Architectural Heritage Imaging: When Graphical Science Meets Model Theory

Abstract

In the field of Architectural Heritage (AH), the Representative Model – that is an Interpretative Model by its very nature – is the core of the process of study, knowledge, understanding, communication, and valorisation (De Luca & Russo, 2021). The use of the interpretative model recalls the so-called Model Theory (Hodges, 2020), and in particular in the AH field the representative model acts in the visual dimension. This model is a cultural product, synthesis of the apparent oppositions between tangible and intangible, signifier and meaning, reality and interpretation, picture and image, history and memory.AH current lines of study suggest combining Model Theory with Visual Sciences (Bertoline, 1998; Mitchell, 2015), and therefore with the Graphical Science (Cardone, 2017; Cicalò, 2020), favouring the broadening of the traditional fields of representation, drawing, geometry, and history. It fosters the understanding of the heuristic role of the architectural interpretative model: The process of drawing and modelling becomes a methodology of analysis, in the sense of a process of visual computing of spatial, historical, and cultural characteristics of built heritage; In this way the model is a sort of multi-dimensional vector that reproduces significant aspects of the architectural phenomena, and thus facilitates its study, understanding and communication (Ware, 2000; Brusaporci, 2015). This kind of model has characteristics and visual and meta-medial declinations, which differ according to the individualities of the represented architecture, the purposes of the visualisations, the user profiles. At the same time, the discourse on the model is intrinsically linked to its images, its visualizations according to ontologies that, in the case of the architectural sphere, are characterised by spaces, cultures, building systems, materials, architectural orders, historical transformations, etc.DOI: https://doi.org/10.20365/disegnarecon.31.2023.ed