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

Chadha, Mayank; Ramancha, Mukesh K.; Vega, Manuel A.; Conte, Joel P.; Todd, Michael D.

doi:10.1016/j.ress.2022.108845

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

Journal Article · Fri Sep 23 00:00:00 EDT 2022 · Reliability Engineering and System Safety

DOI:https://doi.org/10.1016/j.ress.2022.108845· OSTI ID:1890987

Chadha, Mayank ^[1]; ^[1]; ^[2]; Conte, Joel P. ^[1]; ^[1]

Univ. of California, San Diego, CA (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

This paper proposes an approach to select a maintenance strategy from a predefined set of choices considering the decision maker’s behavioral risk profile. It is assumed that the damage state is characterized by a continuous state parameter probabilistically inferred from observable sensor data. This work applies an engineering application of consequence-based decision-making incorporating the acceptable risk intensity of the decision-maker, i.e., the decision-maker’s (individual or an organization) valuation of the outcome of a decision, using a risk profile model. The utility of a decision-maker is subjective, and this paper considers the fact that different decision-makers mentally assign a different importance factor (the utility) to the seriousness or urgency to take necessary actions with the increasing intensity of structural damage. The approach herein incorporates a layer of human psychology on selecting appropriate maintenance strategies that not only depend on the posterior distribution of unmeasurable damage state but also consider the behavioral risk profile of the decision-maker. Further, the collective decision-making of an organization consisting of many individuals is also investigated. The approach is exemplified in a case study involving life cycle monitoring of a miter gate, part of a lock system enabling navigation of inland waterways.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: US Army Research Office (ARO); USDOE

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1890987

Report Number(s):: LA-UR-21-28992

Journal Information:: Reliability Engineering and System Safety, Journal Name: Reliability Engineering and System Safety Vol. 229; ISSN 0951-8320

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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

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