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Data interoperability between element classifications and semantics of historic buildings. Case study of the Duomo of Molfetta (Italy) Enrique Nieto-Juliána, Juan Moyanoa, Alessandra Pilib Silvana Brunoc a University of Seville, Department of Graphical Expression and Building Engineering, Universidad de Sevilla, Spain b Department of Architecture, Built environment and Construction Engineering, Politecnico di Milano, Milano, Italy c Department of Civil, Environmental, Land, Construction and Chemistry Engineering, Politecnico di Bari, Italy. Abstract The research deals with the study and implementation of new solutions for the preservation of the Architectural Heritage, covering the phases of auscultation, intervention, and maintenance. A sustainable technological process is applied, based on the collaborative BIM methodology, which constitutes a bulwark for the efficient management of the historic building as it is effective in the interoperability of data from the Heritage-BIM (HBIM) project. In the study and conservation of Heritage, there are many disciplines that are part of the investigation or intervention and that require extracting, sharing and contributing data linked to the historic building. The use of a new collaborative construction process management technology involves implementing design, modeling, planning and maintenance improvement processes. These logistic production benefits will now be transferred to a historical-constructive context. On the other hand, reinforcing the interoperability of the data is decisive for a BIM system to function with full capacity. Classifying allows standardizing and organizing elements based on an established criterion. Assigning classifications to historical elements and systems in an HBIM project, including detected problems or deterioration, is also essential for proper interoperability between architects, archaeologists, historians, engineers. Currently, the BIM environment has classification systems, based on the correct identification of the most common systems in construction. But the context of the historic building is very complex as it is affected by the temporal-evolutionary component and by the great diversity of types within an architectural style. To solve it, the ontology provides us with a computable knowledge base, a representation system through which it is possible to model a domain of knowledge. In this work, a semantic segmentation of the point cloud obtained by TLS is carried out using the Cyclone 3DR point cloud processing software. In the second phase of work, the elements are structured in an HBIM project. From these data, a classification of the elements is executed using the ontology base developed by (Pili, 2023). Element classification covers an export process in IFC format. This workflow will allow the creation of a future universal classification related to Cultural Heritage. For the procedure to be properly implemented, the Duomo di Molfetta (Bari, Italy) has been taken as a case study. Keywords HBIM, Scan-to-BIM, Segmentation, Ontology, Heritage, Preservation References 1. Acierno, M., Cursi, S., Simeone, D., & Fiorani, D. (2017). Architectural heritage knowledge modelling: An ontologybased framework for conservation process. Journal of Cultural Heritage, 24, 124–133. https://doi.org/10.1016/j.culher.2016.09.010 2. Afsari, K., & Eastman, C. M. (2016). A Comparison of Construction Classification Systems Used for Classifying Building Product Models. 52nd ASC Annual International Conference Proceedings, 2001, 1–8. 3. Alshawabkeh, Y., & Baik, A. (2023). Integration of photogrammetry and laser scanning for enhancing scan-to-HBIM modeling of Al Ula heritage site. Heritage Science 2023 11:1, 11(1), 1–17. https://doi.org/10.1186/S40494-023-00997-2 4. Castán, M. Q., & Hernández, L. A. (2022). Metodología de captura y procesado para el desarrollo de una base de datos gráfica 3D del patrimonio arquitectónico. Ge-Conservacion, 21(1), 72–84. https://doi.org/10.37558/GEC.V21I1.1048 5. Croce, V., Caroti, G., Luca, L. De, Jacquot, K., Piemonte, A., & Véron, P. (2021). From the Semantic Point Cloud to Pg. 81
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