Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Kriging

02/06/2020
by   Chi Zhang, et al.
3

Large uncertainties in many phenomena of interest have challenged the reliability of pertaining decisions. Collecting additional information to better characterize involved 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, a primary challenge facing VoI analysis is the very high computational cost 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.

READ FULL TEXT
research
07/08/2020

A two-level Kriging-based approach with active learning for solving time-variant risk optimization problems

Several methods have been proposed in the literature to solve reliabilit...
research
01/10/2019

Surrogate-assisted reliability-based design optimization: a survey and a new general framework

Reliability-based design optimization (RBDO) is an active field of resea...
research
04/18/2011

Metamodel-based importance sampling for the simulation of rare events

In the field of structural reliability, the Monte-Carlo estimator is con...
research
09/11/2021

Adaptive network reliability analysis: Methodology and applications to power grid

Flow network models can capture the underlying physics and operational c...
research
02/04/2020

REAK: Reliability analysis through Error rate-based Adaptive Kriging

As models in various fields are becoming more complex, associated comput...
research
02/23/2023

Active learning for structural reliability analysis with multiple limit state functions through variance-enhanced PC-Kriging surrogate models

Existing active strategies for training surrogate models yield accurate ...
research
08/28/2017

Deep Learning for Accelerated Reliability Analysis of Infrastructure Networks

Natural disasters can have catastrophic impacts on the functionality of ...

Please sign up or login with your details

Forgot password? Click here to reset