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Book Chapter

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

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,
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Maria Laura Leonardi

University of Minho, Portugal - ORCID: 0000-0003-3659-3955

Stefano Cursi

CNR, National Research Council of Italy, Italy - ORCID: 0000-0002-8499-1459

Daniel V. Oliveira

University of Minho, Portugal - ORCID: 0000-0002-8547-3805

Miguel Azenha

University of Minho, Portugal - ORCID: 0000-0003-1374-9427

Elena Gigliarelli

CNR, National Research Council of Italy, Italy - ORCID: 0000-0003-1692-5190

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  • Publication Year: 2023
  • Pages: 781-790

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  • Publication Year: 2023

Chapter Information

Chapter Title

Linked Data for the Categorization of Failures Mechanisms in Existing Unreinforced Masonry Buildings

Authors

Maria Laura Leonardi, Stefano Cursi, Daniel V. Oliveira, Miguel Azenha, Elena Gigliarelli

DOI

10.36253/979-12-215-0289-3.78

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

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

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

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