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Quantified point clouds and enriched BIM-Models for digitalised maintenance planning
- Source :
- MATEC Web of Conferences, Vol 364, p 05001 (2022)
- Publication Year :
- 2022
- Publisher :
- EDP Sciences, 2022.
-
Abstract
- 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.
- Subjects :
- Engineering (General). Civil engineering (General)
TA1-2040
Subjects
Details
- Language :
- English, French
- ISSN :
- 2261236X
- Volume :
- 364
- Database :
- Directory of Open Access Journals
- Journal :
- MATEC Web of Conferences
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.73cc74045bfb47eb9a8f69c9d4f23fc5
- Document Type :
- article
- Full Text :
- https://doi.org/10.1051/matecconf/202236405001