  Large uncertainties in many phenomena have challenged decision making.
Collecting additional information to better characterize reducible
uncertainties is among decision alternatives. Value of information (VoI)
analysis is a mathematical decision framework that quantifies expected
potential benefits of new data and assists with optimal allocation of resources
for information collection. However, analysis of VoI is computational very
costly because of the underlying Bayesian inference especially for
equality-type information. This paper proposes the first surrogate-based
framework for VoI analysis. Instead of modeling the limit state functions
describing events of interest for decision making, which is commonly pursued in
surrogate model-based reliability methods, the proposed framework models system
responses. This approach affords sharing equality-type information from
observations among surrogate models to update likelihoods of multiple events of
interest. Moreover, two knowledge sharing schemes called model and training
points sharing are proposed to most effectively take advantage of the knowledge
offered by costly model evaluations. Both schemes are integrated with an error
rate-based adaptive training approach to efficiently generate accurate Kriging
surrogate models. The proposed VoI analysis framework is applied for an optimal
decision-making problem involving load testing of a truss bridge. While
state-of-the-art methods based on importance sampling and adaptive Kriging
Monte Carlo simulation are unable to solve this problem, the proposed method is
shown to offer accurate and robust estimates of VoI with a limited number of
model evaluations. Therefore, the proposed method facilitates the application
of VoI for complex decision problems.
