Bridge Management Based on Bayesian Decision Analysis with Sustainability Considerations
Giordano, Pier Francesco ; Posani, Magda ; Galimshina, Alina and Limongelli, Maria Pina LU
(2025)
11th International Conference on Experimental Vibration Analysis for Civil Engineering Structures, EVACES 2025
In Lecture Notes in Civil Engineering
674 LNCE.
p.252-261
- Abstract
With increasing climate-induced stresses and ageing infrastructure worldwide, integrating sustainability into infrastructure management is essential. Structural Health Monitoring (SHM) systems provide valuable insights for assessing and maintaining the safety and integrity of ageing structures, but their implementation entails significant costs. Resources allocated to SHM could alternatively support other interventions, such as retrofitting, demolition, or bridge replacement, each with distinct implications for immediate costs, structural safety, and long-term sustainability. This paper presents a Bayesian decision analysis framework for optimizing management decisions in the context of sustainability. The framework quantifies the... (More)
With increasing climate-induced stresses and ageing infrastructure worldwide, integrating sustainability into infrastructure management is essential. Structural Health Monitoring (SHM) systems provide valuable insights for assessing and maintaining the safety and integrity of ageing structures, but their implementation entails significant costs. Resources allocated to SHM could alternatively support other interventions, such as retrofitting, demolition, or bridge replacement, each with distinct implications for immediate costs, structural safety, and long-term sustainability. This paper presents a Bayesian decision analysis framework for optimizing management decisions in the context of sustainability. The framework quantifies the expected costs of various management actions, including SHM adoption, by balancing the potential benefits of monitoring data against implementation costs. It is applied to a case study on bridge management during a flood event, where scour threatens bridge foundations. The results highlight the potential of Bayesian analysis in supporting rational decision-making while integrating environmental sustainability considerations into infrastructure management.
(Less)
- author
- Giordano, Pier Francesco
; Posani, Magda
; Galimshina, Alina
and Limongelli, Maria Pina
LU
- organization
- publishing date
- 2025
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Bayesian Decision Theory, Bridge Management, CO emission, Structural Health Monitoring, Sustainability, Value of Information
- host publication
- Experimental Vibration Analysis for Civil Engineering Structures, EVACES 2025 - Volume 1
- series title
- Lecture Notes in Civil Engineering
- editor
- Cunha, Álvaro and Caetano, Elsa
- volume
- 674 LNCE
- pages
- 10 pages
- publisher
- Springer Science and Business Media B.V.
- conference name
- 11th International Conference on Experimental Vibration Analysis for Civil Engineering Structures, EVACES 2025
- conference location
- Porto, Portugal
- conference dates
- 2025-07-02 - 2025-07-04
- external identifiers
-
- scopus:105018074206
- ISSN
- 2366-2557
- 2366-2565
- ISBN
- 9783031961090
- DOI
- 10.1007/978-3-031-96110-6_23
- language
- English
- LU publication?
- yes
- id
- 213ad264-bf46-4d35-b22e-086e9713ece4
- date added to LUP
- 2025-11-28 12:02:04
- date last changed
- 2025-11-28 12:02:33
@inproceedings{213ad264-bf46-4d35-b22e-086e9713ece4,
abstract = {{<p>With increasing climate-induced stresses and ageing infrastructure worldwide, integrating sustainability into infrastructure management is essential. Structural Health Monitoring (SHM) systems provide valuable insights for assessing and maintaining the safety and integrity of ageing structures, but their implementation entails significant costs. Resources allocated to SHM could alternatively support other interventions, such as retrofitting, demolition, or bridge replacement, each with distinct implications for immediate costs, structural safety, and long-term sustainability. This paper presents a Bayesian decision analysis framework for optimizing management decisions in the context of sustainability. The framework quantifies the expected costs of various management actions, including SHM adoption, by balancing the potential benefits of monitoring data against implementation costs. It is applied to a case study on bridge management during a flood event, where scour threatens bridge foundations. The results highlight the potential of Bayesian analysis in supporting rational decision-making while integrating environmental sustainability considerations into infrastructure management.</p>}},
author = {{Giordano, Pier Francesco and Posani, Magda and Galimshina, Alina and Limongelli, Maria Pina}},
booktitle = {{Experimental Vibration Analysis for Civil Engineering Structures, EVACES 2025 - Volume 1}},
editor = {{Cunha, Álvaro and Caetano, Elsa}},
isbn = {{9783031961090}},
issn = {{2366-2557}},
keywords = {{Bayesian Decision Theory; Bridge Management; CO emission; Structural Health Monitoring; Sustainability; Value of Information}},
language = {{eng}},
pages = {{252--261}},
publisher = {{Springer Science and Business Media B.V.}},
series = {{Lecture Notes in Civil Engineering}},
title = {{Bridge Management Based on Bayesian Decision Analysis with Sustainability Considerations}},
url = {{http://dx.doi.org/10.1007/978-3-031-96110-6_23}},
doi = {{10.1007/978-3-031-96110-6_23}},
volume = {{674 LNCE}},
year = {{2025}},
}