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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]

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]

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]