Leveraging Digital Intelligence for the Design and Fabrication of Urban Sculpture Art
DOI:
https://doi.org/10.56294/dm2025501Keywords:
Digital Intelligence, Urban Sculpture Art, Efficient Generative Adversarial Network, Smart Sensors, Feature Extraction, Principal Component AnalysisAbstract
With the rise of digital intelligent technology, its application fields are more and more extensive, including urban sculpture. This study addresses the critical factors of durability and maintenance associated with the digital components used in outdoor urban sculptures. The primary objective of this research is to employ cutting-edge digital intelligent technologies in the conceptualization and realization of urban sculpture. We introduce an innovative Efficient Generative Adversarial Network (EGAN), enhanced by fruit fly optimization (FFO), which facilitates the generation of unique patterns and designs for urban sculptures through smart sensor integration. This approach leverages a variety of data collected by smart sensors, which is subsequently preprocessed through data cleaning and normalization techniques. We apply Principal Component Analysis (PCA) for effective feature extraction, allowing for the development of intelligent digital frameworks for urban sculpture design models. Our results demonstrate that the proposed method significantly enhances design efficiency (25 hours), resolution (600 dpi), material strength (35 MPa), environmental adaptability (high), and overall durability (10 years) of urban sculpture patterns derived from smart sensor data. The digital intelligent technology-based design approach surpasses traditional methodologies in meeting the stringent standards set for urban sculpture design, thereby contributing to the future of urban art installations.
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Copyright (c) 2025 Yiming Zhang , Ajmera Mohan Singh (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
