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This paper explores the application of Digital Twins (DTs) for supporting the structural health monitoring (SHM) of timber buildings, with a particular emphasis on historical timber structures. It reviews technologies and methods for DT creation, examining how geospatial data and Building Information Modeling (BIM) serves as foundational elements in developing digital twins. The paper discusses integrating these elements with various data and models to enhance SHM tasks, detailing actionable DT approaches for documenting, analyzing, simulating and predicting structural behavior. The study highlights that while high-fidelity geometric models are well developed, their application for advanced SHM tasks remains limited. Integration of sensor data into BIM frameworks is promising but still faces challenges such as limited automation and difficulties in handling real-time and long-term data. Additionally, the paper notes that the implementation of DTs for the simulation of the effects of hygrothermal phenomena on the long-term behavior and durability of timber structures is underexplored. Multi-dimensional BIM models and machine and deep learning techniques in data-driven, physics-based models are promising approaches for timber cultural heritage management and beyond.
Riggio et al. (Thu,) studied this question.