Numerical Investigations into the Value of Information in Lifecycle Analysis of Structural Systems

Abstract

Preposterior analysis can be used to assess the potential of an experiment to enhance decision-making by providing information on parameters of the decision problem that are surrounded by epistemic uncertainties. The present paper describes a framework for preposterior analysis for support of decisions related to maintenance of structural systems. In this context, experiments may refer to inspections or structural health monitoring. The value-of-information concept comprises a powerful tool for determining whether the experimental cost is justified by the expected gained benefit during the lifecycle of a structural system and for identifying the optimal among different possible experimental schemes. This concept is herein elaborated through case studies that involve individual structural components subject to deterioration as well as systems with interdependencies. Extensive numerical investigations demonstrate how the decision problem is influenced by the assumed probabilistic models, including the type of probability distribution and the degree of uncertainty reflected in the coefficient of variation, the degradation law, the quantity and quality of information, and the probabilistic dependencies between the components of a system. Furthermore, challenges and potentials in value-of-information analysis for structural systems are discussed.