A parametric Scan-to-FEM framework for the digital twin generation of historic masonry structures

Abstract

Historic masonry buildings are characterised by uniqueness, which is intrinsically present in their building techniques, morphological features, architectural decorations, artworks, etc. From the modelling point of view, the degree of detail reached on transforming discrete digital representations of historic buildings, e.g., point clouds, into 3D objects and elements strongly depends on the final purpose of the project. For instance, structural engineers involved in the conservation process of built heritage aim to represent the structural system rigorously, neglecting architectural decorations and other details. Following this principle, the software industry is focusing on the definition of a parametric modelling approach, which allows performing the transition from half-raw survey data (point clouds) to geometrical entities in nearly no time. In this paper, a novel parametric Scan-to-FEM approach suitable for architectural heritage is presented. The proposed strategy uses the Generative Programming paradigm implementing a modelling framework into a visual programming environment. Such an approach starts from the 3D survey of the case-study structure and culminates with the definition of a detailed finite element model that can be exploited to predict future scenarios. This approach is appropriate for architectural heritage characterised by symmetries, repetition of modules and architectural orders, making the Scan-to-FEM transition fast and efficient. A Portuguese monument is adopted as a pilot case to validate the proposed procedure. In order to obtain a proper digital twin of this structure, the generated parametric model is imported into an FE environment and then calibrated via an inverse dynamic problem, using as reference metrics the modal properties identified from field acceleration data recorded before and after a retrofitting intervention. After assessing the effectiveness of the strengthening measures, the digital twin ability of reproducing past and future damage scenarios of the church is validated through nonlinear static analyses.