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Enhancing Compliance and Efficiency in Infrastructure Projects through Smart Standards
Citation Link: https://doi.org/10.15480/882.13537
Publikationstyp
Conference Paper
Date Issued
2024-09-18
Sprache
English
TORE-DOI
Start Page
180
End Page
187
Citation
35. Forum Bauinformatik, fbi 2024: 180-187
Contribution to Conference
Publisher
Technische Universität Hamburg, Institut für Digitales und Autonomes Bauen
Peer Reviewed
true
The BIM master plan aims to achieve comprehensive implementation of BIM technology in federal road construction by 2025. This technology is already being used extensively in the planning phase, supported by integrating digital models created and exchanged by existing standards and guidelines. Rule-based, semi-automated test procedures ensure model quality and compliance with regulations. However, one drawback is that the necessary information is usually only available in non-machine-readable continuous texts and illustrations. An essential part of the project is therefore the conversion of traditional regulations into Smart Standards, which are serialized as XML documents according to the NISO STS to optimize their management and the provision of the contained information. In preparation, the suitability of existing regulations was analyzed and ten regulations were transferred to the NISO STS, considering the current version of the federal road construction object catalog. Furthermore, a concept for the description of information requirements and checking rules in the regulations was developed. The converted Smart Standards are integrated into the checking systems and the checking rules entered are tested on a model. Finally, recommendations for the management and administration of the Smart Standards were formulated.
Subjects
algorithms
Building information modeling (BIM)
design automation
energy simulation
DDC Class
006: Special computer methods
620: Engineering
Publication version
publishedVersion
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Name
Enhancing Compliance and Efficiency in Infrastructure Projects through Smart Standards.pdf
Type
Main Article
Size
336.19 KB
Format
Adobe PDF