- CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
- A cura di Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi
Linked Data for the Categorization of Failures Mechanisms in Existing Unreinforced Masonry Buildings
- Maria Laura Leonardi
- Stefano Cursi
- Daniel V. Oliveira
- Miguel Azenha
- Elena Gigliarelli
- © 2023 Author(s) |
- CC BY-NC 4.0
- DOI: 10.36253/979-12-215-0289-3.78
Assessing the structural integrity of unreinforced masonry structures is a complex and time-consuming process that necessitates the knowledge of various experts and meticulous cross-referencing of diverse data to achieve a comprehensive understanding of the building. In recent years, the Architecture and Construction Industry has witnessed a digital transformation, largely driven by Building Information Modeling (BIM). BIM has proven immensely valuable in the conservation of historic buildings. However, while it excels in new construction projects, its full potential is not fully realized when dealing with existing structures. A clear example of this limitation can be observed in the Industry Foundation Classes (IFC) format, which lacks instances necessary for accurately representing existing building features. This research contribution aims to advance the process of semantic enrichment of BIM for existing buildings, building upon findings from existing literature. Leveraging the Linked Data Approach and utilizing both existing ontologies and newly proposed domain ontologies, the objective is to facilitate the identification of vulnerabilities and potential local failure mechanisms. The geometric information of the building is represented in the IFC STEP format and enriched semantically by establishing new relationships between classes that are not present in the standard IFC. This approach is applied to a case study in the historical center of Castelnuovo di Porto, Italy. The results of this work demonstrate how the proposed model, enhancing the BIM representation of existing buildings and enabling better identification of potential weaknesses, contributes to improved preservation and seismic resilience of historic structures
- Keywords:
- BIM,
- Linked Data,
- Semantic Modeling,
- Historic Constructions,
- Structural Masonry,
University of Minho, Portugal - ORCID: 0000-0003-3659-3955
CNR, National Research Council of Italy, Italy - ORCID: 0000-0002-8499-1459
University of Minho, Portugal - ORCID: 0000-0002-8547-3805
University of Minho, Portugal - ORCID: 0000-0003-1374-9427
CNR, National Research Council of Italy, Italy - ORCID: 0000-0003-1692-5190
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Informazioni sul capitolo
Titolo del capitolo
Linked Data for the Categorization of Failures Mechanisms in Existing Unreinforced Masonry Buildings
Autori
Maria Laura Leonardi, Stefano Cursi, Daniel V. Oliveira, Miguel Azenha, Elena Gigliarelli
DOI
10.36253/979-12-215-0289-3.78
Opera sottoposta a peer review
Anno di pubblicazione
2023
Copyright
© 2023 Author(s)
Licenza d'uso
Licenza dei metadati
Informazioni bibliografiche
Titolo del libro
CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
Sottotitolo del libro
Managing the Digital Transformation of Construction Industry
Curatori
Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi
Opera sottoposta a peer review
Anno di pubblicazione
2023
Copyright
© 2023 Author(s)
Licenza d'uso
Licenza dei metadati
Editore
Firenze University Press
DOI
10.36253/979-12-215-0289-3
eISBN (pdf)
979-12-215-0289-3
eISBN (xml)
979-12-215-0257-2
Collana
Proceedings e report
ISSN della collana
2704-601X
e-ISSN della collana
2704-5846