A measurable evaluation method of visual comfort in underground space by intelligent sorting and classification algorithms

• Published in: Underground space (Beijing) 2022-06, Vol.7 (3), p.453-464
• Main Authors: Zhou, Biao, Gui, Yingbin, Xie, Xiongyao, Li, Wensheng, Li, Qing
• Format: Article
• Language: English
• Subjects: Machine learning; Measurable evaluation; Underground space; Visual comfort
• ISSN: 2467-9674; 2467-9674
• DOI: 10.1016/j.undsp.2021.10.004

Based on human perception and machine learning methods, this study proposes a measurable method for evaluating visual comfort in underground spaces. First, a comfort evaluation index based on the characteristics of human visual perception is proposed, and color features and segmentation extraction methods for intelligent methods are given. Then, using probability statistics and machine learning methods, a multi-class intelligent sorting and classification algorithm for ranking visual comfort levels is constructed and a comparison is made of the suitability of different intelligent methods for evaluating visual comfort. The random forest algorithm is then selected as the most effective measurable intelligent evaluation algorithm for underground spaces. Finally, the proposed method is compared to intelligent methods employed by previous research, and a case study, the Wujiaochang underground space in Shanghai, China, is applied as the background. Results show that the proposed method can effectively improve the quantification and refinement of human perception and evaluation of comfort in underground spaces; this method will also be useful in computer-aided generative design in the future.