Tata Alessandra

Visual Programming for a Machine Semi-Automatic Process of HBIM Models Geometric Evaluation

This paper presents a semi-automatic methodology for evaluating the geometric reliability of HBIM models through the integration of visual programming techniques within BIM environments. The research addresses the challenge of reconciling the parametric standardization of BIM with the geometric complexity and uniqueness of historical architecture, focusing on the need to quantify deviations between survey data and digital models.

The proposed workflow is based on the comparison between point clouds derived from architectural surveys and corresponding HBIM elements, using a Visual Programming Language (VPL) implemented in Dynamo. As illustrated in the operational pipeline (Fig. 3, p. 798), the method involves the import of point cloud data and model geometries, computation of normal vectors, projection of points onto model surfaces, and calculation of distances to evaluate geometric deviation. These values are then classified according to predefined reliability thresholds, generating a Level of Accuracy (LoA) and automatically updating model parameters within the BIM environment. Visualization outputs (Figs. 7–9, pp. 799–801) enable the identification of low, medium, and high reliability zones through color-coded mapping.

Results demonstrate that the proposed approach significantly reduces the time required for reliability assessment and supports iterative model refinement through semi-automated feedback. The study highlights the potential of integrating algorithmic processes within HBIM workflows to improve transparency, reproducibility, and accuracy in heritage modeling, while emphasizing the interpretative nature of digital reconstruction and the importance of explicitly declaring model reliability.

Prosthetic Visualizations for a Smart Heritage

The development of ICT has favoured the spread of real–time, pervasive and ubiquitous applications. In particular, VR and AR visualizations allow a close interrelation between people, data, environments and objects. Consequently, it is possible to enrich cultural heritage with information by visually superimposing multimedia content, in absolute respect of their physical consistency. In this way, it is possible to create a ‘smart heritage’ dimension that combines the potential of the ‘Phygital’ with the protection and enhance-ment of assets often characterized by important elements of fragility. An important role is played by AI applications, which automatically direct the processes of ‘Interpretation’ and ‘Presentation’ of the heritage. Based on the experience of the 3D reconstruction of the no longer existing Baroque configuration of the Basilica of Collemaggio in L’Aquila, aim of the paper is a theoretical–methodological reflection on the concept of VR / AR / MR for cultural heritage.

Visual Programming for a Machine Semi-Automatic Process of HBIM Models Geometric Evaluation

The topic of the relationship between digital restitutive model and measurement can find important development possibilities in machine procedures, in particular in the Historic Building Information Modeling (HBIM) field. In fact, BIM uses parameterized and pre-defined objects in special 3D libraries articulated according to the architectural components, not corresponding to ideal configurations. Moreover, BIM platforms are limited in modeling deformations, damages, and degradations. The paper investigates the advantages of using visual programming to increase the possibilities given by the BIM software, born for new buildings, by carrying out a semi-automatic assessment of the geometric reliability directly in the BIM environment. In particular, the algorithm compares model’s shapes with the cast of the artifact given by the point cloud, and declares it, by automatically filling a dedicated reliability parameter linked to the BIM model element.

A Proposal of Integration of Point Cloud Semantization and VPL for Architectural Heritage Parametric Modeling

Current architectural survey processes utilize point clouds generated by laser scanning and digital photogrammetry. Increasingly, these surveys produce 3D models, particularly parametric models, in what is known as the “scan to 3D model” or “scan to BIM” process. However, the phases of analysis and classification of architectural elements, segmentation and semantization of point clouds, and semi-automatic modeling remain complex and labor-intensive and require an active role commitment of the scholar or modeler. These steps are usually performed manually, resulting in high subjectivity and low reproducibility. This paper proposes a reproducible workflow that automatically segments point clouds, identifies geometric shapes by comparing them with a library of ideal geometries, and extracts necessary points for modeling through mathematical analysis. The extracted information is then processed using a visual programming algorithm, imported into the VPL environment, and used for automated modeling. Initial results from an ongoing experiment on the automated modeling of vaults using point clouds from surveys are presented.