Cost-benefit analysis for SHM systems based on minimum detectable parameter change
(2024) 11th European Workshop on Structural Health Monitoring, EWSHM 2024- Abstract
The expected monetary value is a criterion for assessing the Value of Structural Health Monitoring (SHM) and supporting the associated decision making. Its calculation can be challenging, as it requires considering all possible outcomes of the SHM system. In this article a new approach to cost-benefit analysis for SHM systems is proposed, which is based on an estimate of the capability of the SHM system to detect changes in structural parameters. By applying the linear Bayesian filter for parameter identification, it is possible to predict, considering prior knowledge of the unchanged structure, the minimum change in a structural parameter that the SHM system can detect with a given reliability. This prediction simplifies the... (More)
The expected monetary value is a criterion for assessing the Value of Structural Health Monitoring (SHM) and supporting the associated decision making. Its calculation can be challenging, as it requires considering all possible outcomes of the SHM system. In this article a new approach to cost-benefit analysis for SHM systems is proposed, which is based on an estimate of the capability of the SHM system to detect changes in structural parameters. By applying the linear Bayesian filter for parameter identification, it is possible to predict, considering prior knowledge of the unchanged structure, the minimum change in a structural parameter that the SHM system can detect with a given reliability. This prediction simplifies the calculation of the expected monetary value, which is based on a single measurement, namely the one corresponding to the first reliable detection of change. The approach is showcased in a case study, that is the choice of a SHM system for a navigation lock subjected to changing load.
(Less)
- author
- Marsili, Francesca ; Iannacone, Leandro LU and Kessler, Sylvia
- organization
- publishing date
- 2024
- type
- Contribution to conference
- publication status
- published
- subject
- keywords
- Cost-benefit analysis, Linear Bayesian Filter, Structural Health Monitoring, Value of Information
- conference name
- 11th European Workshop on Structural Health Monitoring, EWSHM 2024
- conference location
- Potsdam, Germany
- conference dates
- 2024-06-10 - 2024-06-13
- external identifiers
-
- scopus:85202546627
- DOI
- 10.58286/29575
- language
- English
- LU publication?
- yes
- id
- 390cdde9-56eb-48af-b1c8-4237d8e37254
- date added to LUP
- 2025-01-15 13:40:45
- date last changed
- 2025-04-04 14:32:38
@misc{390cdde9-56eb-48af-b1c8-4237d8e37254,
abstract = {{<p>The expected monetary value is a criterion for assessing the Value of Structural Health Monitoring (SHM) and supporting the associated decision making. Its calculation can be challenging, as it requires considering all possible outcomes of the SHM system. In this article a new approach to cost-benefit analysis for SHM systems is proposed, which is based on an estimate of the capability of the SHM system to detect changes in structural parameters. By applying the linear Bayesian filter for parameter identification, it is possible to predict, considering prior knowledge of the unchanged structure, the minimum change in a structural parameter that the SHM system can detect with a given reliability. This prediction simplifies the calculation of the expected monetary value, which is based on a single measurement, namely the one corresponding to the first reliable detection of change. The approach is showcased in a case study, that is the choice of a SHM system for a navigation lock subjected to changing load.</p>}},
author = {{Marsili, Francesca and Iannacone, Leandro and Kessler, Sylvia}},
keywords = {{Cost-benefit analysis; Linear Bayesian Filter; Structural Health Monitoring; Value of Information}},
language = {{eng}},
title = {{Cost-benefit analysis for SHM systems based on minimum detectable parameter change}},
url = {{http://dx.doi.org/10.58286/29575}},
doi = {{10.58286/29575}},
year = {{2024}},
}