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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.00021 |
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| _version_ | 1866909395249528832 |
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| author | Kamariotis, Antonios Chatzi, Eleni Straub, Daniel Dervilis, Nikolaos Goebel, Kai Hughes, Aidan J. Lombaert, Geert Papadimitriou, Costas Papakonstantinou, Konstantinos G. Pozzi, Matteo Todd, Michael Worden, Keith |
| author_facet | Kamariotis, Antonios Chatzi, Eleni Straub, Daniel Dervilis, Nikolaos Goebel, Kai Hughes, Aidan J. Lombaert, Geert Papadimitriou, Costas Papakonstantinou, Konstantinos G. Pozzi, Matteo Todd, Michael Worden, Keith |
| contents | 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 management and 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 (PHM), and reliability communities, and deliver a roadmap towards monitoring-based decision support. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00021 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Monitoring-Supported Value Generation for Managing Structures and Infrastructure Systems Kamariotis, Antonios Chatzi, Eleni Straub, Daniel Dervilis, Nikolaos Goebel, Kai Hughes, Aidan J. Lombaert, Geert Papadimitriou, Costas Papakonstantinou, Konstantinos G. Pozzi, Matteo Todd, Michael Worden, Keith Computers and Society 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 management and 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 (PHM), and reliability communities, and deliver a roadmap towards monitoring-based decision support. |
| title | Monitoring-Supported Value Generation for Managing Structures and Infrastructure Systems |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2402.00021 |