Natural language Processing (NLP) for automated compliance checking: an investigation of the preprocessing of a Brazilian urban regulatory code
uma investigação do pré-processamento de um código regulatório urbanístico brasileiro
DOI:
https://doi.org/10.46421/entac.v19i1.2200Keywords:
Natural Language Processing, Automation, Pre-processing, Artificial Intelligence, Urban codeAbstract
Manually checking for compliance is a resource-intensive and error-prone task. Information in regulatory codes can be extracted automatically using natural language processing (NLP) techniques, making compliance checking simpler and more reliable. This work investigates a script using NLP techniques for the pre-processing – first phase of information extraction - of a Brazilian regulatory code. For this, the Python programming language and the NLTK library were used. An accuracy of 68% was achieved the performance of the labeller, indicating the need for improvements in the pre-processing for the Portuguese language.
References
Fuchs, S., Amor, R. (2021). Natural Language Processing for Building Code
Interpretation: A Systematic Literature Review
Beach, T. H, Hippolyte, J., Rezgui, Y. 2020. Towards the adoption of automated
regulatory compliance checking in the built environment. Automation in Construction.
, 103285.
Nawari, N. 2012. The Challenge of Computerizing Building Codes in a BIM Environment.
Comput. Civ. Eng. 1, 285–292.5
Olsson, P., Axelsson, J., Hooper, M., Harrie, L. 2018. Automation of building permission
by integration of BIM and geospatial data. International Journal of Geo-Information. 7
(8), 307.
Nawari, N. O. (2019). Generalized Adaptive Framework for Computerizing the Building
Design Review Process, Journal of architecture Engineering, 26(1), 04019026.
Altintas, Y. D., Ilal, M. 2021. Loose coupling of GIS and BIM data models for automated
compliance checking against zoning codes, Automation in Construction, 128, p. 103743.
Kim, I., Choi, J., Teo, E.A.L., Sun, H. 2020. Development of KBIM e-submission
prototypical system for the openBIM-based building permit framework. Journal of Civil
Engineering and Management. 26 (8), 744-756.
Shahi, K., McCabe, B.Y., Shahi, A.. Framework for Automated Model-Based e-Permitting
System for Municipal Jurisdictions, Journal of Management in Engineering, 35 (6),
2019
Salama, D. M.; El-Gohary, N.M. Semantic Text Classification for Supporting Automated
Compliance Checking in Construction, Journal of Computing in Civil Engineering, 30(1),
2014
Nieves, T.; Mendonça, E. A. de.; Ferreira, S. L.. Processamento de Linguagem Natural na
indústria AEC: uma abordagem para tradução de regulamentos edilícios brasileiros
para o domínio BIM. In: SIMPÓSIO BRASILEIRO DE TECNOLOGIA DA INFORMAÇÃO E
COMUNICAÇÃO NA CONSTRUÇÃO, 3., 2021, Uberlândia. Anais [...]. Porto Alegre: ANTAC,
p. 1-14. Disponível em: https://eventos.antac.org.br/index.php/sbtic/article/view/613, accessed em: 03 ago. 2021.
Barbosa, J.; Vieira, J.; Santos, R.; Junior, G.; Muniz, M.; Moura, R. Introdução ao
Processamento de Linguagem Natural usando Python. In: III Escola Regional de
Informática do Piauí. Livro Anais - Artigos e Minicursos. 2017. P. 336-360.
Rodriguez, M. Bezerra, B. (2020). Processamento de Linguagem Natural para
Reconhecimento de Entidades Nomeadas em Textos Jurídicos de Atos Administrativos
(Portarias). Revista de Engenharia e Pesquisa Aplicada. Special Edition, p. 67-77.
Zhang, J.; El-Gohary, N. M. Extending Building Information Models Semi automatically
Using Semantic Natural Language Processing Techniques, Journal of Computing in Civil
Engineering, 30(5), C4016004.
NLTK Project. (n.d.). Natural Language Toolkit — NLTK 3.6.2 documentation. [online]
Available at: https://www.nltk.org/index.html, accessed March 2022.
Zhang, J.; El-Gohary, N. M. Semantic NLP-Based Information Extraction from
Construction Regulatory Documents for Automated Compliance Checking, Journal of
Computing in Civil Engineering, 30(2), 04015014.
Xue, X.; Zhang, J. Evaluation of Seven Part-of-Speech Taggers in Tagging Building Codes:
Identifying the Best Performing Taggers and Common Soucers of Erros. In. Construction
Research Congress 2020. 2020. Tempe. Construction Research Congress 2020:
Computer Applications. p.498-507
Aluísio, S. M.; Pelizzoni, J. M.; Marchi, A. R.; Oliveira, L. H.; Manenti, R. E.;
Marquivafável, V. (2003). An Account of the Challenge of Tagging a Reference Corpus
for Brazilian Portuguese, pages 110–117. Springer Berlin Heidelberg, Berlin, Heidelberg.