HBIM for supporting the diagnosis of historical buildings: case study of the Master Gate of San Francisco in Portugal
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2022Materia/s
Modelado Histórico de Información para la Construcción (HBIM)
Materia/s Unesco
Resumen
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




