Abstract:

To investigate the risk of waterlogging hazards in underground spaces, taking 3 980 underground space samples from Beijing's central urban area as the research subject, the Zipf distribution was employed to characterize the non-normal long-tail distribution features of their scale areas. A vulnerability measurement model for underground space waterlogging based on multi-source data fusion was constructed. The dynamic evolution patterns of risks under varying inundation threshold scenarios were quantified, and the influence mechanisms of distance sensitivity (k), population density (p), and usage importance coefficient (c) on vulnerability were elucidated. Differentiated prevention and control measures were proposed for underground spaces with different purposes. The results show that the risk of waterlogging disasters in Beijing's central urban underground spaces exhibits a core-periphery differentiation characteristic. As the inundation height threshold increases from 0.2 m to 0.8 m, the spatial distribution of risks evolves from point-like aggregation to band-like extension. The disaster resistance capacity of low-risk underground spaces diminishes rapidly with increasing inundation height thresholds. The straight-line distance is significantly negatively correlated with waterlogging vulnerability, whereas population density shows a significant positive correlation. With the rise in the usage importance coefficient, the risk associated with multi-functional underground spaces increases markedly. Engineering measures aimed at eliminating surrounding water accumulation points and reducing human activities in these areas during urban flooding can significantly mitigate risks.

Key words: underground space, waterlogging disaster, vulnerability measurement model, inundation height threshold, distance sensitivity, usage importance coefficient