Abstract
With the acceleration of urbanisation and the increased utilisation of underground space, providing a comfortable and healthy environment in public underground areas has emerged as a significant research topic. This study constructs a comprehensive decision-making framework for underground space environments by integrating human perception evaluations with physical environmental parameters. Using Shanghai Wujiaochang as a case study, field data collection and questionnaire surveys were conducted to evaluate key factors such as temperature (22.63 °C–26.39 °C), wind speed (0.26 m/s–0.67 m/s), and sound levels (59.68 dB–61.21 dB) for commercial-oriented spaces, and 63.15 dB–75.45 dB for transport-oriented spaces) to users’ perceived experiences. The appropriate ranges for key parameters were identified through single-indicator fitted regression analysis and the XGBoost machine-learning model, revealing the relationship between environmental parameters and human perception. The results indicated significant differences in user needs across various functional spaces, with commercial-oriented areas emphasising environmental attractiveness and comfort, while transport-oriented spaces prioritised access efficiency and safety. This study provided quantitative design benchmarks for underground spaces’ dynamic regulation and sustainable management, proposing a precise and adaptive environmental decision-making framework that combines physical parameters with user-perception feedback.
| Original language | English |
|---|---|
| Article number | 204 |
| Journal | Buildings |
| Volume | 15 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jan 2025 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- environmental regulation
- machine learning
- multidimensional perception
- suitability parameters
- underground space
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