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HBIM for supporting the diagnosis of historical buildings: case study of the Master Gate of San Francisco in Portugal

Identifiers
URI: http://hdl.handle.net/20.500.12251/2968
View/Open: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132897789&doi=10.1016%2fj.autcon.2022.104453&partnerID=40&md5=eb795fb84a040977b19c53add29a592b
ISSN: 0926-5805
DOI: 10.1016/j.autcon.2022.104453
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Author
García Gago, Jesús María; Sánchez Aparicio, Luis Javier; Soilán, M.; González Aguilera, Diego
Date
2022
Subject/s

Modelado Histórico de Información para la Construcción (HBIM)

Building Information Modeling (BIM)

Nube de puntos

Portugal

Edificación militar

Inteligencia Artificial

Unesco Subject/s

1203.09 Diseño Con Ayuda del Ordenador

2501.21 Simulación Numérica

3305.34 Topografía de la Edificación

Abstract

This paper aims at developing a Historical Building Information Modelling methodology for supporting the diagnosis phase in historical constructions. To this end, the work evaluates the capacity of HBIM for integrating all the data generated during the pre-diagnosis and previous tests, including the data coming from point cloud clustering methods. According to this, we propose different families with low Level of Detail (LoD) and high Level of Information (LoI), including strategies for integrating the data of point cloud clustering methods. This proposal is applied to a case study in the Fortress of Almeida (Portugal), demonstrating the viability of the approach for the diagnosis of historical constructions. Future works will be focused on improving the integration of the 3D point clouds features by using convex-hull methods as well as integrating the results of clustering approaches based on artificial intelligence. © 2022 The Authors

This paper aims at developing a Historical Building Information Modelling methodology for supporting the diagnosis phase in historical constructions. To this end, the work evaluates the capacity of HBIM for integrating all the data generated during the pre-diagnosis and previous tests, including the data coming from point cloud clustering methods. According to this, we propose different families with low Level of Detail (LoD) and high Level of Information (LoI), including strategies for integrating the data of point cloud clustering methods. This proposal is applied to a case study in the Fortress of Almeida (Portugal), demonstrating the viability of the approach for the diagnosis of historical constructions. Future works will be focused on improving the integration of the 3D point clouds features by using convex-hull methods as well as integrating the results of clustering approaches based on artificial intelligence. © 2022 The Authors

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