School of Design, Shunde Polytechnic, Shunde District, Foshan City, Guangdong Province, China.
Faculty of Humanities and Arts, Macau University of Science and Technology, Taipa, Macau, China.
PLoS One. 2024 Nov 7;19(11):e0310594. doi: 10.1371/journal.pone.0310594. eCollection 2024.
The floor plan layout of museum exhibition spaces is the skeleton network of the museum, which determines the internal circulation and spatial form of the museum. This paper studies the method and practice of using artificial intelligence technology to assist in the space design of exhibition halls in urban cultural museums. First, it introduces the limitations of traditional space design methods for exhibition halls in urban cultural museums and the superiority and application prospects of the CGAN (conditional generative adversarial network) model in space design. Second, the principle and training process of the CGAN model are explained in detail, and the experimental results and analysis are given. By learning 100 floor plans of exhibition halls of urban culture museums, the CGAN model can generate a new floor plan design for an exhibition hall, which provides a new idea and innovative method for this design task. Finally, the limitations and future research directions of the CGAN model in the space design of urban cultural museum exhibition halls are discussed. The study shows that using the CGAN model to learn the floor plans of exhibition halls of urban cultural museums can effectively improve the innovation and practicability of space design and has the following advantages: (1) It can quickly generate a large number of exhibition hall floor plans, shorten the design cycle, and improve design efficiency. (2) The generated floor plan designs of the exhibition hall are diverse and personalized, meeting the design requirements of different scenarios and needs. (3) The method promotes the deep integration of space design and artificial intelligence technology and provides new possibilities and ideas for space design. These conclusions provide new ideas and methods for the space design of exhibition halls of urban cultural museums and provide a reference and inspiration for space design and intelligent applications in other fields, such as office space design, home decoration space design, landscape space design, and historical arcade and building renovation design.
博物馆展览空间的平面布局是博物馆的骨架网络,决定了博物馆的内部流线和空间形式。本文研究了利用人工智能技术辅助城市文化博物馆展厅空间设计的方法和实践。首先,介绍了传统城市文化博物馆展厅空间设计方法的局限性和 CGAN(条件生成对抗网络)模型在空间设计中的优势和应用前景。其次,详细解释了 CGAN 模型的原理和训练过程,并给出了实验结果和分析。通过学习 100 个城市文化博物馆展厅的平面图,CGAN 模型可以为展厅生成新的平面图设计,为这项设计任务提供了新的思路和创新方法。最后,讨论了 CGAN 模型在城市文化博物馆展厅空间设计中的局限性和未来研究方向。研究表明,利用 CGAN 模型学习城市文化博物馆展厅的平面图,可以有效提高空间设计的创新性和实用性,具有以下优点:(1)可以快速生成大量的展厅平面图,缩短设计周期,提高设计效率。(2)生成的展厅平面图设计多样且个性化,满足不同场景和需求的设计要求。(3)该方法促进了空间设计与人工智能技术的深度融合,为空间设计和其他领域的智能应用提供了新的可能性和思路,如办公空间设计、家居装饰空间设计、景观空间设计、历史街区和建筑改造设计等。