Issue |
MATEC Web Conf.
Volume 364, 2022
International Conference on Concrete Repair, Rehabilitation and Retrofitting (ICCRRR 2022)
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Article Number | 05001 | |
Number of page(s) | 8 | |
Section | Developments in Concrete Material Technology, Assessment and Processing | |
DOI | https://doi.org/10.1051/matecconf/202236405001 | |
Published online | 30 September 2022 |
Quantified point clouds and enriched BIM-Models for digitalised maintenance planning
Institute of Building Materials Research (ibac), RWTH Aachen University, 52062 Aachen, Germany
* Corresponding author: morgenstern@ibac.rwth-aachen.de
Digitisation in the construction industry continues to advance and, together with the increasing dissemination and further development of hardware and software, is steadily opening up further opportunities for innovative ways of working. Building Information Modelling (BIM) is currently becoming the standard for new construction but has not yet been optimised for use in existing buildings. Therefore, the Institute of Building Materials Research (ibac) is researching new methods and possibilities for BIM-based building preservation. In this paper, the automated creation and analysis of point clouds as well as the implementation of further information from in situ diagnosis and monitoring systems in BIM-Models are presented. On a practical example, the different steps of a subsequent digitisation of an existing building are demonstrated considering new possibilities as autonomous robots and the intelligent utilisation of sensors and diagnostics tools. The goal is a decision support tool, which is independent from proprietary software, adaptive to different types of buildings and open for various interfaces. Current results show that quantifying point clouds and making BIM-models usable beyond the planning and execution phase for new buildings are essential steps for the digitisation of building maintenance. The proposed digital workflow holds great potential for effective building diagnoses and efficient service life management.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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