Inteligência artificial para automação de estimativa de custo em projeto arqitetônico
uma revisão sistemática da literatura
DOI:
https://doi.org/10.46421/sbtic.v4i00.2616Palavras-chave:
Inteligência Artificial, Estimativa de custos, Projeto Arquitetônico, Revisão sistemática da literaturaResumo
O custo é um fator importante na definição da viabilidade de projeto. Por isso, compreender o impacto das decisões projetuais no custo auxilia o arquiteto a realizar escolhas melhor embasadas. Analisando o recente avanço das tecnologias envolvendo Inteligência Artificial (IA) na arquitetura, supõe-se a IA como um potencial meio de solução. Neste contexto, foi realizada uma revisão sistemática da literatura (RSL) no repositório do Portal Periódicos CAPES para entender como a inteligência artificial pode ser implementada para automatização da estimativa de custos no processo de projeto. Com isso, foram desenvolvidos o mapeamento dos principais parâmetros de custo e a documentação das capacidades das IAs estudadas na arquitetura, bem como suas limitações. Por fim, realiza-se um comparativo de desempenho e propõe-se direcionamentos para futuras pesquisas. Assim, os resultados apontam que, por meio da IA, é possível obter um mecanismo de predição de custo adequado para o arquiteto, uma vez entendida a relação entre os recursos necessários e o nível de precisão pretendido.
Downloads
Referências
AS, Imdat; PAL, Siddharth; BASU, Prithwish. Artificial intelligence in architecture: Generating conceptual design via deep learning. International Journal of Architectural Computing, 2018, v. 16 (4), p. 306–327. Disponível em: <https://journals.sagepub.com/doi/full/10.1177/1478077118800982>. ISSN: 1478-0771. DOI:https://doi.org/10.1177/1478077118800982.
DOBRUCALI, Esra; DEMIR, Ismail Hakki. A simple formulation for early-stage cost estimation of building construction projects. Građevinar (Zagreb), 2021, v. 73 (8), p. 819-832. Disponível em: <http://www.casopis-gradjevinar.hr/archive/article/3013>. ISSN: 0350-2465. DOI: https://doi.org/10.14256/JCE.3013.2020.
EKICI, Berk et al. Performative computational architecture using swarm and evolutionary optimisation: A review. Building and environment, 2019, v.147, p.356-371. Disponível em: <https://pure.tudelft.nl/ws/portalfiles/portal/84485303/1_s2.0_S0360132318306413_main.pdf>. ISSN: 0360-1323. DOI: 10.1016/j.buildenv.2018.10.023.
ELMOUSALAMI, Haytham H. Comparison of Artificial Intelligence Techniques for Project Conceptual Cost Prediction: A Case Study and Comparative Analysis. IEEE transactions on engineering management, 2021, v. 68 (1), p. 183-196. Disponível em: <https://ieeexplore.ieee.org/document/9007411>. ISNN: 0018-9391. DOI: 10.1109/TEM.2020.2972078.
FAZELI, Abdulwahed et al. An integrated BIM-based approach for cost estimation in construction projects. Engineering, construction, and architectural management, 2021, v, .28 (9), p. 2828-2854. Disponível em: <https://www.emerald.com/insight/content/doi/10.1108/ECAM-01-2020-0027/full/html>. ISSN: 0969-9988. DOI: https://doi.org/10.1108/ECAM-01-2020-0027.
JIANG, Qinghua. Estimation of construction project building cost by back-propagation neural network. Journal of engineering, design and technology, 2020, v. 18 (3), p. 601-609. Disponível em: <https://www.emerald.com/insight/content/doi/10.1108/JEDT-08-2019-0195/full/html>. ISSN: 1726-0531. DOI: https://doi.org/10.1108/JEDT-08-2019-0195.
LEACH, Neil. Architecture in the Age of Artificial Intelligence: An Introduction to AI for Architects. London: Bloomsbury Visual Arts, 2022. ISBN: 9781350165519
LEE, Jaewook et al. BIM-based preliminary estimation method considering the life cycle cost for decision-making in the early design phase. Journal of Asian architecture and building engineering, 2020, vl.19 (4), p.384-399. Disponível em: <https://www.tandfonline.com/doi/full/10.1080/13467581.2020.1748635>. ISNN: 1346-7581. DOI: 10.1080/13467581.2020.1748635.
LEE, Minghui et al. Technology acceptance model for Building Information Modelling Based Virtual Reality (BIM-VR) in cost estimation. Journal of information technology in construction, 2022, v. 27, p. 914-92. Disponível em: <https://www.itcon.org/paper/2022/44>. ISNN: 1874-4753. DOI: 10.36680/j.itcon.2022.044.
LIANG, Rui; WANG, Po-Hsun; HU, Linhui. Application of Visual Recognition Based on BP Neural Network in Architectural Design Optimization. Computational intelligence and neuroscience, 2022, v. 2022, p.1-9. Disponível em: <https://www.hindawi.com/journals/cin/2022/3351196/>. ISSN: 1687-5265. DOI: 10.1155/2022/3351196.
LU, Yijun et al. Multi-Objective Optimization of Building Environmental Performance: An Integrated Parametric Design Method Based on Machine Learning Approaches. Energies (Basel), 2022, Vol.15 (19), p.7031. Disponível em: <https://www.mdpi.com/1996-1073/15/19/7031>. ISSN: 1996-1073. DOI: https://doi.org/10.3390/en15197031.
MIRANDA, Sergio Lautaro Castro et al. Predictive Analytics for Early-Stage Construction Costs Estimation. Buildings (Basel), 2022, v.12 (7), p. 1043. Disponível em: <https://www.mdpi.com/2075-5309/12/7/1043>. ISSN: 2075-5309. DOI: https://doi.org/10.3390/buildings12071043.
PARK, Uyeol et al. A Stacking Heterogeneous Ensemble Learning Method for the Prediction of Building Construction Project Costs. Applied sciences, 2022, v. 12 (19), p. 9729. Disponível em: <https://www.mdpi.com/2076-3417/12/19/9729>. ISSN: 2076-3417. DOI: https://doi.org/10.3390/app12199729.
PHAM, T. Q. D. et al. Efficient estimation and optimization of building costs using machine learning.
International journal of construction management, 2023,v. 23 (5), p. 909-921. Disponível em: <https://www.tandfonline.com/doi/abs/10.1080/15623599.2021.1943630?journalCode=tjcm20>. ISSN: 1562-3599. DOI: https://doi.org/10.1080/15623599.2021.1943630.
PENA, Castro et al. Artificial intelligence applied to conceptual design. A review of its use in architecture. Automation in Construction, 2021, v. 124. Disponível em: <https://www.sciencedirect.com/science/article/pii/S0926580521000017?via%3Dihub>.DOI:https://doi.org/10.1016/j.autcon.2021.103550.
SHIN, Yoonseok. Application of Boosting Regression Trees to Preliminary Cost Estimation in Building Construction Projects. Computational intelligence and neuroscience, 2015, v. 2015, p. 149702-9. Disponível em: <https://www.hindawi.com/journals/cin/2015/149702/>. ISNN: 1687-5265. DOI: 10.1155/2015/149702.
SHISHEHGARKHANEH, Milad Baghalzadeh et al. BIM-Based Resource Tradeoff in Project Scheduling Using Fire Hawk Optimizer (FHO). Buildings (Basel), 2022, v..12 (9), p.1472. Disponível em: <https://www.mdpi.com/2075-5309/12/9/1472>. ISSN: 2075-5309. DOI: https://doi.org/10.3390/buildings12091472.
WANG, Wei-Chih et al. Conceptual cost estimations using neuro-fuzzy and multi-factor evaluation methods for building projects. Journal of Civil Engineering and Management, 2017, v. 23 (1), p. 1-14. Disponível em: <https://www.researchgate.net/publication/312558888_Conceptual_cost_estimations_using_neuro-fuzzy_and_multi-factor_evaluation_methods_for_building_projects> DOI:10.3846/13923730.2014.948908.
WANG, Yali et al. Cost prediction of building projects using the novel hybrid RA-ANN model. Engineering, construction, and architectural management, 2023. Disponível em: <https://www.emerald.com/insight/content/doi/10.1108/ECAM-07-2022-0666/full/html>. ISSN: 0969-9988. DOI: https://doi.org/10.1108/ECAM-07-2022-0666.
WOOD, Jamin et al. Using LOD in Structural Cost Estimation during Building Design Stage: Pilot Study. Procedia Engineering, 2014, v. 85, p. 543-552. Disponível em: <https://www.sciencedirect.com/science/article/pii/S1877705814019481>. ISSN 1877-7058. DOI:https://doi.org/10.1016/j.proeng.2014.10.582.
YANG, Seung-Won et al. Parametric Method and Building Information Modeling-Based Cost Estimation Model for Construction Cost Prediction in Architectural Planning. Applied sciences, 2022, v.12 (19), p. 9553. Disponível em: <https://www.mdpi.com/2076-3417/12/19/9553>. ISSN: 2076-3417. DOI: 10.3390/app12199553
ZHANG, Xiaocun; ZHANG, Xueqi. Design of low-carbon and cost-efficient concrete frame buildings: a hybrid optimization approach based on harmony search. Journal of Asian architecture and building engineering, 2022, p.1-14. Disponível em: <https://www.tandfonline.com/doi/full/10.1080/13467581.2022.2145202>. DOI: https://doi.org/10.1080/13467581.2022.2145202
ZHAO, Liang et al. Construction Cost Prediction Based on Genetic Algorithm and BIM. International journal of pattern recognition and artificial intelligence, 2020, v. 34 (7), p.2059026. Disponível em: <https://www.worldscientific.com/doi/10.1142/S0218001420590260>. ISSN: 0218-0014. DOI: 10.1142/S0218001420590260.
Downloads
Publicado
Como Citar
Edição
Seção
Licença
Copyright (c) 2023 SIMPÓSIO BRASILEIRO DE TECNOLOGIA DA INFORMAÇÃO E COMUNICAÇÃO NA CONSTRUÇÃO
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
Adota-se Atribuição 4.0 Internacional (CC BY 4.0). Ver detalhamento em https://creativecommons.org/licenses/by/4.0/deed.pt_BR
Dados de financiamento
-
Fundação Cearense de Apoio ao Desenvolvimento Científico e Tecnológico
Números do Financiamento R$450