A digital twin framework for civil engineering structures

• URL: http://arxiv.org/abs/2308.01445v2
• Date: Tue, 31 Oct 2023 15:05:15 GMT
• Title: A digital twin framework for civil engineering structures
• Authors: Matteo Torzoni and Marco Tezzele and Stefano Mariani and Andrea Manzoni and Karen E. Willcox
• Abstract summary: The digital twin concept represents an appealing opportunity to advance condition-based and predictive maintenance paradigms. This work proposes a predictive digital twin approach to the health monitoring, maintenance, and management planning of civil engineering structures.
• Score: 0.6249768559720122
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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