MULTI-OBJECTIVE OPTIMIZATION ALGORITHM FOR LAYOUT OPTIMIZATION OF EDUCATIONAL INSTITUTIONAL SPACES

Authors

  • Érika Mayumi Shibata Centro Universitário Facens
  • Natália Nakamura Barros Centro Universitário Facens

DOI:

https://doi.org/10.29327/sbqp2021.438102

Keywords:

Multi-objective algorithm, Form optimization, Educational institutions

Abstract

The inevitable transformation of the digital revolution combined with changes in people's lives driven by the Covid-19 pandemic and climate change will bring new possibilities to amplify computational capabilities through design methodologies and innovative productions. The main objective of this research is to verify the potential use of a multi-objective algorithm to optimize educational institutional layout spaces, considering lighting and social distancing aspects. The research classified according to its finality in experimental research with the following lineation: (i) bibliographic research; (ii) ambient and params definition; (iii) developing generative models; (iv) computational simulation; (v) multi-objective optimization; and (vi) analysis of the results. The data referring to light comfort in ambient were obtained through simulations made in Rhinoceros software, plug-in Grasshopper. And data referring to social distancing in educational institutions were inserted and automatized in the model. As a result, we were verified that the optimization algorithm quickly found the best layout, considering minimal distancing between desks together with the minor solar incidence on the desks. This study will help the decision-making design process in the search for the best architectural solution.

References

FROTA, A. B.; SCHIFFER, S. R. Manual de conforto térmico: arquitetura, urbanismo. São Paulo:Studio Nobel, 2001.

GASPAR, J. A. M. O significado atribuído a BIM ao longo do tempo. 2019. 238 f. Dissertação(Mestrado em Arquitetura, Tecnologia e Cidade) - Faculdade de Engenharia Civil,Arquitetura e Urbanismo, Universidade Estadual de Campinas, Campinas, 2019.

GIL, A. C. Como elaborar projetos de pesquisa. 5. ed. São Paulo: Atlas, 2002. E-book.

GILCHRIST, A. Industry 4.0: The Industrial Internet of Things. [S. l.]: Apress, 2016. E-book.

KOLARAVIC, B.; MALKAWI A. Performative Architecture: Beyond Instrumentality. 1. Ed. NewYork, 2005.

LAMBERTS, R.; DUTRA, L.; PEREIRA, F.O.R. Eficiência energética na arquitetura. [3.ed.] Rio deJaneiro, 2014.

MARCONI, M. de A.; LAKATOS, E. M. Fundamentos de metodologia científica. 7. ed. SãoPaulo: Atlas, 2010. E-book.

MONTEIRO, Ari.; SANTOS, Eduardo. O Uso de Modelagem Generativa para Representaçãode Modulações de Alvenarias em Ferramentas BIM. In: SIGraDi 2009 – Proceedings... 13thCongress of the Iberoamerican Society of Digital Graphics, Sao Paulo, Brazil, November 16-18,2009.

OMID, Hanie; GOLABCHI, Mahmood. Survey of parametric optimization plugins in Rhinocerosused in contemporary architectural design. In: FOURTH INTERNATIONAL CONFERENCE ONMODERN RESEARCH IN CIVIL ENGINEERING, ARCHITECTURE, URBAN MANAGEMENT ANDENVIRONMENT, 21 maio 2019, Karaj. Anais... Karaj: University of Applied Science, 21 maio2019.

OXMAN, R. A Performance-based Model in Digital Design: PERFORMATIVE—Design BeyondAesthetic. Architectural Engineering and Design Management, v.3 ed. 3, p. 169-180, 1 jan.2007.

PETROV, Martin; WALKER, James. Optioneering Methods for Optimization, Methods ofexploring primary and secondary performance criteria in urban design. In: ECAADE 38, 1 set.

2020, Berlin. Proceedings... Berlin: eCAADe, 1 set. 2020. p. 29–36.

QINGSONG, Ma; FUKUDA, Hiroatsu. Parametric Office Building for Daylight and EnergyAnalysis in the Early Design Stages. In: Social and Behavioral Sciences, Urban Planning andArchitectural Design for Sustainable Development (UPADSD), Lecce. Proceedings... Lecce:Elsevier, v. 216, 6 jan. 2016, p. 818–828.

SCHMID, Aloísio. L. A Idéia de Conforto: reflexões sobre o ambiente. 1. ed.Curitiba: PactoAmbiental, 2005.

SHAN, Rudai. Integrating Genetic Algorithm with Rhinoceros and Grasshopper in WholeBuilding Energy Simulation. In: GRAND RENEWABLE ENERGY 2014, 27 jul. 2014, Tóquio.

Proceedings... Tóquio: Japan Council for Renewable Energy, 27 jul. 2014.

SHI, Xing; YANG, Wenjie. Performance-Driven Architectural Design and OptimizationTechnique from a Perspective of Architects. Automation in Construction, v. 32, p. 125–135, 1jul. 2013.

WORLD HEALTH ORGANIZATION. Considerations for school-related public health measures inthe context of COVID-19. Genebra, 10 mai. 2020. Acesso em: 18 jun. 2020YU, Wei et al. Application of Multi-Objective Genetic Algorithm to Optimize Energy Efficiencyand Thermal Comfort in Building Design. Energy and Buildings, v. 88, p. 135–143, 1 fev. 2015.

ZHANG, Anxiao et al. Optimization of Thermal and Daylight Performance of School BuildingsBased on a Multi-Objective Genetic Algorithm in the Cold Climate of China. Energy andBuildings, v. 139, p. 371–384, 15 mar. 2017.

Published

2021-11-19

How to Cite

Mayumi Shibata, Érika ., & Nakamura Barros, N. . (2021). MULTI-OBJECTIVE OPTIMIZATION ALGORITHM FOR LAYOUT OPTIMIZATION OF EDUCATIONAL INSTITUTIONAL SPACES. SIMPÓSIO BRASILEIRO DE QUALIDADE DE PROJETO DO AMBIENTE CONSTRUÍDO, 7, 1–9. https://doi.org/10.29327/sbqp2021.438102

Issue

Section

Inovações tecnológicas e de informação no desenvolvimento do projeto

Similar Articles

<< < 4 5 6 7 8 9 10 11 12 13 > >> 

You may also start an advanced similarity search for this article.