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Towards Automated BIM and BEM Model Generation using a B-Rep-based Method with Topological Map

Authors :
O. Roman
G. Mazzacca
E. M. Farella
F. Remondino
M. Bassier
G. Agugiaro
Source :
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol X-4-2024, Pp 287-294 (2024)
Publication Year :
2024
Publisher :
Copernicus Publications, 2024.

Abstract

In many countries, recent boosts in the construction and renovation sectors and energy efficiency directives are driving a growing interest in the built environment among designers and maintainers. In this context, customized software solutions tailored for Building Information Modelling (BIM) and Building Energy Modelling (BEM) are proving to be indispensable for optimizing operational efficiency within the Architecture, Engineering, Construction, Owner, and Operator (AECOO) sector and for facilitating the generation of buildings' Digital Twins (DTs). These DTs rely on accurate geometry and ancillary information (semantics, sensors, etc.) to define properties of single elements, enabling crucial simulations in structural conditions or energy needs. However, BIM and BEM model creation and their enrichment with semantic information are highly time-consuming and prone to manual errors. Hence, there is an increasing demand for automatic methods featuring a high level of geometric accuracy to reconstruct building elements, such as walls, floors, and openings captured via 3D reality-based surveying. This paper introduces an automated method for creating Boundary Representation (B-Rep) models from 3D surveying data for the generation of digital building replicas. The method is based on the detection and computation of topological elements from 3D reality-based point clouds. It proves valuable for architectural or design workflows and for conducting energy or quality system simulations.

Details

Language :
English
ISSN :
21949042 and 21949050
Volume :
X-4-2024
Database :
Directory of Open Access Journals
Journal :
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.07442984fad54ace87aa140fa59ccb2b
Document Type :
article
Full Text :
https://doi.org/10.5194/isprs-annals-X-4-2024-287-2024