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This study aims to evaluate the performance of the previous sequential Bayesian update for synthesizing tsunami scenarios in a diverse database consisting of complex fault rupture patterns with heterogeneous slip distributions. We utilize an existing database comprising 1771 tsunami scenarios targeting the city of Westport (WA, U.S.), which includes synthetic wave height records and inundation distributions resulting from a fault rupture in the Cascadia subduction zone. After preprocessing the training dataset according to the developed framework, Bayesian updates are performed sequentially to evaluate the probability that each training scenario is a test case. In addition to detecting the scenario with the highest probability, i.e., the most likely scenario, we synthesize the scenario by the weighted mean of all the learning scenarios by their probabilities. The accuracies of tsunami risk evaluation based on both resultant scenarios are evaluated from the maximum offshore wave, inundation depth, and its distribution. The results of the cross-validation with five different testing/training datasets showed that the weighted mean scenario has almost comparable performance to that of the most likely scenario. Additionally, the sequential Bayesian update improves the accuracy of both methods if a 3–4 minute observation time window is given, and has an advantage over the benchmark results provided by dynamic time warping with full-time series data. [ABSTRACT FROM AUTHOR]

In this study, we propose a scenario superposition method for real‐time tsunami wave prediction. In the offline phase, prior to actual tsunami occurrence, hypothetical tsunami scenarios are created, and their wave data are decomposed into spatial modes and scenario‐specific coefficients by the singular value decomposition. Then, once an actual tsunami event is observed, the proposed method executes an online phase, which is a novel contribution of this study. Specifically, the predicted waveform is represented by a linear combination of training scenarios consisting of precomputed tsunami simulation results. To make such a prediction, a set of weight parameters that allow for appropriate scenario superposition is identified by the Bayesian update process. At the same time, the probability distribution of the weight parameters is obtained as reference information regarding the reliability of the prediction. Then, the waveforms are predicted by superposition with the estimated weight parameters multiplied by the waveforms of the corresponding scenarios. To validate the performance and benefits of the proposed method, a series of synthetic experiments are performed for the Shikoku coastal region of Japan with the subduction zone of the Nankai Trough. All tsunami data are derived from numerical simulations and divided into a training data set used as scenario superposition components and a test data set for an unknown real event. The predicted waveforms at the synthetic gauges closest to the Shikoku Islands are compared to those obtained using our previous prediction method incorporating sequential Bayesian updating. Plain Language Summary: In this study, we propose a new tsunami waveform prediction method that represents actual tsunami waveforms by superposing scenario waveforms resulting from a series of precomputed tsunami simulations. The prediction scheme uses a Bayesian approach to sequentially estimate the weight parameters assigned to the scenarios using actual real‐time wave height data. At the same time, it identifies the probability distribution of the weight parameters and provides information to understand the forecast error. The actual wave heights of the target event can be predicted by multiplying of the wave heights in the scenario database by the estimated weight parameters. To verify the advantages of the proposed method, a demonstration test is conducted for the Shikoku region of Japan, which is threatened by a large tsunami risk due to the Nankai Trough subduction zone. We show that the proposed method can predict wave heights even when the target event is not present in the scenario database, and we show its superiority over the previously proposed method. Key Points: The proposed scenario superposition method achieves real‐time tsunami waveform prediction via a linear combination of precomputed scenariosA set of parameters weighting each scenario is probabilistically estimated using a Bayesian approachThree‐hour tsunami waveforms can be predicted using wave heights measured at existing gauge locations in less than 10 min [ABSTRACT FROM AUTHOR]

The large deformation and fluidization process of a solid–fluid mixture includes significant changes to the temporal scale of the phenomena and the shape and properties of the mixed material. This paper presents an extended B‐spline (EBS)‐based implicit material point method (EBS‐MPM) for the coupled hydromechanical analysis of saturated porous media to enhance the overall versatility of MPM in addressing such diverse phenomena. The proposed method accurately represents phenomena such as high‐speed motion in both the quasi‐static and dynamic states by employing a full formulation of coupled hydromechanical modeling. The weak imposition of boundary conditions based on Nitsche's method allows representing the boundary conditions independent of the relative position of the particles and computational grid. In addition, it enables dynamic changes in the boundary domain based on the deformation. The robustness of this boundary representation is reinforced using EBS basis functions, which prevent the degradation of the condition number of the system matrices regardless of the position of the boundary domain with respect to the computational grid. Furthermore, a stabilization method based on a variational multiscale method (VMS) approach is employed to provide the flexibility in choosing arbitrary basis functions for spatial discretization, facilitating the effective construction of EBS. Numerical examples including comparisons between a full formulation and a simplified formulation are presented to demonstrate the performance of the developed method under various boundary conditions and loading states across different time scales. [ABSTRACT FROM AUTHOR]

Summary: This study presents a diffusive‐discrete crack transition scheme for stably conducting ductile fracture simulations within a finite strain framework. In the developed scheme, the crack initiation and propagation processes are determined according to an energy minimization problem based on crack phase‐field theory, and the predicted diffusive path is transformed to a discrete representation using the finite cover method during the staggered iterative scheme. In particular, for stably conducting ductile fracture simulations, three computational techniques, the staggered iterative configuration update technique, the subincremental damage update technique, and the crack opening stabilization technique, are introduced. The first and second techniques can be used regardless of whether discrete cracks are considered, and the third technique is specialized for diffusive‐discrete crack transition schemes. Accordingly, ductile fracture simulations with the developed scheme rarely encounter troublesome problems such as oscillations in the displacement field and severe distortion of finite elements that can lead to divergence of calculations. After the crack phase‐field model for ductile fractures is formulated and discretized, the numerical algorithms for realizing the diffusive‐discrete crack transition while maintaining computational stability are explained. Several representative numerical examples are presented to demonstrate the performance and capabilities of the developed approach. [ABSTRACT FROM AUTHOR]

This study examined the applicability of factorization machines with quantum annealing (FMQA) to the field of landslide risk assessment for two specific black‐box optimization problems, hyperparameter optimization (HPO) for metamodeling and metamodel‐based simulation optimization (MBSO) targeting granular flow simulation using discrete element method (DEM). These two optimization problems are solved successively: HPO is first performed to determine the hyperparameters of the Gaussian process regression (GPR) metamodel, which is then used as a low‐cost, fast approximate solver of granular flow simulations for MBSO. After conducting a series of granular flow simulations using DEM, a metamodel is created that outputs a risk index of interest, the run‐out distance, from its input parameters by employing GPR with two hyperparameters, length‐scale and signal variance. Subsequently, HPO is performed to obtain the optimal set of hyperparameters by applying FMQA and other optimization methods using another set of hyperparameters determined using the gradient‐ascent method as the reference solution. Finally, using the metamodel created by each optimization method as an approximate solver for DEM simulations, MBSO is performed to find the optimal target output, the maximum run‐out distance, in the space of physical input parameters for risk assessment. A comparison of the performance of FMQA with that of other methods shows that FMQA is competitive in terms of efficiency and stability with state‐of‐the‐art algorithms such as Bayesian optimization. [ABSTRACT FROM AUTHOR]

This study presents a semi‐implicit MPM to adequately characterize the mechanical behavior of unsaturated soil based on Biot's mixture theory. To represent the dependency of the degree of saturation on the suction, we employ the VG model along with a soil‐water characteristic curve, which determines a functional form of permeability called the Mualem model. Hencky's hyperelastic model and the Drucker‐Prager model assuming nonassociativity are adopted for elastic and plastic deformations, respectively. The novelty of this study is the incorporation of the fractional‐step method into the MPM framework so that the pore water pressure is obtained by implicitly solving the pressure Poisson's equation, which reduces numerical instability and improves computational efficiency. Also, because the drag force between solid and liquid phases is evaluated using the intermediate velocity of pore water relative to the intermediate velocity of solid skeleton, the time increment can be chosen without considering the magnitude of water permeability. In addition, to suppress "odd‐even" oscillation, we employ a sub‐grid method in which two grids with different spatial resolutions are used for the velocities and pore water pressure. Furthermore, considering the advantages and disadvantages of two different interpolation schemes for pore water pressure, we suggest switching the schemes depending on the model conditions. Several numerical examples are presented to demonstrate the performance of the proposed method. Specifically, unidirectional consolidation and leak flow analyses are performed for verification purposes, followed by validation analysis of a model experiment of infiltration‐induced landslides. [ABSTRACT FROM AUTHOR]

In this paper, we propose a digital model to run an evacuation simulation that reflects the road network blockage caused by the landslide and river flooding damage in Marumori-machi, Miyagi Prefecture, which was severely damaged by Typhoon No. 19 in 2019. In particular, we propose an evacuation agent simulation model that can be extended in the future to scenarios related to disaster response decisions, education, and awareness on the part of residents and can reproduce the evacuation agent situation of a real disaster. The method adjusts a set of parameters of vehicles and pedestrian agents to reproduce the evacuation situation. Then, using the parameter set, we perform the agent simulations under different scenarios varying the time of disaster occurrence and evacuation. Finally, we evaluate the changes in the number of people who have completed their evacuation, the number of victims, etc. The results of the scenarios with different disaster occurrence times showed that the final evacuation rate situation improved by at least 1% (about 170 people who completed evacuation) during daylight time compared to nighttime. The relationship between sunset time and the start of evacuation was qualitatively and quantitatively demonstrated to be supported. It was also confirmed that the evacuation situation did not change much with the time of the evacuation announcement. These results show trends limited to the present study's disasters and scenarios and do not necessarily provide generalized findings for disaster response. However, the results indicate that applying the proposed methodology to a greater number of disasters and scenario conditions could lead to better analysis and optimization of disaster response. Interviews with government disaster management officials in the target areas suggest that confirming the effectiveness of disaster response while visualizing the distribution of disaster risk in the areas from a bird's eye view, as in this study, could enhance existing response plans. This approach may also present information comprehensibly for staff and residents who did not experience the disaster firsthand, simulating the experience for better understanding. [ABSTRACT FROM AUTHOR]

In this study, we present an optimization method for determining a cost‐effective sparse configuration for tsunami gauges to realize the reconstruction of high‐resolution wave height distribution throughout the target region based on the concept of super‐resolution. This optimization method consists of three procedures. First, we generate time series data of tsunami wave heights at synthetic gauges by conducting numerical simulations of various earthquake and tsunami scenarios at the target site. Next, we apply proper orthogonal decomposition to the synthetic tsunami data to extract the spatial features of the wave height distribution. Finally, according to these spatial features, an optimization process is performed to determine a sparse configuration of synthetic gauges. In the optimization, the optimal gauges are sequentially selected from the set of synthetic gauges to reconstruct the wave height distribution with the highest accuracy. Targeting hypothetical Nankai Trough earthquakes and tsunamis, we determine the optimal configuration near Shikoku and demonstrate the wave height reconstruction capability of the approach by comparing the performance of networks with optimally and randomly placed gauges. The results indicate that coastal gauges contribute more to improving the reconstruction accuracy and that a configuration with 21 optimal gauges has satisfactory performance. In addition, we assess the performance of the existing NOWPHAS network installed in the Shikoku region and find that the reconstruction performance of the existing network is equivalent to that of the optimal gauge network. Plain Language Summary: This study introduces a method of optimizing the sparse locations where actual tsunami gauges should be installed to obtain information on tsunami wave heights at any given point. By optimizing the locations of the observation points, it is possible to extend observations recorded at only a small number of points to obtain a good approximation to data at other points where the tsunami was not directly observed. First, numerical simulations are performed based on assumed earthquake and tsunami scenarios to generate synthetic time series data of tsunami waves. Then, by applying proper orthogonal decomposition to the obtained synthetic data, the characteristics of the tsunami wave height distribution are extracted. Finally, these characteristics are used to perform optimization by sequentially selecting the best gauges from among a set of candidate points to reconstruct wave height information for the entire target area, thereby determining the placements of a limited number of gauges. In a numerical demonstration example simulating a Nankai Trough earthquake and tsunami, the placement of gauges off the coast of Shikoku is optimized, and it is shown that the wave heights at arbitrary points over the entire area can be reproduced using data from at least 21 optimally placed gauges. Key Points: An optimization method implementing a greedy algorithm is presented to design a cost‐effective observational network of tsunami gaugesHigh‐resolution information about tsunami wave height is constructed from sparse observations based on the concept of super‐resolutionThe optimization process considering the existing observation network locations identifies their sufficiency or deficiency [ABSTRACT FROM AUTHOR]

Summary: This study presents an enhancement of the diffusive‐discrete crack transition scheme (10.1002/nme.7169) to describe dynamic fracture at finite strain. In the enhanced scheme, the crack initiation, propagation, and bifurcation processes are determined from an energy minimization problem based on crack phase‐field theory, and the predicted diffusive crack is replaced by the discrete representation using the finite cover method. In the meantime, numerical damping is introduced to maintain computational stability and avoid distortion of the physical mesh in the finite cover context. By taking advantage of the features of the diffusive‐discrete crack transition scheme, the proposed approach enables us to stably simulate a series of dynamic fracture events involving crack initiation at an arbitrary location, propagation, and bifurcation in arbitrary directions, arbitrary divisions of an original object into multiple portions, and independent motions of divided portions. After spatial and temporal discretizations by the finite cover method and the Newmark method are described, as well as the simulation algorithm of the enhanced finite cover‐based staggered iterative procedure for dynamic fracture, several representative numerical examples are presented to demonstrate the performance and capabilities of the developed approach. [ABSTRACT FROM AUTHOR]

With a view to simulating the entire process of a landslide‐triggered tsunami, ranging from tsunami generation to offshore wave propagation, with relatively low computational costs, we present a 2D–3D coupling strategy to bridge 3D MPM–FEM hybrid and 2D shallow water (SW) simulations. Specifically, considering the difference in basis functions between the 3D and 2D analysis methods, we devise a novel variable passing scheme in the domain overlapping method, in which a slightly overlapped domain enables the generated wave to pass through the connection boundaries with as little discrepancy as possible. For the tsunami generation stage in the 3D domain, the hybrid method combining the finite element method (FEM) and material point method (MPM) is adopted. In this method, the 3D governing equation of the solid phase is solved with the MPM, whereas the well‐established 3D stabilized FEM is applied to that of the fluid phase in an Eulerian frame. Additionally, the phase‐field method is employed to track the free surface of the 3D fluid domain. On the other hand, the SW equation that represents the offshore wave motion in the 2D domain is solved by the 2D stabilized FEM. Several numerical examples are presented to demonstrate the effectiveness of the developed scheme in properly passing the data from 3D/2D to 2D/3D domains. [ABSTRACT FROM AUTHOR]

Summary: An enhancement of the extended B‐spline‐based implicit material point method (EBS‐MPM) is developed to avoid pressure oscillation and volumetric locking. The EBS‐MPM is a stable implicit MPM that enables the imposition of arbitrary boundary conditions thanks to the higher‐order EBS basis functions and the help of Nitsche's method. In particular, by means of the higher‐order EBS basis functions, the EBS‐MPM can suppress the cell‐crossing errors caused by material points crossing the background grid boundaries and can avoid both the stress oscillations arising from inaccurate numerical integration and the ill‐conditioning of the resulting tangent matrices. Although the higher‐order EBS basis functions are known to avoid volumetric locking, the problem of pressure oscillation has not yet been resolved. Therefore, to suppress pressure oscillation due to quasi‐incompressibility, we propose the incorporation of the F‐bar projection method into the EBS‐MPM, which is compatible with the higher‐order EBS basis functions. Three representative numerical examples are presented to demonstrate the capability of the proposed method in suppressing both the pressure oscillation and volumetric locking. The results of the proposed method are compared to those of the finite element method with F‐bar elements and those of isogeometric analysis with quadratic NURBS elements. [ABSTRACT FROM AUTHOR]

It is commonplace that the ground near river is thickly covered with soft clay. Thus, in order to prevent it from the natural hazard, it is necessary that local people construct a robust embankment of countermeasure by constructing river bank in the soft ground, with low level of bearing capacity. To solve the problem aforementioned, local people are widely known to apply traditional method such as timber or bamboo pile. To establish this method, the Ministry of Public Works has published a technical guideline for reinforced soft ground before embankment. The guideline shows the process of mattress construction including: 1) cutting the ground for site preparation; 2) installing timber piles; 3) laying geo-grid on top of the piles and; 4) putting and compacting a granular material on the geo-grid. The guideline assists engineers to construct an embankment on the mattress. Unfortunately, the guideline never details a reasonable design based on geotechnical engineering. Hence, this paper aims to propose and discuss, a criterion of bearing capacity of reinforced soft clay using several empirical calculations. The model cases were simulated for small footings subjected by static loading on the mattress. As a result, the calculations will lead to the criterion of allowable bearing capacity of reinforced soft clay with timber pile.

This study presents a novel transition scheme that can trace an actual crack path as closely as possible and stably update its explicit crack tip even in a large deformation regime. The crack initiation and propagation processes are determined from an energy minimization problem with respect to the displacement field and crack phase‐field, and the predicted path represented by a diffuse crack topology is replaced by a discrete path by applying the finite cover method. By developing a technique for determining explicit crack tips, the crack topology is updated from diffusive to discrete intermittently during the course of the staggered iterative procedure. Therefore, even a curved crack path that evolves significantly within a single time increment can be explicitly captured. Additionally, to stably update an explicit crack tip within the finite strain framework, we introduce a stabilization technique. Specifically, pseudo‐stiffness is applied to severely damaged elements around the discrete crack path to prevent excessively large deformations, and the corrector of the displacement increment in the global Newton–Raphson iterative procedure is intentionally diminished so that the discrete crack opens in a gradual and stable manner. After describing the individual techniques devised in the proposed scheme for achieving these features based on their algorithmic aspects, we present several representative numerical examples to demonstrate the performance and capability of the developed approach. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: 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. Key Points: 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 [ABSTRACT FROM AUTHOR]