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Building Information Modeling Using Constraint Logic Programming

Published online by Cambridge University Press:  14 July 2022

JOAQUÍN ARIAS Open the ORCID record for JOAQUÍN ARIAS [Opens in a new window] ,
SEPPO TÖRMÄ ,
MANUEL CARRO  and
GOPAL GUPTA
JOAQUÍN ARIAS
Affiliation:
CETINIA, Universidad Rey Juan Carlos, Madrid, Spain (e-mail: joaquin.arias@urjc.es)
SEPPO TÖRMÄ
Affiliation:
VisuaLynk Oy, Espoo, Finland (e-mail: seppo.t.torma@gmail.com)
MANUEL CARRO
Affiliation:
Universidad Politécnica de Madrid, Madrid, Spain IMDEA Software Institute, Pozuelo de Alarcón, Spain (e-mails: manuel.carro@upm.es, manuel.carro@imdea.org)
GOPAL GUPTA
Affiliation:
University of Texas at Dallas, Richardson, TX 75080, USA (e-mail: gupta@utdallas.edu)
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Abstract

Building Information Modeling (BIM) produces three-dimensional object-oriented models of buildings combining the geometrical information with a wide range of properties about materials, products, safety, to name just a few. BIM is slowly but inevitably revolutionizing the architecture, engineering, and construction industry. Buildings need to be compliant with regulations about stability, safety, and environmental impact. Manual compliance checking is tedious and error-prone, and amending flaws discovered only at construction time causes huge additional costs and delays. Several tools can check BIM models for conformance with rules/guidelines. For example, Singapore’s CORENET e-Submission System checks fire safety. But since the current BIM exchange format only contains basic information about building objects, a separate, ad-hoc model pre-processing is required to determine, for example, evacuation routes. Moreover, they face difficulties in adapting existing built-in rules and/or adding new ones (to cater for building regulations, that can vary not only among countries but also among parts of the same city), if at all possible. We propose the use of logic-based executable formalisms (CLP and Constraint ASP) to couple BIM models with advanced knowledge representation and reasoning capabilities. Previous experience shows that such formalisms can be used to uniformly capture and reason with knowledge (including ambiguity) in a large variety of domains. Additionally, incorporating checking within design tools makes it possible to ensure that models are rule-compliant at every step. This also prevents erroneous designs from having to be (partially) redone, which is also costly and burdensome. To validate our proposal, we implemented a preliminary reasoner under CLP(Q/R) and ASP with constraints and evaluated it with several BIM models.

Keywords

building information modelingconstraintcommonsense reasoninganswer set programming
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 22 , Issue 5: 38th International Conference on Logic Programming Special Issue II , September 2022 , pp. 723 - 738
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Copyright
© The Author(s), 2022. Published by Cambridge University Press

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Footnotes

*

We would like to thank Vishal Singh, and Mehmet Yalcinkaya, from VisuaLynk, and especially Olli Seppänen, who hosted J. Arias at Aalto University in the summer of 2019, for useful discussions in the early stages of the work we present in this paper. Work partially supported by EIT Digital, EU H2020 project BIM4EEB (Grant 820660), MICINN projects RTI2018-095390-B-C33 InEDGEMobility (MCIU/AEI/FEDER, UE), PID2019-108528RB-C21 ProCode, Comunidad de Madrid project S2018/TCS-4339 BLOQUES-CM co-funded by EIE Funds of the European Union, US NSF (Grants IIS 1718945, IIS 1910131, IIP 1916206), DoD, and Amazon.

References

Arias, J., Carro, M., Chen, Z. and Gupta, G. 2022. Modeling and reasoning in event calculus using goal-directed constraint answer set programming. Theory and Practice of Logic Programming 22, 1, 51–80.CrossRefGoogle Scholar
Arias, J., Carro, M., Salazar, E., Marple, K. and Gupta, G. 2018. Constraint answer set programming without grounding. Theory and Practice of Logic Programming 18, 3-4, 337–354.CrossRefGoogle Scholar
Arias, J., Moreno-Rebato, M., Rodriguez-García, J. A. and Ossowski, S. 2021. Modeling administrative discretion using goal-directed answer set programming. In Advances in Artificial Intelligence, CAEPIA 20/21. LNCS, vol. 12882. Springer, 258–267.Google Scholar
Balduccini, M. and Lierler, Y. 2017. Constraint answer set solver EZCSP and why integration schemas matter. Theory and Practice of Logic Programming 17, 4, 462–515.CrossRefGoogle Scholar
Banbara, M., Kaufmann, B., Ostrowski, M. and Schaub, T. 2017. Clingcon: The next generation. Theory and Practice of Logic Programming 17, 4, 408–461.CrossRefGoogle Scholar
BuildingSMART 2020. Industry Foundation Classes (IFC). URL: https://technical.buildingsmart.org/standards/ifc/. [Accessed on July, 2020].Google Scholar
Gelfond, M. and Lifschitz, V. 1988. The stable model semantics for logic programming. In 5th International Conference on Logic Programming, 1070–1080.Google Scholar
Holzbaur, C. 1995. OFAI CLP(Q,R) Manual, Edition 1.3.3. Technical Report TR-95-09, Austrian Research Institute for Artificial Intelligence, Vienna.Google Scholar
Lee, H., Lee, J., Park, S. and Kim, I. 2016. Translating building legislation into a computer-executable format for evaluating building permit requirements. Automation in Construction 71, 49–61.CrossRefGoogle Scholar
Li, B., Teizer, J. and Schultz, C. 2020. Non-monotonic spatial reasoning for safety analysis in construction. In Proceedings of the 22nd International Symposium on Principles and Practice of Declarative Programming, 1–12.Google Scholar
Lierler, Y. 2021. Constraint answer set programming: Integrational and translational (or SMT-based) approaches. Theory and Practice of Logic Programming, 1–31. https://doi.org/10.1017/S1471068421000478.CrossRefGoogle Scholar
Pauwels, P., Van Deursen, D., Verstraeten, R., De Roo, J., De Meyer, R., Van De Walle, R. and Van Campenhout, J. 2011. A semantic rule checking environment for building performance checking. Automation in Construction 20, 5, 506–518.CrossRefGoogle Scholar
Singapore Government. 2016. Corenet BIM e-Submission. URL: https://www.corenet.gov.sg/general/building-information-modeling-(bim)-e-submission.aspx.Google Scholar
Solihin, W. 2015. A Simplified BIM Data Representation Using a Relational Database Schema for an Efficient Rule Checking System and its Associated Rule Checking Language. Ph.D. thesis, Georgia Institute of Technology.Google Scholar
Zhang, C., Beetz, J. and de Vries, B. 2018. Bimsparql: Domain-specific functional sparql extensions for querying rdf building data. Semantic Web 9, 6, 829–855.CrossRefGoogle Scholar
Zhang, S., Teizer, J., Lee, J., Eastman, C. and Venugopal, M. 2013. Building Information Modeling (BIM) and safety: Automatic safety checking of construction models and schedules. Automation in Construction 29, 183–195.CrossRefGoogle Scholar