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Abstract
In the landscape of contemporary Civil Engineering, Artificial Intelligence (AI) stands as a critical pillar of innovation, fundamentally transforming the operation and maintenance (O&M) of infrastructures systems. The present study focuses on the integration of AI to boost digital twins in addressing the complexities of life-cycle management of infrastructure assets. AIboosted digital twins embody a synthesis of real-time data acquisition, advanced analytics, and predictive modelling, marking a significant departure from traditional O&M approaches that are often reactive and less informed. The methodology employed encapsulates the convergence of data and model twin-driven insights and computational intelligence, using environmental conditions to feed sophisticated probabilistic models and multi-physics simulations. This research specifically investigates the application of these technologies in the context of a case study on floating offshore wind turbines (FOWTs), yet the primary focus is the expansive role of AI-boosted digital twins across Civil Engineering domains. Significant findings from the study reveal the capability of AI-boosted digital twins to identify potential failure modes in structural components, predict the evolution of deterioration, and recommend timely O&M interventions in terms of different actions. In general, the present approach not only enhances the predictive accuracy of structural health assessments, but also optimizes resource allocation and minimizes downtime. By distilling the essence of these digital twins into actionable insights, the research underscores their potential to revolutionize infrastructure management. The implications are vast, heralding a new era of intelligent O&M strategies that promise increased safety, extended service life and cost-effectiveness. The integration of AI-boosted digital twins is posited to become an industry standard, advocating for a shift towards more resilient, adaptive, and intelligent Civil Engineering practices.
Original language | English |
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Title of host publication | The 9th International Conference "Civil Engineering - Science & Practice" |
Subtitle of host publication | GNP 2024 Proceedings |
Editors | Marina Rakočević |
Place of Publication | Kolasin |
Publisher | UNIVERSITY OF MONTENEGRO |
Pages | 23-31 |
Number of pages | 8 |
Edition | 1 |
ISBN (Electronic) | 9788682707363 |
Publication status | Published - 5 Mar 2024 |
Event | The Ninth International Conference on Civil Engineering - Science & Practice: GNP2024 - KOLAŠIN, KOLAŠIN, Montenegro Duration: 5 Mar 2024 → 9 Mar 2024 |
Conference
Conference | The Ninth International Conference on Civil Engineering - Science & Practice |
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Abbreviated title | GNP2024 |
Country/Territory | Montenegro |
City | KOLAŠIN |
Period | 5/03/24 → 9/03/24 |
Bibliographical note
ACKNOWLEDGEMENTSThe MSCA Fellowship via URKI (EP/X022765/1), ROYAL SOCIETY (IES\R1\211087), and COST Action MODENERLANDS (CA20109), are gratefully acknowledged by the authors.
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Dive into the research topics of 'Artificial intelligence-assisted civil engineering: Digital twins for the wind energy infrastructure'. Together they form a unique fingerprint.Projects
- 1 Finished
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Resilient tall wind turbines to enhance Chile's wind energy infrastructure
Baniotopoulos, C. (Principal Investigator)
11/03/22 → 10/03/24
Project: Research Councils