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Public Art and Space Environment Design Using Genetic Algorithm-Guided 3D Virtual Reconstruction.

Man Luo1

  • 1School of Art and Design, Shanghai University of Engineering Science, Shanghai 201620, China.

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Public art enhances urban spaces by reflecting city identity. A new genetic algorithm optimizes public space design, achieving 94.87% efficiency and high resident satisfaction.

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Area of Science:

  • Urban planning and design
  • Public art studies
  • Computational design

Background:

  • Public art significantly shapes urban environments, reflecting cultural heritage.
  • Understanding the interplay between public art and urban space design is crucial for city development.
  • Contemporary trends in public art and space design require innovative approaches.

Purpose of the Study:

  • To examine the relationship between public art and public space environments.
  • To explore virtual reconstruction requirements for 3D environments.
  • To propose and validate a genetic algorithm for optimizing urban space design.

Main Methods:

  • Analysis of public art types, status, and impact on urban spaces.
  • Investigation of virtual process requirements using collaborative software for 3D reconstruction.
  • Development and experimental validation of a genetic algorithm for space environment optimization.

Main Results:

  • The proposed genetic algorithm achieved a high efficiency of 94.87%.
  • Experimental results confirmed the algorithm's viability and effectiveness.
  • The optimized design approach led to positive resident feedback and high satisfaction scores.

Conclusions:

  • The genetic algorithm provides a valid and efficient method for optimizing urban space design.
  • Integrating public art considerations into space design positively impacts urban environments and resident experience.
  • This research contributes to the advancement of public art and urban space environment design for city growth.