Evaluation of inspection features including exposure risk using a value of information analysis

Francesco, Domenic Di; Chryssanthopoulos, Marios K.; Faber, Michael Havbro; Bharadwaj, Ujjwal

doi:10.1080/10286608.2021.1887154

Cited by 2 publications

(6 citation statements)

References 27 publications

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“…It is sometimes referred to as preposterior decision analysis (Jordaan, 2005), and sometimes as multistage decision-making under uncertainty (Gelman et al, 2014). Its application to structural integrity management has been investigated extensively in recent years (Pozzi and Der Kiureghian, 2011;Straub, 2014;Di Francesco et al, 2021).…”

Section: Quantitative Methods (Bayesian Decision Analysis)mentioning

confidence: 99%

“…Quantifying the expected value of an inspection requires mathematical characterization of the information provided and a model relating it to the parameters of interest (Faber, 2000;Straub, 2004;Straub et al, 2006;Di Francesco et al, 2021). Assuming perfect information results in overestimation of the VoI of inspection activities.…”

Section: Inspection Qualitymentioning

confidence: 99%

“…These qualities should be reflected in its expected value. A detailed review of approaches to mathematically characterize such features of an imperfect inspection is provided in Di Francesco et al (2021). In Ali et al (2020), an alternative categorization of imperfect information (based on that proposed in Nielsen et al (2019) is also presented in the context of VoI.…”

Section: Imperfect Datamentioning

confidence: 99%

“…As identified in Di Francesco et al (2021), the precision, bias and reliability of inspection data can be modeled as shown in Equations ( 6) and ( 7). Here, a measurement of the extent of damage, d meas , is normally distributed with a mean equal to the true extent, d plus any bias, δ, with some sizing variance, ε.…”

Section: Imperfect Datamentioning

confidence: 99%

“…In Qin et al (2015), cases of an inspection not detecting damage are considered to be missing data. In Di Francesco et al (2021), such instances are treated as information in a Bayesian updating procedure. Rather, missing data represents the absence of information and some examples of this are listed below:…”

Section: Incomplete (Missing) Datamentioning

confidence: 99%

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Decision-theoretic inspection planning using imperfect and incomplete data

Francesco

Chryssanthopoulos

Faber

et al. 2021

DCE

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Attempts to formalize inspection and monitoring strategies in industry have struggled to combine evidence from multiple sources (including subject matter expertise) in a mathematically coherent way. The perceived requirement for large amounts of data are often cited as the reason that quantitative risk-based inspection is incompatible with the sparse and imperfect information that is typically available to structural integrity engineers. Current industrial guidance is also limited in its methods of distinguishing quality of inspections, as this is typically based on simplified (qualitative) heuristics. In this paper, Bayesian multi-level (partial pooling) models are proposed as a flexible and transparent method of combining imperfect and incomplete information, to support decision-making regarding the integrity management of in-service structures. This work builds on the established theoretical framework for computing the expected value of information, by allowing for partial pooling between inspection measurements (or groups of measurements). This method is demonstrated for a simulated example of a structure with active corrosion in multiple locations, which acknowledges that the data will be associated with some precision, bias, and reliability. Quantifying the extent to which an inspection of one location can reduce uncertainty in damage models at remote locations has been shown to influence many aspects of the expected value of an inspection. These results are considered in the context of the current challenges in risk based structural integrity management.

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Section: Quantitative Methods (Bayesian Decision Analysis)mentioning

confidence: 99%