1. A digital twin framework for civil engineering structures.
- Author
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Torzoni, Matteo, Tezzele, Marco, Mariani, Stefano, Manzoni, Andrea, and Willcox, Karen E.
- Subjects
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DEEP learning , *DIGITAL twins , *STRUCTURAL engineering , *CIVIL engineering , *CIVIL engineers , *ARTIFICIAL neural networks , *STRUCTURAL health monitoring - Abstract
The digital twin concept represents an appealing opportunity to advance condition-based and predictive maintenance paradigms for civil engineering systems, thus allowing reduced lifecycle costs, increased system safety, and increased system availability. This work proposes a predictive digital twin approach to the health monitoring, maintenance, and management planning of civil engineering structures. The asset-twin coupled dynamical system is encoded employing a probabilistic graphical model, which allows all relevant sources of uncertainty to be taken into account. In particular, the time-repeating observations-to-decisions flow is modeled using a dynamic Bayesian network. Real-time structural health diagnostics are provided by assimilating sensed data with deep learning models. The digital twin state is continually updated in a sequential Bayesian inference fashion. This is then exploited to inform the optimal planning of maintenance and management actions within a dynamic decision-making framework. A preliminary offline phase involves the population of training datasets through a reduced-order numerical model and the computation of a health-dependent control policy. The strategy is assessed on two synthetic case studies, involving a cantilever beam and a railway bridge, demonstrating the dynamic decision-making capabilities of health-aware digital twins. • We present a digital twin framework for the SHM of civil structures. • The asset-twin dynamical system is encoded through a probabilistic graphical model. • Sensor recordings are assimilated with artificial neural networks. • A reduced-order model is employed offline to generate training vibration responses. • Our strategy allows predictive maintenance and management planning of structures. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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