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Recent Advances in Intelligent Applications of Well-Being Spaces Design and...
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Recent Advances in Intelligent Applications of Well-Being Spaces Design and Engineering

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Energy, Physics, Environment, and Systems".

Deadline for manuscript submissions: 30 October 2025 | Viewed by 2327

Special Issue Editors

Prof. Dr. Guodan Liu

E-Mail Website
Guest Editor
School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266033, China
Interests: new energy development; energy efficiency technology for buildings; thermal environments and indoor human thermal comfort
Prof. Dr. Hongbing Chen

E-Mail Website
Guest Editor
School of Environment and Energy Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Interests: comprehensive utilization of solar energy; nano phase change fluids; heat and mass transfer in porous media; green buildings and building energy efficiency
Dr. Sheng Miao

E-Mail Website
Guest Editor
School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China
Interests: industrial Internet of Things technology; data mining and deep learning; artificial intelligence systems; digital twins

Special Issue Information

Dear Colleagues,

Well-being spaces are intentionally designed environments that promote mental, emotional, and physical health. These spaces can be found in various settings, including workplaces, healthcare facilities, community centers, and educational institutions. They often incorporate natural elements, comfortable furnishings, and thoughtful layouts to foster relaxation, social interaction, and overall wellness. With the rapid development of information technology, data science, and computer technology, various novel approaches have been introduced into the field. In particular, artificial intelligence (AI) can be used to enhance well-being space design and engineering by providing data-driven insights that inform user-centered design choices, enabling personalization through adaptive environments that respond to individual preferences. It employs predictive analytics to anticipate usage patterns, helping create flexible layouts and optimize resource efficiency. Additionally, AI-powered simulation tools allow designers to visualize the impact of changes, while smart technology integration fosters user interaction through wellness-promoting features, ultimately contributing to more effective, sustainable, and supportive environments.

This Special Issue aims to reflect the current state of the art and new developments in the application of artificial intelligence, machine learning, and data-driven applications for air quality, water quality, thermal comfort, solar radiation, energy planning and policy, and other topics relevant to well-being spaces. This Special Issue will provide a comprehensive and interdisciplinary avenue for researchers, experts, and engineers to publish their latest findings. We welcome original research articles, case studies, and reviews focusing on, but not limited to, the following topics:

  • Artificial intelligence as applied to well-being spaces;
  • Indoor/outdoor building environmental assessment and simulation;
  • Healthy and comfortable built environments;
  • Green buildings and sustainable systems based on computer science;
  • Advances in modelling and simulation tools;
  • Data mining in well-being space datasets;
  • Machine learning applications for energy management;
  • Predictive maintenance and assessment;
  • Renewable energy applications;
  • Modeling in health, productivity, and well-being related to the built environment.

Prof. Dr. Guodan Liu
Prof. Dr. Hongbing Chen
Dr. Sheng Miao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • well-being spaces
  • artificial intelligence (AI)
  • healthy and comfortable built environment
  • data mining and machine learning
  • renewable energy applications

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

35 pages, 13096 KiB  
Article
Impact of Streetscape Built Environment Characteristics on Human Perceptions Using Street View Imagery and Deep Learning: A Case Study of Changbai Island, Shenyang
by Xu Lu, Qingyu Li, Xiang Ji, Dong Sun, Yumeng Meng, Yiqing Yu and Mei Lyu
Buildings 2025, 15(9), 1524; https://doi.org/10.3390/buildings15091524 - 1 May 2025
Viewed by 95
Abstract
Since the reform and opening-up policy, the accelerated urbanization rate has triggered extensive construction of new towns, leading to architectural homogenization and environmental quality degradation. As urban development transitions toward a “quality improvement” paradigm, there is an urgent need to synergistically enhance the [...] Read more.
Since the reform and opening-up policy, the accelerated urbanization rate has triggered extensive construction of new towns, leading to architectural homogenization and environmental quality degradation. As urban development transitions toward a “quality improvement” paradigm, there is an urgent need to synergistically enhance the health performance of human settlements through the optimization of public space environments. The purpose of this study is to explore the impact of the built environment of urban streets on residents’ perceptions. In particular, in the context of rapid urbanization, how to improve the mental health and quality of life of residents by improving the street environment. Changbai Island Street in the Heping District of Shenyang City was selected for the study. Baidu Street View images combined with machine learning were employed to quantify physical characterizations like street plants and buildings. The ‘Place Pulse 2.0’ dataset was utilized to obtain data on residents’ perceptions of streets as beautiful, safe, boring, and lively. Correlation and regression analyses were used to reveal the relationship between physical characteristics such as green visual index, openness, and pedestrians. It was discovered that the green visual index had a positive effect on perceptions of it being beautiful and safe, while openness and building enclosure factors influenced perceptions of it being lively or boring. This study provides empirical data support for urban planning, emphasizing the need to focus on integrating environmental greenery, a sense of spatial enclosure, and traffic mobility in street design. Optimization strategies such as increasing green coverage, controlling building density, optimizing pedestrian space, and enhancing the sense of street enclosure were proposed. The results of the study not only help to understand the relationship between the built environment of streets and residents’ perceptions but also provide a theoretical basis and practical guidance for urban space design. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Applications of Well-Being Spaces Design and Engineering)
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28 pages, 12802 KiB  
Article
Evaluating and Diagnosing Urban Function and Perceived Quality Based on Multi-Source Data and Deep Learning Using Dalian as an Example
by Yumeng Meng, Mei Lyu, Dong Sun, Jiaxuan Shi and Hiroatsu Fukuda
Buildings 2025, 15(7), 998; https://doi.org/10.3390/buildings15070998 - 21 Mar 2025
Viewed by 378
Abstract
Currently, coordinated development in terms of perceived urban quality and function has become a key problem. However, there is an imbalance between the street environment and urban amenities. It is necessary to explore the current status and propose optimization strategies to promote the [...] Read more.
Currently, coordinated development in terms of perceived urban quality and function has become a key problem. However, there is an imbalance between the street environment and urban amenities. It is necessary to explore the current status and propose optimization strategies to promote the coordinated development of urban spaces. Dalian, China, was selected as the study area. Based on space syntax, high-accessibility and low-accessibility streets were selected as study sites. An evaluation system was constructed as part of the study. It included the urban function and perceived street quality. Data on the density and diversity of urban amenities were obtained by establishing points of interest (POIs). The subjective and psychological perception of quality was calculated using street view images (SVIs). Then, a coupling analysis based on the urban function and perceived quality was conducted as part of the study. The results indicated that there were differences in the development levels of urban amenities and in regard to spatial quality in Dalian. Specifically, high-accessibility streets and urban amenities were mainly concentrated in the central urban area. The perceived quality of high-accessibility streets was higher than low-accessibility streets. The coupling analysis found that high-accessibility and low-accessibility streets had the highest proportions of advantage streets and opportunity streets. The urban amenities and subjective perception of quality were the highest in regard to advantage streets. The perception of beauty was the lowest in regard to maintenance streets. The psychological perception was the highest among improvement streets. Openness was the highest in regard to opportunity streets. As a result of the coupling analysis, this study not only helps to optimize the layout of urban amenities and improve the quality of the street environment, but also provides practical guidance for future urban design. Additionally, the results of this study will help to promote the coordinated development of street environments and urban amenities and enhance the overall livability and spatial quality of the urban environment. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Applications of Well-Being Spaces Design and Engineering)
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24 pages, 4196 KiB  
Article
Impact of Physical Features on Visual Walkability Perception in Urban Commercial Streets by Using Street-View Images and Deep Learning
by Gonghu Huang, Yiqing Yu, Mei Lyu, Dong Sun, Bart Dewancker and Weijun Gao
Buildings 2025, 15(1), 113; https://doi.org/10.3390/buildings15010113 - 31 Dec 2024
Viewed by 1447
Abstract
Urban commercial streets are a crucial component of urban life, serving as the central hubs of commercial activity and providing vital spaces for both residents and visitors to engage in various activities. Walkability is commonly used as a key indicator of environmental quality, [...] Read more.
Urban commercial streets are a crucial component of urban life, serving as the central hubs of commercial activity and providing vital spaces for both residents and visitors to engage in various activities. Walkability is commonly used as a key indicator of environmental quality, playing a significant role in improving residents’ health, community interaction, and environmental quality of life. Therefore, promoting the development of a high-quality walking environment in commercial districts is crucial for fostering urban economic growth and the creation of livable cities. However, existing studies predominantly focus on the impact of the built environment on walkability at the urban scale, with limited attention given to commercial streets, particularly the influence of their physical features on walking-need perceptions. In this study, we utilized Google Street-View Panorama (GSVP) images of the Tenjin commercial district and applied the Semantic Differential (SD) method to assess four walking-need perceptions of visual walkability perception, including usefulness, comfort, safety, and attractiveness. Additionally, deep-learning-based semantic segmentation was employed to extract and calculate the physical features of the Tenjin commercial district. Correlation and regression analysis were used to investigate the impact of these physical features on the four walking-need perceptions. The results showed that the different walking-need perceptions in the Tenjin commercial district are attractiveness > safety > comfort > usefulness. Furthermore, the results show that there are significant spatial distribution differences in walking-need perceptions in the Tenjin commercial district. Safety perception is more prominent on primary roads, all four walking-need perceptions in the secondary roads at a high level, and the tertiary roads have generally lower scores for all walking-need perceptions. The regression analysis indicates that walkable space and the landmark visibility index have a significant impact on usefulness, street cleanliness emerges as the most influential factor affecting safety, greenness is identified as the primary determinant of comfort, while the landmark visibility index exerts the greatest influence on attractiveness. This study expands the existing perspectives on urban street walkability by focusing on street-level analysis and proposes strategies to enhance the visual walkability perception of commercial streets. These findings aim to better meet pedestrian needs and provide valuable insights for future urban planning efforts. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Applications of Well-Being Spaces Design and Engineering)
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