarios using an observational wave sequence based on a sequential Bayesian update scheme. The proposed method consists of two phases: an offline preliminary learning phase and an online real‐time detection update phase. The innovation of this study is that proper orthogonal decomposition (POD) and Bayesian update are used together with an established tsunami simulation technique. In the offline reinforcement learning process, a series of tsunami simulations are carried out based on geophysically feasible scenarios, and the spatial modes of wave data calculated at predefined synthetic gauge locations are extracted through the application of POD. When a real tsunami event occurs and observational ocean data are obtained, the online process can then be performed as follows: using the stored spatial modes along with their component coefficients, pseudocoefficients are repeatedly estimated from the obtained wave data and used to sequentially update the most likely tsunami scenario according to the posterior probability through Bayesian update. A verification analysis is carried out to illustrate the procedure of the proposed method, and a validation analysis is conducted to demonstrate both the capabilities and applicability of the process with reasonable accuracy. A comprehensive discussion details the characteristic features of the proposed method in terms of the real‐time prediction of tsunami hazards and risks. We present a real‐time tsunami scenario detection framework using sequential probability updates and a kind of unsupervised learning. We first carry out a series of tsunami simulations based on geophysically feasible earthquake scenarios. Then, the characteristics of simulated wave history data from predefined ocean gauges are learned by means of proper orthogonal decomposition (POD), that is, the commonalities (“spatial modes”) among all tsunami scenarios are extracted. The scenario‐specific components (“component coefficients”) obtained together with such spatial modes enable us to handle all learned scenarios. When a real tsunami event occurs, the extracted spatial modes are used to infer the specific components of the current event, called “pseudocoefficients”, from the real‐time wave observations. By inputting these “pseudocoefficients” and the prelearned “component coefficient” into a Bayesian update scheme, the likelihood that each of the learned scenarios corresponds to the current event is sequentially evaluated. To demonstrate the specific procedure and its capabilities, validation analyses are conducted targeting the Nankai subduction zone. Our framework successfully detects the most likely scenarios, which have wave histories, maximum wave heights, and fault rupture patterns similar to those of the test scenarios, from the training dataset. A sequential Bayesian update scheme for the detection of the most likely tsunami scenario using proper orthogonal decomposition is presentedTwo case studies targeting the Nankai Trough are carried out to demonstrate the procedure and capabilities of the proposed methodThe most likely tsunami scenarios are detected with 7 min of observational wave data from 71 synthetic gauge points A sequential Bayesian update scheme for the detection of the most likely tsunami scenario using proper orthogonal decomposition is presented Two case studies targeting the Nankai Trough are carried out to demonstrate the procedure and capabilities of the proposed method The most likely tsunami scenarios are detected with 7 min of observational wave data from 71 synthetic gauge points

In conventional deformation analysis of geomaterials, the infinitesimal and the finite deformation theories have been widely used. These theories have been successfully implemented in several numerical methods, such as finite element method (FEM). As a result, it is now possible to predict a wide variety of deformation behaviors of geomaterials. However, when dealing with large deformation problems using the framework of the FEM, excess distortion of the FEM mesh may lead to instability of the calculation.

Evaluating the verification of numerical simulation analysis results requires a benchmark for the real behavior of a material. The present report provides a set of experimental data as a benchmark for Discrete Element Method (DEM) analysis. The experiment consisted of the measurement of the particle characteristics and the angles of repose of artificial particles prepared by a 3D printer. The use of a printer is effective for reducing the errors in the particle modeling of the DEM analysis. The particle characteristics of the artificial particles were measured in terms of their size, frictional properties, coefficients of restitution, and elastic stiffness. Then, the angles of repose of the particles were tested under cubical and cylindrical conditions. In addition, the visualization inside samples in the repose state was carried out using X-ray CT analysis. Thus, in the resting state, the strain distribution inside the samples as well as the displacement levels and vectors could be obtained. This technical report is based on the activities of the TC105 domestic committee of the JGS.

Abstract: Flowing sediments such as debris and liquefied soils could exert a tremendous amount of force as they impact objects along their paths. The total impact force generally varies with slope angle, velocity at impact, and thickness of the flowing sediment. Estimation of the impact force of flowing sediments against protective measures such as earth retaining structures is an important factor for risk assessment. In this paper, we conduct small-scale laboratory physical modeling of sand flow at different slopes and measure the impact force exerted by this material on a fixed rigid wall. We also conduct numerical simulations in the Eulerian framework using computational fluid dynamics algorithms to analyze and reproduce the laboratory test results. The numerical simulations take into consideration the overtopping of the wall with sand, which influenced the measured impact force–time history responses. In addition, the numerical simulations are shown to capture accurately the change of the impact force with slope angle. Finally, the modeling approach conducted in this study is used to estimate the quasi-static force generated by the sediment as it comes to rest on the wall following impact.

A numerical method is developed for the prediction of large deformations associated with a geomaterial flow during the occurrences of such geo-disasters as landslides and slope failures. The geomaterial is modeled as a viscous fluid, where a Bingham type constitutive model is proposed based on Coulomb's failure criterion and the viscosity is derived from the cohesion and friction angle. Numerical experiments on a 2D gravitational flow of a geomaterial column show that the constitutive model performs well. For solving Navier-Stokes's equations, Constrained Interpolated Profile (CIP) scheme is utilized, which is able to efficiently treat solid, liquid and gas together and has been successfully applied to problems in fluid dynamics. To prevent numerical instabilities due to a large Bingham viscosity, an implicit calculation procedure is proposed to calculate the viscous forces in the CIP. The method is finally used to simulate an earthquake-induced landslide that took place on May 26th, 2003 in the Northern part of Miyagi Prefecture in Japan. Results from soil-slump tests on the samples from the landslide site are used for determining the parameters in the simulation. Good agreements between the calculated and the observed results are obtained.

This study presents an innovative probabilistic tsunami inundation assessment for an earthquake scenario to randomly generate tsunami inundation depth distributions by quantitatively evaluating the spatial correlation of tsunami inundation depths using singular value decomposition (SVD) derived from proper orthogonal decomposition and to evaluate the tsunami inundation depths considering the imminent occurrence of an earthquake. We found a good agreement between the evaluation results of the proposed surrogate model and the numerical results of the nonlinear long wave equations for the tsunami inundation depth distribution in Kamakura city, Japan, due to the Sagami Trough megathrust earthquake. Evaluating the spatial correlation using SVD has the advantage that the covariance matrix does not need to be defined in advance but can be defined from the data itself. We also achieved a significant reduction in the number of required tsunami propagation simulations for the probabilistic assessment and attained higher computational efficiency by extracting spatial correlations with SVD. Furthermore, we conducted a probabilistic tsunami inundation assessment focusing on a relatively short period (i.e., 50 years) considering the time‐dependent occurrence probability of the target earthquake. The proposed probabilistic assessment method with mode decomposition is applicable to the general probabilistic tsunami hazard assessment by integrating it with physical stochastic slip models. This study proposes an innovative method to evaluate the probabilistic tsunami inundation area on land considering the imminence of an earthquake occurrence. Generally, the computational cost associated with evaluating the probabilistic tsunami inundation area is high because it is necessary to consider all possible uncertainties. In this study, we apply a singular value decomposition method to a small number of inundation depth distributions to extract the characteristics of these distributions. As a result, we are able to evaluate the probabilistic tsunami inundation depth in a future 50‐year period using a small number of inundation depth distributions. The singular value decomposition is used to evaluate the eigenmodes of the tsunami inundation depth and reduce the computational costThe variations in three variables are considered earthquake fault uncertainties: the fault depth, slip amount, and slip distributionA time‐dependent probabilistic tsunami inundation assessment is performed with the Brownian passage time distribution to account for seismic imminence The singular value decomposition is used to evaluate the eigenmodes of the tsunami inundation depth and reduce the computational cost The variations in three variables are considered earthquake fault uncertainties: the fault depth, slip amount, and slip distribution A time‐dependent probabilistic tsunami inundation assessment is performed with the Brownian passage time distribution to account for seismic imminence

Heavy rains brought about by Typhoon Hagibis (Typhoon No. 19) in October 2019 caused great damage to the Kanto and Tohoku regions of Japan. In the Tohoku region, the rainfall was particularly high in the coastal areas of Fukushima, Miyagi, and Iwate Prefectures. In this disaster report, focus is placed on the geotechnical damage in the northern and central areas of Miyagi Prefecture. In northern Miyagi, a levee failure occurred due to overflow; the possible reasons for the breakage are discussed. In central Miyagi, a slope failure occurred due to heavy rain which led to a river revetment erosion failure; this is also discussed in the present report.