An alternative quantification of the value of information in structural health monitoring

Abstract

Analogous to an experiment, a structural health monitoring (SHM) system may be thought of as an information-gathering mechanism. Gathering the information that is representative of the structural state and correctly inferring its meaning helps engineers (decision-makers) mitigate possible losses by taking appropriate actions (risk-informed decision-making). However, the design, research, development, installation, maintenance, and operation of an SHM system are an expensive endeavor. Therefore, the decision to invest in new information is rationally justified if the reduction in the expected losses by utilizing newly acquired information is more than the intrinsic cost of the information acquiring mechanism incurred over the lifespan of the structure. This article investigates the economic advantage of installing an SHM system for inference of the structural state, risk, and lifecycle management by using the value of information (VoI) analysis. Among many possible choices of SHM system designs (different information-gathering mechanisms), pre-posterior decision analysis can be used to select the most feasible design. Traditionally, the cost–benefit analysis of an SHM system is carried out through pre-posterior decision analysis that helps one evaluate the benefit of an experiment or an information-gathering mechanism using the expected value of information metric. This study proposes an alternate normalized metric that evaluates the expected reward ratio (benefit/gain of using an SHM system) relative to the investment risk (cost of SHM over the lifecycle). The analysis of evaluating the relative benefit of various SHM system designs is carried out by considering the concept of the VoI, by performing pre-posterior analysis, and the idea of a perfect experiment is discussed.

An optimal sensor placement design framework for structural health monitoring using Bayes risk

“…However, acquiring information through a mechanism 𝑒 bears cost, represented by Ψ design-in (𝑒). Acquiring the new information is meaningful and economical if and only if the additional cost required to gather the information is outweighed by the reduction in the expected losses evaluated by considering the additional information (see Chadha et al [47]). Recall the expression of the Bayes risk Ψ design (𝑒) in Eq.…”

An Optimal Sensor Network Design Framework for Structural Health Monitoring Using Value of Information

“…The goal of an SHM system is to recommend a maintenance strategy selected from a set of predefined choices Ω D ={d 0 , d 1 , … , d n }. For a risk-profile of the decision-maker parametrized by ( γ , ξ ) , let L ( d i ,θ true ; γ , ξ ) denote the consequence cost/regret/loss function that defines the total perceived loss as a consequence of making the decision d i when the true state of the structure is θ true ( t ) at time t (see [1,2]). To obtain the benefit of an SHM system in the design phase, we require the following:…”

“…1. We need a probabilistic state-parameter evolution model (See [2]). Let Θ (t) denote a random variable representing the state-parameter at a time instance t ∈ Ω T .…”

The modeling of risk perception in the use of structural health monitoring information for optimal maintenance decisions

“…A number of works [45,46] dealt with considering and modeling risk-aversiveness in the decision-making process. Faber et al [47,48,49,50] investigated the decision-making under uncertainty in SHM and structural reliability problems. More recently, approaches quantifying the economic benefit of using an SHM system (refer to [51,52,53,54]) have been investigated by using value of information theory [55] in conjuncture with expected utility theory.…”