Optimal sampling placement in a gaussian random field based on value of information

Ikumasa Yoshida, Yosuke Tasaki, Yu Otake, Stephen Wu

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)


In the context of sampling, monitoring, and sensing in infrastructures, there is an interest in algorithms to produce an observation plan that is cost effective, while maximizing the benefits of the new observations. This paper proposes a method to obtain an optimal sampling plan in terms of the number and placement of additional sampling points based on value of information (VoI). VoI can be computed easily through updating a Gaussian random field, i.e., kriging, which is a probabilistic interpolation method. Particle swarm optimization is introduced to optimize a set of sites for new observations with respect to VoI. In the paper, after presenting the basic concept and formulation, we describe applying the method to the placement of additional borings as a liquefaction countermeasure for an embankment along a river. The optimal sampling placement may be obtained at a feasible computational cost even when the number of additional sampling points is greater than 10. The optimal number of sampling points is also evaluated based on VoI. DOI: 10.1061/AJRUA6.0000970. 2018 American Society of Civil Engineers.

Original languageEnglish
Article number00970
JournalASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Issue number3
Publication statusPublished - 2018 Sept 1


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