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

作     者：Biao Zhou Yingbin Gui Xiongyao Xie Wensheng Li Qing Li 

作者机构：Key Laboratory of Geotechnical and Underground EngineeringTongji UniversityShanghaiChina Department of Geotechnical EngineeringTongji UniversityShanghaiChina China Railway Siyuan Survey and Design GroupWuhanChina 

出 版 物：《Underground Space》 (地下空间（英文）)

年 卷 期：2022年第7卷第3期

页      面：453-464页

核心收录：

学科分类：08[工学] 0814[工学-土木工程] 0823[工学-交通运输工程] 

基　　金：supported by the National Key Special Project(2018YFC0808702) National Natural Science Foundation of China(52038008) Shanghai Science and Technology Commission Innovation Action Plan(20dz1202406) 

主　　题：Underground space Visual comfort Measurable evaluation Machine learning 

摘      要：Based on human perception and machine learning methods,this study proposes a measurable method for evaluating visual comfort in underground ***,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 ***,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 *** random forest algorithm is then selected as the most effective measurable intelligent evaluation algorithm for underground ***,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 *** 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.