The research proposes a novel and expeditious approach starting from direct and SfM (Structure from Motion) surveying up to H-BIM to provide a supporting tool to the knowledge of traditional bell towers, also useful for the seismic vulnerability assessment. The research draws on the possibility to enrich the H-BIM model with a semantic layer that takes into account the vulnerability indicators as defined in 2011 Italian Guidelines for the assessment and reduction of seismic risk of cultural heritage. Starting from 3D data survey, passing through the historical and constructive analysis, and the semantic classification of parts, the workflow foresaw the setting up of a semantic layer creating parameters to be assigned to each architectural component of the building and according an expeditious check-list which focuses on empirical knowledge of bell towers. This kind of information stored in the model could be very useful in situations that require particularly timely decision-making, such as in the case of calamitous events.

This paper addresses the challenge of digitally reconstructing ruined architectural sites and retracing their history, in order to virtually recompose their geometrical, stylistic and material integrity. To this end, the research team analyzed the ruins of the church of Santa Maria de Monasterio Albo, located in the ancient village of Misterbianco (Sicily) and destroyed (together with the entire hamlet) by the 1669 eruption of Mount Etna. In the last years, some excavation campaigns brought the church to the light, unveiling the remains of the main portal and six altars, which are one of the most remarkable examples of Mannerist art in eastern Sicily. This research aimed to three-dimensional (3D) reconstruct both the altars and the portal, ideally reviving their original 17th century configuration. This goal was achieved through an in-depth archival research (documents dating back to the years between 1300 and 1666 were consulted), an analysis of Classic and Renaissance treatises, and two integrated digital survey campaigns (laser scans and photogrammetry). The outcome is represented by the 3D models of the seven artifacts, which include surviving parts reconstructed as photogrammetric meshes, several fragments were placed in their likely early location through a virtual anastylosis, and NURBS (Non Uniform Rational Basis-Splines) surfaces (recreating the no longer existing elements). The latter were 3D modelled based on the treatises (which provided information on the correct proportioning) or in analogy with other coeval similar artifacts. Overall, the digital reconstruction was based on the ethical principles of transparency of the intervention, recognition of non-original additions and distinction between evidence and hypothesis, according to the London Charter and the Seville Principles. The experimentation provides a valid support for possible interventions in the real world and is the starting point to develop a digital archive of the site, which would make the different accuracy levels the reconstruction explicit.Highlights:3D virtual reconstruction is effective to visualize and bring back to life ruined architectural artefacts.Information about the artefacts original appearance was harvested through digital survey campaigns, archival documents, and comparisons with iconographic sources and coeval buildings.The 3D reconstruction follows ethical principles of transparency and combines photogrammetric meshes (partly relocated through a virtual anastylosis) and NURBS surfaces.

This chapter combines traditional artificial intelligence (AI) concepts, i.e., computational ontologies, with more recent trends, i.e., deep learning for content-based semantic retrieval in Cultural Heritage. More specifically, the proposed AI-empowered system employs computational ontologies for modelling photographs of religious historical buildings. The ontology, besides supporting data-modelling and concept-level annotation, guides a learning process – implemented through Convolutional Neural Network (CNN) – for automated image categorization and retrieval. The whole system has been tested on the ruins of the church of Santa Maria delle Grazie in Misterbianco, Catania, Italy, showing satisfactory performance. Request access from your librarian to read this chapter's full text.

In recent years, laser scanning and other survey techniques have become popular for documenting and preserving architectural and structural heritage, including masonry bridges. These bridges require careful monitoring to ensure proper structural integrity analyses. However, analysing large datasets of complex point clouds can be challenging and time consuming. This research introduces an automated method to quickly assess the geometric characteristics of vault of bridges by means of a simple algorithm purposely developed for this application. The developed methodology was implemented in MATLAB. The main geometric features, such as length, arrow, and span, are extracted, and conditional algorithms classify the vault types based on specific thresholds. The methodology is tested on surveyed masonry bridges coming from a case study: the historical Circumetnea railway bridges in Sicily, Italy. A digital survey campaign was conducted using various survey techniques such as laser scanning and photogrammetry to obtain the vault point clouds. This research represents a step forward from previous methodologies by efficiently segmenting and analysing vaults from point clouds, enabling accurate classification and geometric analysis.