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Open AccessArticle

An Efficient Pipeline to Obtain 3D Model for HBIM and Structural Analysis Purposes from 3D Point Clouds

Polytechnic of Bari, via E. Orabona 4, 70125 Bari, Italy
AESEI Spin-off - Polytechnic of Bari, via S. Eligio 1/L, 74015 Martina Franca (Taranto), Italy
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(4), 1235;
Received: 2 January 2020 / Revised: 7 February 2020 / Accepted: 10 February 2020 / Published: 12 February 2020
The aim of this work is to identify an efficient pipeline in order to build HBIM (heritage building information modelling) and create digital models to be used in structural analysis. To build accurate 3D models it is first necessary to perform a geomatics survey. This means performing a survey with active or passive sensors and, subsequently, accomplishing adequate post-processing of the data. In this way, it is possible to obtain a 3D point cloud of the structure under investigation. The next step, known as “scan-to-BIM (building information modelling)”, has led to the creation of an appropriate methodology that involved the use of Rhinoceros software and a few tools developed within this environment. Once the 3D model is obtained, the last step is the implementation of the structure in FEM (finite element method) and/or in HBIM software. In this paper, two case studies involving structures belonging to the cultural heritage (CH) environment are analysed: a historical church and a masonry bridge. In particular, for both case studies, the different phases were described involving the construction of the point cloud and, subsequently, the construction of a 3D model. This model is suitable both for structural analysis and for the parameterization of rheological and geometric information of each single element of the structure. View Full-Text
Keywords: scan-to-BIM; point cloud; HBIM; FEM; Rhinoceros scan-to-BIM; point cloud; HBIM; FEM; Rhinoceros
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Pepe, M.; Costantino, D.; Restuccia Garofalo, A. An Efficient Pipeline to Obtain 3D Model for HBIM and Structural Analysis Purposes from 3D Point Clouds. Appl. Sci. 2020, 10, 1235.

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