Optimal Sampling Placement in a Gaussian Random Field Based on Value of Information
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 4, Issue 3
Abstract
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.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 4 • Issue 3 • September 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Dec 31, 2016
Accepted: Dec 22, 2017
Published online: Apr 20, 2018
Published in print: Sep 1, 2018
Discussion open until: Sep 20, 2018
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