Optimal placement of sampling locations for identification of a two-dimensional space

Yoshida, Ikumasa; Tasaki, Yosuke; Tomizawa, Yukihisa

doi:10.1080/17499518.2021.1971255

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…Yoshida et al (2015, 2022) proposed value of information (VoI), which is defined as the expected reduction in decision error risk, to determine the observation locations such that the absolute value of VoI is minimized for the liquefaction countermeasure region along a river embankment and contaminant area. 47 , 48 ) These approaches to the selection of optimal measurement points mitigate the ill-posedness of inverse problems.…”

Section: Kalman Filters and Bayesian Methodsmentioning

confidence: 99%

“…King et al (2015) 45) and Shoji et al (2020) 46) selected the optimal sensor locations to maximize the smallest eigenvalue of the observability Gramian and the QR decomposition with column pivoting of the matrix to immediately estimate an entire target facility's behavior from information obtained from limited observation points, respectively. Yoshida et al (2015Yoshida et al ( , 2022 proposed value of information (VoI), which is defined as the expected reduction in decision error risk, to determine the observation locations such that the absolute value of VoI is minimized for the liquefaction countermeasure region along a river embankment and contaminant area. 47),48) These approaches to the selection of optimal measurement points mitigate the ill-posedness of inverse problems.…”

Section: Points Of Attentionmentioning

confidence: 99%

“…[49] is the standard deviation comprising the variance/covariance matrix R t in Eq. [48], and the value of , i S i should be selected from , 1 S 1 , , 2 S 2 , + and , obs S obs , depending on the observation point associated with error e i . Figure 15 shows the NRMSEs calculated with all the observation data Developments of inverse analysis by Kalman filters No.…”

Section: Applications To Geotechnical Engineering Practicesmentioning

confidence: 99%

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Developments of inverse analysis by Kalman filters and Bayesian methods applied to geotechnical engineering

MURAKAMI,

FUJISAWA,

SHUKU

2023

Proceedings of the Japan Academy. Ser. B: Physical and Biologic

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The present paper reviews recent activities on inverse analysis strategies in geotechnical engineering using Kalman filters, nonlinear Kalman filters, and Markov chain Monte Carlo (MCMC)/Hamiltonian Monte Carlo (HMC) methods. Nonlinear Kalman filters with finite element method (FEM) broaden the choices of unknowns to be determined for not only parameters but also initial and/or boundary conditions, and the use of the posterior probability of the state variables can be widely applied to, for example, the decision making for design changes. The relevance of the unknowns and the observed values and the selection of the best sensor locations are some of the considerations made while using the Kalman filter FEM. This paper demonstrates several real-world geotechnical applications of the nonlinear Kalman filter and the MCMC with FEM. Future studies should focus on the following areas: attaining excellent performance for longterm forecasts using short-term observation and developing a viable method for selecting equations that describe physical phenomena and constitutive models.

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Section: Kalman Filters and Bayesian Methodsmentioning

confidence: 99%

Section: Points Of Attentionmentioning

confidence: 99%

Section: Applications To Geotechnical Engineering Practicesmentioning

confidence: 99%

See 1 more Smart Citation

Developments of inverse analysis by Kalman filters and Bayesian methods applied to geotechnical engineering

MURAKAMI,

FUJISAWA,

SHUKU

2023

Proceedings of the Japan Academy. Ser. B: Physical and Biologic

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“…Placing sensors in optimal locations so that the sensory network provides valuable environmental information is known as the location–allocation problem and has attracted considerable attention for many years [ 9 , 10 , 11 , 12 ]. The sensor location–allocation problem has also been studied for water resource management [ 13 , 14 , 15 ], structural health monitoring [ 16 ], soil contamination [ 17 ], and many other domains. While different systems pose different challenges, the sensor location–allocation problem can be viewed as a case of choosing the best subset of sensor locations from a set of candidates that results in a desirable outcome under budget constraints, which usually dictate the number of sensors and their properties.…”

Section: Introductionmentioning

confidence: 99%

Information Theory Solution Approach to the Air Pollution Sensor Location–Allocation Problem

Mano

Kendler

Fishbain

2022

Sensors

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Air pollution is one of the prime adverse environmental outcomes of urbanization and industrialization. The first step toward air pollution mitigation is monitoring and identifying its source(s). The deployment of a sensor array always involves a tradeoff between cost and performance. The performance of the network heavily depends on optimal deployment of the sensors. The latter is known as the location–allocation problem. Here, a new approach drawing on information theory is presented, in which air pollution levels at different locations are computed using a Lagrangian atmospheric dispersion model under various meteorological conditions. The sensors are then placed in those locations identified as the most informative. Specifically, entropy is used to quantify the locations’ informativity. This entropy method is compared to two commonly used heuristics for solving the location–allocation problem. In the first, sensors are randomly deployed; in the second, the sensors are placed according to maximal cumulative pollution levels (i.e., hot spots). Two simulated scenarios were evaluated: one containing point sources and buildings and the other containing line sources (i.e., roads). The entropy method resulted in superior sensor deployment in terms of source apportionment and dense pollution field reconstruction from the sparse sensors’ network measurements.

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Preliminary field estimation of vertical displacements of prestressed concrete bridges under construction based on measured strains and inclinations

Yoshida,

Sekiya,

Wada

et al. 2024

Structures

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Optimal placement of sampling locations for identification of a two-dimensional space

Cited by 6 publications

References 20 publications

Developments of inverse analysis by Kalman filters and Bayesian methods applied to geotechnical engineering

Developments of inverse analysis by Kalman filters and Bayesian methods applied to geotechnical engineering

Information Theory Solution Approach to the Air Pollution Sensor Location–Allocation Problem

Preliminary field estimation of vertical displacements of prestressed concrete bridges under construction based on measured strains and inclinations

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