TY - JOUR
T1 - Monitoring-supported value generation for managing structures and infrastructure systems
AU - Kamariotis, Antonios
AU - Chatzi, Eleni
AU - Straub, Daniel
AU - Dervilis, Nikolaos
AU - Goebel, Kai
AU - Hughes, Aidan J.
AU - Lombaert, Geert
AU - Papadimitriou, Costas
AU - Papakonstantinou, Konstantinos G.
AU - Pozzi, Matteo
AU - Todd, Michael
AU - Worden, Keith
N1 - Publisher Copyright:
© The Author(s), 2024. Published by Cambridge University Press.
PY - 2024/11/4
Y1 - 2024/11/4
N2 - To maximize its value, the design, development and implementation of structural health monitoring (SHM) should focus on its role in facilitating decision support. In this position paper, we offer perspectives on the synergy between SHM and decision-making. We propose a classification of SHM use cases aligning with various dimensions that are closely linked to the respective decision contexts. The types of decisions that have to be supported by the SHM system within these settings are discussed along with the corresponding challenges. We provide an overview of different classes of models that are required for integrating SHM in the decision-making process to support the operation and maintenance of structures and infrastructure systems. Fundamental decision-theoretic principles and state-of-the-art methods for optimizing maintenance and operational decision-making under uncertainty are briefly discussed. Finally, we offer a viewpoint on the appropriate course of action for quantifying, validating, and maximizing the added value generated by SHM. This work aspires to synthesize the different perspectives of the SHM, Prognostic Health Management, and reliability communities, and provide directions to researchers and practitioners working towards more pervasive monitoring-based decision-support.
AB - To maximize its value, the design, development and implementation of structural health monitoring (SHM) should focus on its role in facilitating decision support. In this position paper, we offer perspectives on the synergy between SHM and decision-making. We propose a classification of SHM use cases aligning with various dimensions that are closely linked to the respective decision contexts. The types of decisions that have to be supported by the SHM system within these settings are discussed along with the corresponding challenges. We provide an overview of different classes of models that are required for integrating SHM in the decision-making process to support the operation and maintenance of structures and infrastructure systems. Fundamental decision-theoretic principles and state-of-the-art methods for optimizing maintenance and operational decision-making under uncertainty are briefly discussed. Finally, we offer a viewpoint on the appropriate course of action for quantifying, validating, and maximizing the added value generated by SHM. This work aspires to synthesize the different perspectives of the SHM, Prognostic Health Management, and reliability communities, and provide directions to researchers and practitioners working towards more pervasive monitoring-based decision-support.
KW - decision support
KW - maintenance planning
KW - population-based SHM
KW - SHM
KW - value of information
KW - verification & validation
UR - http://www.scopus.com/inward/record.url?scp=85210184572&partnerID=8YFLogxK
U2 - 10.1017/dce.2024.24
DO - 10.1017/dce.2024.24
M3 - Review article
AN - SCOPUS:85210184572
SN - 2632-6736
VL - 5
JO - Data-Centric Engineering
JF - Data-Centric Engineering
M1 - e27
ER -