Integration of parametric design tools with artificial intelligence in the construction industry - a review

Bryan Taico-Valverde(1) , Misael Castillo-Sosa(2)
(1) Universidad Nacional Federico Villarreal,
(2) Universidad Nacional Federico Villarreal

Abstract

Background: Technology continues to reshape the design and execution of construction projects in today's global scenario. Objective: The objectives of the research are focused on providing insight into the most prominent Parametric Design (PD) tools in the construction sector, accurately identifying the Artificial Intelligence (AI) techniques and algorithms used in the PD of buildings and structures, and prominently highlighting the benefits derived from the collaboration between these tools and AI in the context of construction. Methods: The present review is carried out using the Prisma methodology, with a specific search string, and applied to the determined scientific databases, between the years 2015 and 2023 and using exclusion and inclusion criteria. Results: The research concludes that the integration of parametric tools and AI, with special attention to Rhinoceros 3D + Grasshopper, has a significant impact on building through adaptive and complex models.  Collaboration with artificial neural networks (ANN) and convolutional neural networks (CNN) enables detailed simulations and automations. Conclusions: The benefits are numerous: optimized designs, automation, improved quality and efficient decision-making, all of which drive innovation in construction projects.

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Bryan Taico-Valverde
Misael Castillo-Sosa
Integration of parametric design tools with artificial intelligence in the construction industry - a review. (2024). International Journal of Educational Practices and Engineering(IJEPE), 1(1), pp 12-24. https://doi.org/10.70504/ijepe.v1i1.11009
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Integration of parametric design tools with artificial intelligence in the construction industry - a review. (2024). International Journal of Educational Practices and Engineering(IJEPE), 1(1), pp 12-24. https://doi.org/10.70504/ijepe.v1i1.11009