基于AHP和AIGC的大连工业建筑遗产的应用和翻新评估
Yao Liu1, Pengjun Wu1, Xiaowen Li1
1School of Plastic Arts, Daegu University, Gyeongsan-si, Gyeongsangbukdo, South Korea.
PloS one
|October 31, 2024
概括
像稳定扩散这样的生成人工智能 (AIGC) 工具可以通过提高设计质量和效率,显著改善工业建筑遗产的翻新. 这些人工智能工具提供了创新的解决方案,尽管需要进一步开发情感和文化理解.
科学领域:
- 建筑学 建筑学 建筑学 建筑学
- 人工智能的人工智能
- 遗产保护 遗产保护 遗产保护
背景情况:
- 工业建筑遗产带来了独特的翻新挑战.
- 生成型人工智能 (AIGC) 提供了新的设计方法.
- 对遗产设计的AIGC工具的评估对于实际应用至关重要.
研究的目的:
- 探索基于AIGC的设计方案,用于工业建筑遗产的翻新.
- 在这种情况下,比较稳定扩散,中途旅行和Adobe Firefly的有效性.
- 评估AIGC对设计工作流程和质量的影响.
主要方法:
- 建立了使用分析层次过程 (AHP) 进行遗产装修的关键指标权重.
- 使用稳定扩散,中途旅行和Adobe Firefly生成设计方案.
- 使用加权评分方法评估AIGC生成的方案.
主要成果:
- 稳定扩散显示出优越的整体图像控制,而中途之旅在艺术效果方面表现出色,而Adobe Firefly提供了高效率和易用性.
- 稳定扩散获得了最高的整体分数 (6.1和6.3).
- 与传统方法相比,AIGC工具显著减少了设计工作流程和周期时间.
结论:
- AIGC工具提供创新解决方案,提高工业建筑遗产翻新效率.
- 虽然目前的AIGC在理解人类情感和文化细微差别方面存在局限性,但持续的技术进步有望更好地融入设计领域.
- 该研究强调了AIGC在更有效和更有创意的遗产保护战略方面的潜力.
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