arXiv subjects

Zhongqi Yu

Publications and source records attributed to Zhongqi Yu.

2 recordsLinked to original sources

arXiv2025

Quantifying Key Design Factors for Thermal Comfort in Underground Space Through Global Sensitivity Analysis and Machine Learning

This study identified the key design factors related to thermal comfort in naturally ventilated underground spaces under high temperature conditions (outdoor Tmax = 42.9 C) in Fuzhou, China. Fuzhou has a humid subtropical climate and is one of the three hottest cities in China in 2024. Daytime measurements indicated reduced air temperature (AT), mean radiant temperature (MRT), and wind speed (V), together with elevated relative humidity (RH) in the underground space. Physiological Equivalent Temperature (PET) in the underground was consistently lower during peak hours (08:00-16:00), with the maximum difference in PET between pedestrian and underground levels being 11-11.9 C. Higher pedestrian-level PET at L1 was attributed to reduced greenery and shading, and decrement factors indicated greater thermal dampening at L2 (0.197) than at L1 (0.308). Sensitivity analysis showed that MRT was the most influential factor (S1/ST = 0.59-0.72) for aboveground spaces, followed by AT (0.13-0.26). In contrast, underground PET was mainly affected by metabolic rate (MET) (0.63-0.65), followed by RH (0.14-0.20) and V (0.08-0.18). Partial dependence analysis revealed that a 1 met increase in MET raised PET by 1.6 C, whereas a 1 m/s increase in V reduced PET by 1.5-2.2 C in the underground space. Due to cooler and more stable thermal conditions, underground spaces have higher tolerance for intensive physical activities. By buffering fluctuations in AT and MRT, underground environments can significantly alleviate heat stress and provide passive cooling shelter during daytime heat waves. Overall, this study provides empirical evidence to support underground space design in hot-humid climates and offers insights for sustainable urban heat mitigation.

physics.app-ph↗

arXiv2019

Empirical model of campus air temperature and urban morphology parameters based on field measurement and machine learning in Singapore

The rising air temperature caused by Urban Heat Island (UHI) effect has become a problem for Singapore, it not only affects the thermal comfort of outdoor microclimate environment, but also increases the cooling energy consumption of buildings. As part of a multiscale and multi-physics urban microclimate model, weather stations were installed at 15 points within kent ridge campus of National University of Singapore (NUS) and continuously recorded the microclimate data from February 2019 to May 2019. A Geographical Information System (GIS) map and 3D model were constructed for extracting urban morphology parameters such as BDG, PAVE, WALL and HBDG. Through a site survey, SVF and GnPR were calculated. By using multi-criteria linear regression and machine learning, this research investigated five regression models for prediction of outdoor air temperature including linear regression (LR), k-nearest neighbours (KNN), support vector regression (SVR), decision tree (DT) and random forests (RF). The analysis of variables by best subsets regression showed greenery played crucial role in the mitigation of both daytime and night-time UHI. Pedestrian level wind flow was helpful in heat release in the daytime. High-rise buildings provided self-shadowing to reduce ambient air temperature but higher SVF was harmful to heat release in the night-time. For regression models, RF had the best predictive performance. Average RMSE of RF was reduced by 4% to 29% compared to linear regression. The learning curve indicated that the predictive power of LR could not be improved by additional data provision. In contrast, the downward trend in bias and variance suggested that RF can benefit from the training of big data. During the deployment of learning algorithms, RF continued to outperform other learning algorithms.

physics.soc-ph↗