AbstractA digital twin (DT) can be defined as a multiphysics, multiscale model in which a digital model, such as a building information model (BIM), is updated based on data obtained from a physical system, such as sensor data, results from probabilistic simulations, and material/structural models. This study describes sensor data integration within a BIM as the first critical step toward the implementation of DTs to support structural health monitoring (SHM). In particular, the study defines a methodological approach used to integrate the as-built geometry of existing buildings, as well as their material properties and sensor data into a digital model to assist in accessing sensor data to assess a building’s structural performance. A mass-timber structural system consisting of post-tensioned cross-laminated timber (CLT) self-centering shear walls at the George W. Peavy Forest Science Center (“Peavy Hall”) at Oregon State University was used as a case study to test the proposed approach. The BIM of the shear walls was developed using a Scan-to-BIM approach by converting light detection and ranging point clouds into a BIM. Sensors in the building recorded environmental and structural parameters influencing the long-term performance of the shear walls. Measurands included relative humidity, air and wood temperature, wood moisture content, displacements, and deformations of shear walls. The precise placement of these sensors and the possibility to associate the measured parameters of these entities within a BIM is hypothesized to assist with data management by adding a spatial element to data and analysis results. In addition, the integration into the IFC-BIM platform of a material- and phenomena-specific warning tool allows to promptly identify areas of concern in the monitored building. This can support facility managers in planning inspection and maintenance activities and eventually could lead to the prolonged service life of a building.

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