Metric survey and bim technologies to record decay conditions

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Abstract

The paper proposes a method able to describe, classify and organize information assets concerned with Architectural Heritage, through the use of integrated survey procedures, mainly based on Terrestrial Laser Scanner (TLS). The point clouds are then imported into the Building Information Modeling (BIM) software to start with the modeling phase. With regard to this issue, in the last period Building Information Modeling is emerging as the most reliable method to manage architectural design and building processes. Literature supplies both theoretical approaches and several practical applications. However, very little researches are devoted to BIM applied to historical architecture, even if some initial results indicate the actual HBIM (Historic/Heritage BIM) as a possible instrument for the design of an intervention aimed at the conservation of the Cultural Heritage. The focus of the research is the creation of parametric objects representing the preservation status of materials and building components: 3D modeling of decays in the BIM platform ensures to enrich the related database with graphic, geometric and alphanumeric data that can be effectively used to design and manage future interventions. The added value consists in its capability to associate new parameters that describe both the state of conservation of the materials and the detailed description of interventions needed to restore the building. The analyzed case study belongs to Ferrovie dello Stato (the main Italian Railways company) and it is part of the maintenance area, which was originally constituted by a roundhouse containing 51 sheltered railroad tracks and two big sheds.

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APA

Turco, M. L., Mattone, M., & Rinaudo, F. (2017). Metric survey and bim technologies to record decay conditions. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 42, pp. 261–268). International Society for Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprs-Archives-XLII-5-W1-261-2017

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