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Optimizing Built Environment in Urban Negative Spaces Using Parametric Methods—Research on a High-Density City in China

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ORCID

Medium: journal article
Language(s): English
Published in: Buildings, , n. 4, v. 14
Page(s): 1081
DOI: 10.3390/buildings14041081
Abstract:

In the early stage of architectural design, addressing the challenges posed by negative spaces in high-density urban environments is crucial for enhancing spatial efficiency and building sustainability. Multiple studies employed digital methods and tools to address these issues, such as parametric design, simulation, and genetic algorithms, to investigate architectural generation approaches for urban negative spaces. This article proposes an integrated design process that involves finding the location and form of negative spaces, generating solutions using slime mold and wasp algorithms, and optimizing and analyzing solutions using the Wallacei plugin in Grasshopper. This comprehensive approach underscores the potential of parametric design to yield a multitude of solutions while also acknowledging the convergence challenges encountered during simulations, particularly in optimizing for optimal sunlight exposure during the winter solstice and minimal solar radiation in the summer. Analyzing the optimization goals and parameter values of the 15th Pareto optimal solution in the 100th generation reveals: (1) a higher number of units leads to positive correlation growth in both objectives; (2) within a certain number of units, parametrically generated solutions facilitate the convergence of optimization goals, yielding optimal outcomes. Therefore, factors such as the range of unit quantities and proportions need consideration during early-stage parametric design and simulation. This study explores a design methodology for negative spaces in high-density urban cities, validating the feasibility of various mainstream generation methods and offering insights for future research.

Copyright: © 2024 by the authors; licensee MDPI, Basel, Switzerland.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10773690
  • Published on:
    29/04/2024
  • Last updated on:
    05/06/2024
 
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