Optimal Sampling Placement in a Gaussian Random Field Based on Value of Information

Yoshida, Ikumasa; Tasaki, Yosuke; Otake, Yu; Wu, Stephen

doi:10.1061/ajrua6.0000970

Cited by 22 publications

(5 citation statements)

References 3 publications

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{\widehat{\boldsymbol{a}}}_{t}

and the error covariance matrix P t are evaluated as follows (Yoshida et al., 2018, 2021):

{\widehat{\boldsymbol{a}}}_{t}^{-}={\widehat{\boldsymbol{a}}}_{t-1},

{\boldsymbol{P}}_{t}^{-}={\boldsymbol{P}}_{t-1}+{\boldsymbol{\Sigma }}_{v},

{\widehat{\boldsymbol{a}}}_{t}={\widehat{\boldsymbol{a}}}_{t}^{-}+{\boldsymbol{P}}_{t}^{-}{\left({\boldsymbol{\Phi }}_{r}^{p}\right)}^{\mathrm{T}}{\left({\boldsymbol{\Sigma }}_{w}^{p}\right)}^{-1}\left({\boldsymbol{y}}_{t}-{\boldsymbol{\Phi }}_{r}^{p}{\widehat{\boldsymbol{a}}}_{t}^{-}\right),

P_{t} = [{((), {bold-italicP}_{t}^{-})}^{- 1} + {((), {boldΦ}_{r}^{p})}^{normalT} {((), {boldΣ}_{w}^{p})}^{- 1} {boldΦ}_{}]

…”

Section: Methodsmentioning

confidence: 99%

“…For that purpose, we employ the Kalman filter (Welch & Bishop, 1995) while accounting for the effects of uncertainty due to observation and system noise. With the Kalman filter, the posterior estimates of the state vector ât and the error covariance matrix P t are evaluated as follows (Yoshida et al, 2018(Yoshida et al, , 2021:…”

Section: Pseudo-super-resolution (Psr) Methods For Realizing a Virtua...mentioning

confidence: 99%

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Optimization of a Tsunami Gauge Configuration for Pseudo‐Super‐Resolution of Wave Height Distribution

Fujita,

Nomura,

Moriguchi

et al. 2024

Earth and Space Science

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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.

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{\widehat{\boldsymbol{a}}}_{t}

and the error covariance matrix P t are evaluated as follows (Yoshida et al., 2018, 2021):

{\widehat{\boldsymbol{a}}}_{t}^{-}={\widehat{\boldsymbol{a}}}_{t-1},

{\boldsymbol{P}}_{t}^{-}={\boldsymbol{P}}_{t-1}+{\boldsymbol{\Sigma }}_{v},

{\widehat{\boldsymbol{a}}}_{t}={\widehat{\boldsymbol{a}}}_{t}^{-}+{\boldsymbol{P}}_{t}^{-}{\left({\boldsymbol{\Phi }}_{r}^{p}\right)}^{\mathrm{T}}{\left({\boldsymbol{\Sigma }}_{w}^{p}\right)}^{-1}\left({\boldsymbol{y}}_{t}-{\boldsymbol{\Phi }}_{r}^{p}{\widehat{\boldsymbol{a}}}_{t}^{-}\right),

P_{t} = [{((), {bold-italicP}_{t}^{-})}^{- 1} + {((), {boldΦ}_{r}^{p})}^{normalT} {((), {boldΣ}_{w}^{p})}^{- 1} {boldΦ}_{}]

…”

Section: Methodsmentioning

confidence: 99%

Section: Pseudo-super-resolution (Psr) Methods For Realizing a Virtua...mentioning

confidence: 99%

Optimization of a Tsunami Gauge Configuration for Pseudo‐Super‐Resolution of Wave Height Distribution

Fujita,

Nomura,

Moriguchi

et al. 2024

Earth and Space Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…When the random variables follow a normal distribution, x 1 follows a normal distribution even when x 2 is given; thus, the posterior distribution parameters can be calculated analytically (Hoshiya and Yoshida, 1996;Yoshida et al, 2018).…”

Section: Issue-2: Bayesian Inference-based Input Parameter Estimationmentioning

confidence: 99%

Practical dynamic reliability analysis with spatiotemporal features in geotechnical engineering

Otake

Shigeno

Higo

et al. 2021

Georisk: Assessment and Management of Risk for Engineered Syste

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In recent years, improvements to the design of earth structures to ensure soil robustness against accidental disaster load conditions has gained considerable importance. In this paper, we propose a practical reliability analysis scheme that can enable the development of a robust design based on dimensionality reduction; this scheme employs input parameters, and it uses numerical analysis results as multidimensional information while preserving the spatiotemporal information to the maximum extent. Further, the proposed design scheme has practical design functions similar to those of conventional reliability analysis methods, such as the first-order second-moment method and first-order reliability method. Finally, the effectiveness of the proposed design scheme and its significant contribution to the practical design of earth structures are demonstrated via a time series seismic deformation analysis of simple embankments on liquefiable sand ground.

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“…Applications of VoI analysis to Structural Health Monitoring are presented by Pozzi and Der Kiurehian, Straub, Schweckendiek and Vrouwenvelder, Qin et al, and Zonta et al Evaluation of VoI in sequential infrastructure management is illustrated by Srinivasan and Parlikad . The recent interest in evaluating the economic impact of integrating sensors in infrastructure management is testified by many researches . Assessing the VoI allows for an appropriate calibration of investments in inspections, sensors, and monitoring systems, as the overall investment for collecting information should not exceed its value, In addition, it allows for comparing expensive explorative actions (as installing monitoring systems) with “exploitative” actions (i.e., actions that change the actual condition of the system, as retrofitting a component) on an equal ground.…”

Section: Introductionmentioning

confidence: 99%

“…9 The recent interest in evaluating the economic impact of integrating sensors in infrastructure management is testified by many researches. [10][11][12][13][14] Assessing the VoI allows for an appropriate calibration of investments in inspections, sensors, and monitoring systems, as the overall investment for collecting information should not exceed its value, In addition, it allows for comparing expensive explorative actions (as installing monitoring systems) with "exploitative" actions (i.e., actions that change the actual condition of the system, as retrofitting a component) on an equal ground.…”

Section: Introductionmentioning

confidence: 99%

What makes long‐term monitoring convenient? A parametric analysis of value of information in infrastructure maintenance

Pozzi

2019

Struct Control Health Monit

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Summary Information collected by monitoring systems can provide a significant economic benefit to the operation and maintenance of infrastructure components only under specific conditions. The information has to be precise, not redundant, related to relevant decision problems under uncertainty as, for example, the appropriate scheduling of maintenance actions, and the decision maker needs to be able to process that information and react timely. All these considerations can be naturally embedded in the value of information (VoI), a utility‐based metric for assessing the impact of the additional information in decision making under uncertainty. In this paper, we investigate the relation between the VoI and key features of the monitoring system, of the component deterioration and of the decision‐making process, including measure accuracy and availability, deterioration rates, damage predictability, reaction time, maintenance costs, and the economic discount factor. By leveraging previous work, we model the maintenance process as a partially observable Markov decision process, and we compute the VoI of long‐term monitoring. Our proposed framework allows for a detailed quantitative analysis on the joint effects of these features and can be useful to identify conditions when the benefit of monitoring is high, to assign priorities among components that deserve to be instrumented or to optimize the allocation of resources to monitoring efforts.

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Optimal Sampling Placement in a Gaussian Random Field Based on Value of Information

Cited by 22 publications

References 3 publications

Optimization of a Tsunami Gauge Configuration for Pseudo‐Super‐Resolution of Wave Height Distribution

Optimization of a Tsunami Gauge Configuration for Pseudo‐Super‐Resolution of Wave Height Distribution

Practical dynamic reliability analysis with spatiotemporal features in geotechnical engineering

What makes long‐term monitoring convenient? A parametric analysis of value of information in infrastructure maintenance

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