Practical dynamic reliability analysis with spatiotemporal features in geotechnical engineering

Otake, Yu; Shigeno, Kyohei; Higo, Yosuke; Muramatsu, Shogo

doi:10.1080/17499518.2021.1971250

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…In this paper, we replace P ( t ) with a Σ r ( a = 0.10), which is independent of time, simply for convenience. This assumption is justified to a certain extent considering that

{{\Sigma}}_{r}^{2}

is the covariance matrix of the coefficient matrix A and the observation errors in Bayesian inference are conventionally set to 10% of the original variation (e.g., Otake et al., 2021). More appropriate covariance matrices will be explored in future studies with the use of other approaches (e.g., Kalman filtering, hierarchical Bayesian models).…”

Section: Methodsmentioning

confidence: 99%

Sequential Bayesian Update to Detect the Most Likely Tsunami Scenario Using Observational Wave Sequences

et al. 2022

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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.

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“…In this paper, we replace P ( t ) with a Σ r ( a = 0.10), which is independent of time, simply for convenience. This assumption is justified to a certain extent considering that

{{\Sigma}}_{r}^{2}

Section: Methodsmentioning

confidence: 99%

Sequential Bayesian Update to Detect the Most Likely Tsunami Scenario Using Observational Wave Sequences

et al. 2022

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“…The most outstanding conferences tackled topics of liquefaction [202][203][204], disaster waste [205,206], and landslides [207][208][209]. An amount of 7.42% corresponded to proceedings papers, with 97 documents highlighting landslides [110,210,211] and earthquakes [212][213][214][215], while 2.97% were books e.g., [216]; chapters of books, e.g., [156,217]; data papers [218]; notes [219]; reviews [220]; editorials [221], and short reviews, e.g., [222,223].…”

Section: Period III (2011-2021)mentioning

confidence: 99%

A Systematic Review of the Relationship between Geotechnics and Disasters

et al. 2022

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Landslides, earthquakes, and other natural events can change the landscape and generate human and economic losses, affecting transportation and public service infrastructure. In every geotechnical project, the investigation phase plays a fundamental role in reducing the risk of occurrence and mitigating catastrophes. As a result, governments have created entities to study disasters and identify triggering factors that generate huge losses worldwide. This research aims to conduct a systematic review of the relationship between geotechnics and disasters through bibliometric techniques, scientific production evaluation, and case studies analysis to recognize key topics, methods, and thematic development of the research worldwide. The research methodology consisted of three steps: (1) Database analysis, selection, and combination, (2) bibliometric analysis, and (3) systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. The systematic review with bibliometric analysis collected data from 1973 to 2021, with 1299 academic publications indexed in the Scopus and WoS database. These results indicated a growing trend of annual publications on disasters and their relationship with geotechnical studies, highlighting current issues and technological innovation. The main research trends in disaster risk assessment were topics mainly linked to landslides, earthquakes, liquefaction, and inappropriate analysis models with applications of geophysical methods, laboratory tests, remote sensing, and numerical models.

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“…Reliability analysis has proved to be a useful tool for solving the challenging geotechnical problems. [17][18][19][20][21][22][23][24][25] Among a series of available methods, the straightforward and simple method is Monte Carlo simulation method (MCS) which involves the establishment of probabilistic models, (for example, a repetitive execution of Newmark sliding block model). [26][27][28][29][30] However, the MCS requires a large number of executions of deterministic model to obtain the rational result at small target failure probability, and the calculation efficiency deteriorates dramatically.…”

Section: Introductionmentioning

confidence: 99%

“…Reliability analysis has proved to be a useful tool for solving the challenging geotechnical problems 17–25 . Among a series of available methods, the straightforward and simple method is Monte Carlo simulation method (MCS) which involves the establishment of probabilistic models, (for example, a repetitive execution of Newmark sliding block model) 26–30 .…”

Section: Introductionmentioning

confidence: 99%

Seismic reliability analysis of soil slope based on Newmark sliding block model with representative slip surfaces and response surfaces

et al. 2023

Engineering Reports

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This paper presents a framework combining Monte Carlo Simulation (MCS) and the Newmark sliding block model with representative slip surfaces (RSS) (model II) and multiple response surfaces method (MRSM) to conduct seismic reliability analysis and risk assessment of soil slopes. An empirical threshold is introduced to define the limit state function to identify the failure samples in MCS and the sliding area and Newmark sliding displacement are multiplied to quantify the failure consequence. The proposed methodology is illustrated through a soil slope with multiple layers. The calculation results demonstrate that traditional Newmark sliding block model (model I) tend to underestimate the variations of yield acceleration. Both the failure probability and landslide risk exhibit decreasing trends with the increase of threshold. Significant discrepancy in failure probability and landslide risk between two models is found even for a small threshold. Therefore, the proposed methodology is highly recommended in seismic reliability analysis and risk assessment. The contributions of RSSs to the failure probability and landslide risk are insensitive to the variation of displacement thresholds.

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Practical dynamic reliability analysis with spatiotemporal features in geotechnical engineering

Cited by 10 publications

References 26 publications

Sequential Bayesian Update to Detect the Most Likely Tsunami Scenario Using Observational Wave Sequences

Sequential Bayesian Update to Detect the Most Likely Tsunami Scenario Using Observational Wave Sequences

A Systematic Review of the Relationship between Geotechnics and Disasters

Seismic reliability analysis of soil slope based on Newmark sliding block model with representative slip surfaces and response surfaces

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