Abstract

AbstractAttempts 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.

Highlights

  • Impact Statement The primary novel component of this work is that a value of information calculation has been integrated with a partial pooling degradation model, relating information from inspection data across multiple locations

  • Engineering structures are often associated with the absence of large amounts of data and the presence of subject matter knowledge DNVGL (2017c)

  • This is often cited as the reason that quantitative risk-based inspection (RBI) is infeasible in practice (API, 2016; DNVGL, 2017b)

Read more

Summary

Introduction

Impact Statement The primary novel component of this work is that a value of information calculation has been integrated with a partial pooling degradation model, relating information from inspection data across multiple locations. Identifying where, when and how to inspect (or more generally, to collect information) is an on-going challenge to engineers This requires a mechanism for integrating information from an inspection with existing models of structural condition. Engineering structures are often associated with the absence of large amounts of data and the presence of subject matter knowledge DNVGL (2017c) This is often cited as the reason that quantitative risk-based inspection (RBI) is infeasible in practice (API, 2016; DNVGL, 2017b). Information (such as detection of the presence or absence of damage) at one location should inform estimates of the condition of other locations (Straub and Faber, 2005) This concept is intuitive, but incorporating this effect in a quantitative inspection plan requires models capable of characterizing this dependency.

Results
Discussion
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call