Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Kriging
This work addresses the computational bottleneck in VoI analysis for decision-making under uncertainty, particularly in engineering domains like infrastructure, though it appears incremental as it builds on existing surrogate and Kriging methods.
The paper tackles the computational cost of Value of Information (VoI) analysis by proposing the first surrogate-based framework that models system responses instead of limit state functions, enabling knowledge sharing and adaptive training. It demonstrates accurate and robust VoI estimates for a truss bridge load testing problem with limited model evaluations, outperforming state-of-the-art methods that fail to solve it.
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.