Chapter
Mar 7, 2022

Model Validation for Automated Building Code Compliance Checking

Publication: Construction Research Congress 2022

ABSTRACT

To allow full automation of building code compliance checking with different building design models and codes/regulations, input building design models need to be automatically validated. Automated architecture, engineering, and construction (AEC) object identification with high accuracy is essential for such validation. For example, in order to check egress requirements, exits of a building (and their presence or absence) need to be identified automatically through object identification. To address that, the authors propose a new AEC object identification algorithm that can identify needed code checking concepts from building design models based on the invariant signatures of AEC objects, which consisted of Cartesian points-based geometry, relative location and orientation, and material mechanical properties. Building design models in industry foundation classes (IFC) format are processed into invariant signatures, which can fully represent the model data and convert them into computable representations to support automated compliance reasoning. A systematic implementation of the above invariant signatures-based object identification algorithm can be used to automatically conduct building design model validation for code compliance checking preparation. An experimental testing on Chapters 4 and 8 of the International Building Code 2015 and a convenience store design model showed the model validation using the proposed identification algorithms successfully validated ceiling and interior door concepts. Comparing to the manual validation used in current practice, this new object identification algorithm is more efficient in supporting model validation for automated building code compliance checking.

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Go to Construction Research Congress 2022
Construction Research Congress 2022
Pages: 640 - 650

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Published online: Mar 7, 2022

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Jin Wu, S.M.ASCE [email protected]
1Automation and Intelligent Construction (AutoIC) Lab, School of Construction Management Technology, Purdue Univ., West Lafayette, IN. Email: [email protected]
Jiansong Zhang, Ph.D., A.M.ASCE [email protected]
2Automation and Intelligent Construction (AutoIC) Lab, School of Construction Management Technology, Purdue Univ., West Lafayette, IN. ORCID: https://orcid.org/0000-0001-5225-5943. Email: [email protected]
Luciana Debs, Ph.D. [email protected]
3School of Construction Management Technology, Purdue Univ., West Lafayette, IN. ORCID: https://orcid.org/0000-0002-9713-0957. Email: [email protected]

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