School of Plastic Arts, Daegu University, Gyeongsan-si, Gyeongsangbukdo, South Korea.
College of art and design, Southwest Forestry University, Kunming, Yunnan, China.
PLoS One. 2024 Oct 31;19(10):e0312282. doi: 10.1371/journal.pone.0312282. eCollection 2024.
This paper takes the example of industrial architectural heritage in Dalian to explore design scheme generation methods based on generative artificial intelligence (AIGC). The study compares the design effects of three different tools using the Analytic Hierarchy Process (AHP). It first establishes the key indicator weights for the renovation of industrial architectural heritage, with the criterion layer weights as follows: building renovation 0.230, environmental landscape 0.223, economic benefits 0.190, and socio-cultural value 0.356. Among the goal layer weights, the highest weight is for the improvement of living quality at 0.129, followed by resident satisfaction at 0.096, and educational and display functions at 0.088, while the lowest is for renovation costs at only 0.035. The design schemes are generated using Stable Diffusion, Mid Journey, and Adobe Firefly tools, and evaluated using a weighted scoring method. The results show that Stable Diffusion excels in overall image control, Mid Journey demonstrates strong artistic effects, while Adobe Firefly stands out in generation efficiency and ease of use. In the overall score, Stable Diffusion leads the other two tools with scores of 6.1 and 6.3, respectively. Compared to traditional design processes, these tools significantly shorten the design workflow and cycle, improving design quality and efficiency while also providing rich creative inspiration. Overall, although current generative artificial intelligence tools still have limitations in understanding human emotions and cultural differences, with continuous technological iteration, this method is expected to play a larger role in the design field, offering more innovative solutions for the renovation of industrial architectural heritage.
本文以大连工业建筑遗产为例，探讨基于生成式人工智能（AIGC）的设计方案生成方法。研究采用层次分析法（AHP）对三种不同工具的设计效果进行比较。首先，建立工业建筑遗产改造的关键指标权重，准则层权重分别为：建筑改造 0.230、环境景观 0.223、经济效益 0.190、社会文化价值 0.356。目标层权重中，生活质量提升权重最高为 0.129，其次是居民满意度为 0.096，教育展示功能为 0.088，最低的是改造成本仅为 0.035。使用 Stable Diffusion、Mid Journey 和 Adobe Firefly 工具生成设计方案，并采用加权评分法进行评估。结果表明，Stable Diffusion 在整体图像控制方面表现出色，Mid Journey 展示出强大的艺术效果，而 Adobe Firefly 在生成效率和易用性方面表现突出。在总体得分中，Stable Diffusion 以 6.1 和 6.3 的得分分别领先其他两个工具。与传统设计流程相比，这些工具大大缩短了设计工作流程和周期，提高了设计质量和效率，同时提供了丰富的创意灵感。总体而言，尽管当前的生成式人工智能工具在理解人类情感和文化差异方面仍存在局限性，但随着技术的不断迭代，这种方法有望在设计领域发挥更大的作用，为工业建筑遗产的改造提供更多创新解决方案。
