Applying AI technology to recognize BIM objects and visible properties for achieving automated code compliance checking
Abstract
Automated code compliance checking is an effective approach for assessing the quality of building information modeling (BIM) models. Various automated code compliance checking systems have emerged, wherein users need to input all information accurately according to BIM modeling guidelines, in order to ensure the accuracy of checking results. However, as this process involves human inputs, it is difficult to ensure that each input is accurate. In the case of errors or missing inputs, the checking results will be erroneous. Although automated checking systems can be developed accurately, it is difficult to apply these systems practically. Therefore, this paper proposes the application of AI technology to recognize BIM objects and visible properties, in order to improve the operability of automated code compliance checking. The two necessary elements – object names and properties – could be automatically extracted to a certain extent, following the application of the proposed method to the automated code checking process. The error rate of the input could also be reduced, thus making the application of the code checking system more practically feasible. The proposed recognition method for BIM objects and visible properties is also expected to be used widely in BIM-based building e-submission systems and BIM-based forward designs.
Keyword : Building Information Modeling, automated code compliance checking, artificial intelligence, industry foundation classes (IFC), BIM object recognition, visible property recognition
How to Cite
Sun, H., & Kim, I. (2022). Applying AI technology to recognize BIM objects and visible properties for achieving automated code compliance checking. Journal of Civil Engineering and Management, 28(6), 497–508. https://doi.org/10.3846/jcem.2022.16994
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Koo, B., Jung, R., Yu, Y., & Kim, I. (2021b). A geometric deep learning approach for checking element-to-entity mappings in infrastructure building information models. Journal of Computational Design and Engineering, 8(1), 239–250. https://doi.org/10.1093/jcde/qwaa075
Krijnen, T., & Tamke, M. (2015). Assessing implicit knowledge in BIM models with machine learning. In M. Thomsen, M. Tamke, C. Gengnagel, B. Faircloth, & F. Scheurer (Eds.), Modelling behaviour (pp. 397–406). Springer, Cham. https://doi.org/10.1007/978-3-319-24208-8_33
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Lee, J. K., & Kim, M. J. (2014). BIM-enabled conceptual modeling and representation of building circulation. International Journal of Advanced Robotics Systems, 11(8), 127. https://doi.org/10.5772/58440
Liebich, T. (2009). IFC 2x Edition 3 Model implementation guide (Version 2.0).
Malsane, S. M., Matthews, J., Lockley, S., Love, P. E., & Greenwood, D. (2015). Development of an object model for automated compliance checking. Automation in Construction, 49, 51–58. https://doi.org/10.1016/j.autcon.2014.10.004
McCarthy, J. (1959). Programs with common sense. In Proceedings of the Teddington Conference on the Mechanization of Thought Processes (pp. 75–91).
Motamedi, A., Soltani, M. M., Setayeshgar, S., & Hammad, A. (2016). Extending IFC to incorporate information of RFID tags attached to building elements. Advanced Engineering Informatics, 30, 39–53. https://doi.org/10.1016/j.aei.2015.11.004
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Sacks, R., Ma, L., Yosef, R., Borrmann, A., Daum, S., & Kattel, U. (2017). Semantic enrichment for building information modeling: Procedure for compiling inference rules and operators for complex geometry. Journal of Computing in Civil Engineering, 31(6), 04017062. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000705
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Solihin, W., Eastman, C., Lee, Y.-C., Yang, D.-H. (2017a). A simplified relational database schema for transformation of BIM data into a query-efficient and spatially enabled database. Automation in Construction, 84, 367–383. https://doi.org/10.1016/j.autcon.2017.10.002
Solihin, W., Eastman, C., & Lee, Y.-C. (2017b). Multiple representation approach to achieve high-performance spatial queries of 3D BIM data using a relational database. Automation in Construction, 81, 369–388. https://doi.org/10.1016/j.autcon.2017.03.014
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Su, J., Gahelda, M., Wang, R., & Maji, S. (2018). A deeper look at 3D shape classifiers. In Second Workshop on 3D Reconstruction Meets Semantics (ECCV, 2018). https://doi.org/10.1007/978-3-030-11015-4_49
Sun, H., Kim, I., & Choi, J. (2018). A Study of applying machine learning technology on the pre-check process for automatic building code checking system. In Korea Construction Management Society Proceedings (pp. 171–172).
Sun, H., Kim, G., & Kim, I. (2019). A study on applying visual feature recognition method in automated code compliance checking process. In i3CDE 2019 Proceedings, Penang, Malaysia.
TensorFlow. (2021). Image classification. https://www.tensorflow.org/tutorials/images/classification
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Urban Redevelopment Authority. (2020). BIM modelling guideline for URA GFA autochecker system.
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., & Xiao, J. (2015). 3D ShapeNets: A deep representation for volumetric shapes. In Proceedings of 28th IEEE Conference on Computer Vision and Pattern Recognition (CVPR2015). IEEE.
Wu, J., & Zhang, J. (2019). New automated BIM object classification method to support BIM interoperability. Journal of Computing in Civil Engineering, 33(5), 04019033. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000858
Zhou, P., & El-Gohary, N. (2021). Semantic information alignment of BIMs to computer-interpretable regulations using ontologies and deep learning. Advanced Engineering Informatics, 48, 101239. https://doi.org/10.1016/j.aei.2020.101239
Alshehri, F., Keeny, P., Pinheiro, S., Ali, U., & Donnell, J. (2017). Development of a model view definition (MVD) for thermal comfort analyses in commercial buildings using BIM and EnergyPlus. In CitA BIM Gathering 2017 (pp. 226–233). Construction IT Allance of Ireland.
Amor, R., & Dimyadi, J. (2021). The promise of automated compliance checking. Developments in the Built Environment, 5, 100039. https://doi.org/10.1016/j.dibe.2020.100039
Bloch, T., & Sacks, R. (2018). Comparing machine learning and rule-based inferencing for semantic enrichment of BIM models. Automation in Construction, 91, 256–272. https://doi.org/10.1016/j.autcon.2018.03.018
Building and Construction Authority. (2016). Universal design guide for public places.
Building and Construction Authority. (2021). The future of smart regulation [Video]. YouTube. https://www.youtube.com/watch?v=dTJ3DKRb1lc
Cheng, J. C. P., & Lu, Q. (2015). A review of the efforts and roles of the public sector for BIM adoption worldwide. Journal of Information Technology in Construction (ITcon), 20, 442–478.
Choi, J., & Kim, I. (2011). Interoperability tests of IFC property information for open BIM based quality assurance. Transactions of the Society of CAD/CAM Engineers, 16(2), 92–103.
Eastman, C., Lee, J., Jeong, Y., & Lee, J. (2009). Automatic rule-based checking of building designs. Automation in Construction, 18, 1011–1033. https://doi.org/10.1016/j.autcon.2009.07.002
Eastman, C., Jeong, Y., Sacks, R., & Kaner, I. (2010). Exchange model and exchange object concepts for implementation of national BIM standards. Journal of Computing in Civil Engineering, 24(1), 25–34. https://doi.org/10.1061/(ASCE)0887-3801(2010)24:1(25)
Eastman, C., Teicholz, P., Sacks, R., & Liston, K. (2011). BIM handbook: a guide to building information modeling for owners, managers, designers, engineers and contractors (2nd ed.). Wiley.
Edirisinghe, R., & London, K. (2015). Comparative analysis of international and national level BIM standardization efforts and BIM adoption. In Proceedings of the 32nd International Conference of CIB W78 (pp. 149–158), Eindhoven, The Netherlands.
Harty, J., Kouider, T., & Paterson, G. (2015). A guide for small and medium-sized architecture, engineering and construction firms (1st ed.). Routledge. https://doi.org/10.4324/9781315730721
International Organization for Standardization. (2018). Industry Foundation Classes (IFC) for data sharing in the construction and facility management industries – Part 1: data schema (ISO Standard No. ISO 16739:2018). https://www.iso.org/standard/70303.html
International Organization for Standardization. (2020). Industrial automation systems and integration — Product data representation and exchange – Part 242: Application protocol: Managed model-based 3D engineering (ISO Standard No. ISO 10303-242:2020). https://www.iso.org/standard/66654.html
Kim, J., & Lee, J. (2019). Implementation of auto-classification of unclassified objects in BIM model using 2D CNN for design rule-checking systems. Korean Journal of Computational Design and Engineering, 24(4), 452–461. https://doi.org/10.7315/CDE.2019.452
Kim, I., Choi, J., Teo, E. A. L., & Sun, H. (2020). Development of K-BIM e-Submission prototypical system for the openBIM-based building permit framework. Journal of Civil Engineering and Management, 26(8), 744–756. https://doi.org/10.3846/jcem.2020.13756
Kim, J., Song, J., & Lee, J. (2019). Recognizing and classifying unknown object in BIM using 2D CNN. In International Conference on Computer-Aided Architectural Design Futures (pp. 47–57). Springer, Singapore. https://doi.org/10.1007/978-981-13-8410-3_4
Koo, B., & Shin, B. (2017). Using geometry based anomaly detection to check the integrity of IFC classifications in BIM models. Journal of KIBIM, 7(1), 18–27. https://doi.org/10.13161/kibim.2017.7.1.018
Koo, B., & Shin, B. (2018). Applying novelty detection to identify model element to IFC class misclassifications on architectural and infrastructure building information models. Journal of Computational Design and Engineering, 5(4), 391–400. https://doi.org/10.1016/j.jcde.2018.03.002
Koo, B., La, S., Cho, N., & Yu, Y. (2019). Using support vector machines to classify building elements for checking the semantic integrity of building information models. Automation in Construction, 98, 183–194. https://doi.org/10.1016/j.autcon.2018.11.015
Koo, B., Jung, R., & Yu, Y. (2021a). Automatic classification of walland door BIM element subtypes using 3D geometric deep neural networks. Advanced Engineering Informatics, 47(1), 101200. https://doi.org/10.1016/j.aei.2020.101200
Koo, B., Jung, R., Yu, Y., & Kim, I. (2021b). A geometric deep learning approach for checking element-to-entity mappings in infrastructure building information models. Journal of Computational Design and Engineering, 8(1), 239–250. https://doi.org/10.1093/jcde/qwaa075
Krijnen, T., & Tamke, M. (2015). Assessing implicit knowledge in BIM models with machine learning. In M. Thomsen, M. Tamke, C. Gengnagel, B. Faircloth, & F. Scheurer (Eds.), Modelling behaviour (pp. 397–406). Springer, Cham. https://doi.org/10.1007/978-3-319-24208-8_33
Kwon, T. H., Park, S. I., Jang, Y.-H., & Lee, S.-H. (2020). Design of railway track model with three-dimensional alignment based on extended industry foundation classes. Applied Sciences, 10(10), 3649. https://doi.org/10.3390/app10103649
Lee, Y. C., Eastman, C., & Solihin, W. (2021). Rules and validation processes for interoperable BIM data exchange, Journal of Computational Design and Engineering, 8(1), 97–114. https://doi.org/10.1093/jcde/qwaa064
Lee, H., Lee, J., Park S., & Kim, I. (2016). Translating building legislation into a computer executable format for evaluating building permit requirements. Automation in Construction, 71(1), 49–61. https://doi.org/10.1016/j.autcon.2016.04.008
Lee, J. K., & Kim, M. J. (2014). BIM-enabled conceptual modeling and representation of building circulation. International Journal of Advanced Robotics Systems, 11(8), 127. https://doi.org/10.5772/58440
Liebich, T. (2009). IFC 2x Edition 3 Model implementation guide (Version 2.0).
Malsane, S. M., Matthews, J., Lockley, S., Love, P. E., & Greenwood, D. (2015). Development of an object model for automated compliance checking. Automation in Construction, 49, 51–58. https://doi.org/10.1016/j.autcon.2014.10.004
McCarthy, J. (1959). Programs with common sense. In Proceedings of the Teddington Conference on the Mechanization of Thought Processes (pp. 75–91).
Motamedi, A., Soltani, M. M., Setayeshgar, S., & Hammad, A. (2016). Extending IFC to incorporate information of RFID tags attached to building elements. Advanced Engineering Informatics, 30, 39–53. https://doi.org/10.1016/j.aei.2015.11.004
Nova Group. (2020). Building & Construction Authority (BCA), Singapore Awards Project to NOVA to localise IFC data model. https://www.nova-hub.com/novanews/building-and-construction-authority-singapore-awards-project-to-nova-to-localised-ifc-data-model/
Patacas, J., Dawood, N., & Kassem, M. (2020). BIM for facilities management: a framework and acommon data environment using open standards. Automation in Construction, 120, 103366. https://doi.org/10.1016/j.autcon.2020.103366
Professional Engineers Board Singapore. (2019). PEB seminar. https://www.peb.gov.sg/Downloads/seminar_2019.pdf
Public Utilities Board. (2020). PUB BIM checking system BIM modelling guideline.
Sacks, R., Ma, L., Yosef, R., Borrmann, A., Daum, S., & Kattel, U. (2017). Semantic enrichment for building information modeling: Procedure for compiling inference rules and operators for complex geometry. Journal of Computing in Civil Engineering, 31(6), 04017062. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000705
Solihin, W., Eastman, C., & Lee, Y. (2016). A framework for fully integrated building information models in a federated environment. Advanced Engineering Informatics, 30, 168–189. https://doi.org/10.1016/j.aei.2016.02.007
Solihin, W., Eastman, C., Lee, Y.-C., Yang, D.-H. (2017a). A simplified relational database schema for transformation of BIM data into a query-efficient and spatially enabled database. Automation in Construction, 84, 367–383. https://doi.org/10.1016/j.autcon.2017.10.002
Solihin, W., Eastman, C., & Lee, Y.-C. (2017b). Multiple representation approach to achieve high-performance spatial queries of 3D BIM data using a relational database. Automation in Construction, 81, 369–388. https://doi.org/10.1016/j.autcon.2017.03.014
Su, H., Maji, S., Kalogerakis, E., & Learned-Miller, E. (2015). Multiview convolutional neural networks for 3D shape recognition. In Proceedings of the IEEE International Conference on Computer Vision (pp. 945–953). IEEE. https://doi.org/10.1109/ICCV.2015.114
Su, J., Gahelda, M., Wang, R., & Maji, S. (2018). A deeper look at 3D shape classifiers. In Second Workshop on 3D Reconstruction Meets Semantics (ECCV, 2018). https://doi.org/10.1007/978-3-030-11015-4_49
Sun, H., Kim, I., & Choi, J. (2018). A Study of applying machine learning technology on the pre-check process for automatic building code checking system. In Korea Construction Management Society Proceedings (pp. 171–172).
Sun, H., Kim, G., & Kim, I. (2019). A study on applying visual feature recognition method in automated code compliance checking process. In i3CDE 2019 Proceedings, Penang, Malaysia.
TensorFlow. (2021). Image classification. https://www.tensorflow.org/tutorials/images/classification
To, A., Liu, M., Hairul, M., Davis, J., Lee, J., Hesse, H., & Nguyen, H. (2021). Drone-based AI and 3D reconstruction for digital twin augmentation. In G. Meiselwitz (Ed.), Lecture notes in computer science: Vol. 12774. Social computing and social media: Experience design and social network analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-77626-8_35
Urban Redevelopment Authority. (2020). BIM modelling guideline for URA GFA autochecker system.
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., & Xiao, J. (2015). 3D ShapeNets: A deep representation for volumetric shapes. In Proceedings of 28th IEEE Conference on Computer Vision and Pattern Recognition (CVPR2015). IEEE.
Wu, J., & Zhang, J. (2019). New automated BIM object classification method to support BIM interoperability. Journal of Computing in Civil Engineering, 33(5), 04019033. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000858
Zhou, P., & El-Gohary, N. (2021). Semantic information alignment of BIMs to computer-interpretable regulations using ontologies and deep learning. Advanced Engineering Informatics, 48, 101239. https://doi.org/10.1016/j.aei.2020.101239