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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REFERENCES
Adán, A., Quintana, B., Prieto, S. A., and Bosché, F. (2018). “Scan-to-BIM for ‘secondary’ building components.” Advanced Engineering Informatics, 37, 119–138.
Azhar, S. (2011). “Building Information Modeling (BIM): Trends, Benefits, Risks, and Challenges for the AEC Industry.” Leadership and Management in Engineering, 11(3), 241–252.
BuildingSMART. (2018). “Industry Foundation Classes (IFC) - An Introduction.” <https://technical.buildingsmart.org/standards/ifc>(May 18, 2021).
Ding, L., Li, K., Zhou, Y., and Love, P. E. D. (2017). “An IFC-inspection process model for infrastructure projects: Enabling real-time quality monitoring and control”. Automation in construction, 84, 96–110.
Fernández, M., Cantador, I., López, V., Vallet, D., Castells, P., and Motta, E. (2011) “Semantically enhanced information retrieval: an ontology-based approach.” Web Semantics, 9(4), 434–452.
Hamledari, H., McCabe, B., and Davari, S. (2017). “Automated computer vision-based detection of components of under-construction indoor partitions.” Automation in Construction, 74, 78–94.
International Code Council. (2015). 2015 International Building Code. International Code Council (ICC), Washington, DC.
Nguyen, T., and Kim, J. (2011). “Building code compliance checking using BIM technology.” Proc., 2011 Winter Simulation Conference (WSC), IEEE, New York, 3395–3400.
Paliouras, G., Spyropoulos, C. D., and Tsatsaronis, G. (2011). Knowledge-driven multimedia information extraction and ontology evolution: bridging the semantic gap. Springer, Berlin, Heidelberg.
Puente, I., González-Jorge, H., Martínez-Sánchez, J., and Arias, P. (2014). “Automatic detection of road tunnel luminaires using a mobile LiDAR system.” Measurement: Journal of the International Measurement Confederation, 47(1), 569–575.
Sacks, R., Lee, G., Eastman, C., and Teicholz, P. (2018). BIM handbook: a guide to building information modeling for owners, managers, designers, engineers and contractors. Wiley-Blackwell, Hoboken, New Jersey.
Sarawagi, S. (2008). “Information Extraction.” Foundations and Trends in Databases, 3(3), 261–377.
Tan, X., Hammad, A., and Fazio, P. (2010) “Automated code compliance checking for building envelope design.” Journal of Computing in Civil Engineering, 24(2), 203–211.
Wimalasuriya, D. C., and Dou, D. (2010). “Ontology-based information extraction: An introduction and a survey of current approaches.” Journal of Information Science, 36(3), 306–323.
Wu, J., Sadraddin, H. L., Ren, R., Zhang, J., and Shao, X. (2021). “Invariant signatures of architecture, engineering, and construction objects to support BIM interoperability between architectural design and structural analysis.” Journal of Construction Engineering and Management, 147(1).
Xu, X., and Cai, H. (2020). “Semantic approach to compliance checking of underground utilities.” Automation in Construction, 109, 103006.
Yang, Q. Z., and Xu, X. (2004). “Design knowledge modeling and software implementation for building code compliance checking.” Building and Environment, 39(6), 689–698.
Yang, Z., Yu, H., Tang, J., and Liu, H. (2019). “Toward keyword extraction in constrained information retrieval in vehicle social network.” IEEE Transactions on Vehicular Technology, 68(5), 4285–4294.
Yehia, E., Boshnak, E., AbdelGaber, S., Abdo, A., and Elzanfaly, D. S. (2019). “Ontology-based clinical information extraction from physician’s free-text notes.” Journal of Biomedical Informatics, 98, 103276–103276.
Zhang, J., and El-Gohary, N. (2016a). “Extending building information models semi-automatically using natural language processing techniques.” Journal of Computing in Civil Engineering, 30(5).
Zhang, J., and El-Gohary, N. (2016b). “Semantic NLP-based information extraction from construction regulatory documents for automated compliance checking.” Journal of Computing in Civil Engineering, ASCE, 1943–5487.
Zhang, J., and El-Gohary, N. (2017a). “Semantic-based logic representation and reasoning for automated regulatory compliance checking.” Journal of Computing in Civil Engineering, 31(1), 04016037.
Zhang, J., and El-Gohary, N. (2017b). “Integrating semantic NLP and logic reasoning into a unified system for fully-automated code checking. ” Automation in Construction, 73, 45–57.
Zhang, R., and El-Gohary, N. (2019). “A machine-learning approach for semantic matching of building codes and building information models (BIMs) for supporting automated code checking.” Proc. GeoMEast 2019 International Congress and Exhibition, Cairo, Egypt.
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Published online: Mar 7, 2022
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