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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

  1. Acierno, M., Cursi, S., Simeone, D., & Fiorani, D. (2017). Architectural heritage knowledge modelling: An ontology-based framework for conservation process. Journal of Cultural Heritage, 24, 124–133. DOI: 10.1016/j.culher.2016.09.010
  2. Antonino Giuffré. (1993). Sicurezza e conservazione dei centri storici. Il caso Ortigia. (Editori Laterza (ed.)).
  3. Antonino Giuffré, Carocci, C. F., & Tocci, C. (2010). Leggendo il libro delle antiche architetture. Aspetti statici del restauro. Saggi 1985-1997. (Gangemi Editore (ed.))
  4. Barontini, A., Alarcon, C., Sousa, H. S., Oliveira, D. V., Masciotta, M. G., & Azenha, M. (2022). Development and Demonstration of an HBIM Framework for the Preventive Conservation of Cultural Heritage. International Journal of Architectural Heritage, 16(10), 1451–1473. DOI: 10.1080/15583058.2021.1894502
  5. Beetz, J., Van Leeuwen, J., & De Vries, B. (2009). IfcOWL: A case of transforming EXPRESS schemas into ontologies. Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM, 23(1), 89–101. DOI: 10.1017/S0890060409000122
  6. Berlo, L. Van, Krijnen, T., Tauscher, H., Liebich, T., & Kranenburg, A. Van. (2020, April). Future of the Industry Foundation Classes : towards IFC 5. Proceedings of the 38th International Conference of CIB W78, Luxembourg, 13-15 October, April 2020, 123–137. http://itc.scix.net/paper/w78-2021-paper-013
  7. Biagini, C., Capone, P., Donato, V., & Facchini, N. (2016). Towards the BIM implementation for historical building restoration sites. Automation in Construction, 71, 74–86. DOI: 10.1016/j.autcon.2016.03.003
  8. Bonduel, M. (2021). A Framework for a Linked Data-based Heritage BIM. (KU Leuven university) https://kuleuven.limo.libis.be/discovery/fulldisplay?docid=lirias3416395&context=SearchWebhook&vid=32KUL_KUL:Lirias&search_scope=lirias_profile&tab=LIRIAS&adaptor=SearchWebhook&lang=en
  9. Cotella, V. A. (2023). From 3D point clouds to HBIM: Application of Artificial Intelligence in Cultural Heritage. Automation in Construction, 152(December 2022), 104936. DOI: 10.1016/j.autcon.2023.104936
  10. Crofts, N., Doerr, M., & Gill, T. (2003). The CIDOC Conceptual Reference Model: A Standard for Communicating Cultural Content. Cultivate Interactive, 9. http://cidoc.ics.forth.gr/docs/martin_a_2003_comm_cul_cont.htm
  11. Cursi, S., Martinelli, L., Paraciani, N., Calcerano, F., & Gigliarelli, E. (2022). Linking external knowledge to heritage BIM. Automation in Construction, 141(June). DOI: 10.1016/j.autcon.2022.104444
  12. Donkers, A. J. A., Yang, D., De Vries, B., & Baken, N. (2021). Building Performance Ontology. https://alexdonkers.github.io/bop/index.html
  13. Donkers, A., Yang, D., Vries, B. de, & Baken, N. (2023). A Visual Support Tool for Decision-Making over Federated Building Information. Computer-Aided Architectural Design. Interconnections: Co-Computing Beyond Boundaries. https://link.springer.com/chapter/10.1007/978-3-031-37189-9_32
  14. Gigliarelli, E., Calcerano, F., Calvano, M., Ruperto, F., Ruperto -Sapienza, F., Sacco -Studio Arcrea, M., & Cessari, L. (2017). Integrated numerical analysis and Building Information Modeling for Cultural Heritage. https://www.researchgate.net/publication/314371832
  15. Hamdan, A. H., Bonduel, M., & Scherer, R. J. (2019). An ontological model for the representation of damage to constructions. CEUR Workshop Proceedings, 2389, 64–77. https://ceur-ws.org/Vol-2389/05paper.pdf
  16. ICOMOS, I. S. C. F. A. A. O. S. O. A. H. (2005). Recommendations For The Analysis, Conservation And Structural Restoration Of Architectural Heritage. https://www.icomos.org/images/DOCUMENTS/Charters/structures_e.pdf
  17. Kouis, D., & Giannakopoulos, G. (2014). Incorporate Cultural Artifacts Conservation Documentation to Information Exchange Standards – The DOC-CULTURE Case. Procedia - Social and Behavioral Sciences, 147, 495–504. DOI: 10.1016/j.sbspro.2014.07.144
  18. Maurice Murphy, Dublin, E. M., & Pavia, S. (2009). Historic building information modelling (HBIM). Structural Survey, 34(1), 1–5. DOI: 10.1108/02630800910985108/full/html
  19. Norme Tecniche per le Costruzioni, (2018).
  20. Moyano, J., Gil-Arizón, I., Nieto-Julián, J. E., & Marín-García, D. (2022). Analysis and management of structural deformations through parametric models and HBIM workflow in architectural heritage. Journal of Building Engineering, 45, 103274. DOI: 10.1016/j.jobe.2021.103274
  21. Pauwels, P. (2018). Building Element Ontology. https://pi.pauwel.be/voc/buildingelement/index-en.html
  22. Piselli, C., Guastaveglia, A., Romanelli, J., Cotana, F., & Pisello, A. L. (2020). Facility energy management application of HBIM for historical low-carbon communities: Design, modelling and operation control of geothermal energy retrofit in a real Italian case study. Energies, 13(23). DOI: 10.3390/en13236338
  23. Pocobelli, D. P., Boehm, J., Bryan, P., Still, J., & Grau-Bové, J. (2018). BIM for heritage science: a review. Heritage Science, 6(1), 23–26. DOI: 10.1186/s40494-018-0191-4
  24. Poveda-Villalón, M., & Chávez-Feria, S. (2020). Material Properties Ontology. http://bimerr.iot.linkeddata.es/def/material-properties#
  25. Rasmussen, M. H., Lefrançois, M., Schneider, G. F., & Pauwels, P. (2020). BOT: The building topology ontology of the W3C linked building data group. Semantic Web, 12(1), 143–161. DOI: 10.3233/SW-200385
  26. Ursini, A., Grazzini, A., Matrone, F., & Zerbinatti, M. (2022). From scan-to-BIM to a structural finite elements model of built heritage for dynamic simulation. Automation in Construction, 142(January 2021), 104518. DOI: 10.1016/j.autcon.2022.104518
  27. Vanin, F., Zaganelli, D., Penna, A., & Beyer, K. (2017). Estimates for the stiffness, strength and drift capacity of stone masonry walls based on 123 quasi-static cyclic tests reported in the literature. Bulletin of Earthquake Engineering, 15(12), 5435–5479. DOI: 10.1007/s10518-017-0188-5
  28. Veron, P., Messaoudi, T., & Luca, L. De. (2015). Towards an Ontology for Annotating Degradation Phenomena. 7–10. https://hal.science/hal-01408945
  29. Volk, R., Stengel, J., & Schultmann, F. (2014). Building Information Modeling (BIM) for existing buildings - Literature review and future needs. Automation in Construction, 38, 109–127. DOI: 10.1016/j.autcon.2013.10.023
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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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