BIM oriented applications of structural health monitoring based on magnified digital image correlation point-clouds

Abstract

Three-dimensional Digital Image Correlation (3D-DIC) gained popularity as a line-of-sight, high-resolution Structural Health Monitoring (SHM) technique. Nonetheless, 3D-DIC is still impractical when the natural frequencies and the mode shapes of the structure have to be correlated. Recently, the integration of Motion Magnification (MM) as a pre-processing tool for 3D-DIC has been explored to enhance the capabilities of DIC for structural analysis and defined a Magnified DIC (M-DIC) workflow. However, such applications do not take full advantage of the potential of the 3D-DIC to produce point-clouds informed of the dynamic properties of a structure. Hence, the definition of a straightforward workflow to manage 3D-DIC point-clouds for real-world applications should be addressed. Moreover, the large amount of raw output coming from MM and 3D-DIC combination requires appropriate tools to systematically visualise and analyse the data. In this context, Building Information Modelling (BIM) has emerged as a robust repository as well as a data management tool to store and exchange data related to the built environment, including long sequences of SHM data. This research proposes a novel Cloud to Model (C2M) tool called M-DIC2BIM that translates the Scalar Fields (SFs) of the M-DIC point-cloud directly into the faces of the BIM model instances. As a case study, a three-storey aluminium frame structure excited with broadband noise is used to validate the developed methodology. The comparison among undamaged and damaged configurations tests made the evolution of the frame's Operational Deflection Shapes (ODSs) computable by quantifying structural changes in its global and local behaviour and mapping them onto the BIM instances.