Flood simulation and risk assessment in urban underground spaces based on 3D laser scanning: capacity–depth–damage curves and computational fluid dynamics-based flood response

Urban underground spaces are rapidly expanding, but their low elevation, limited drainage capacity, and strong enclosure make them highly vulnerable to pluvial flooding. To elucidate how inundation dynamics in 3D underground spaces under extreme rainfall translate into actionable risk indicators (e.g., depth thresholds and arrival time), we propose and cross-validate a rainfall-informed capacity–depth–damage (C–D–D) curve method and a physics-based computational fluid dynamics (CFD) inundation model. The first approach is a rainfall-informed C–D–D curves method that rapidly maps net inflow to depth evolution and warning indicators (e.g., threshold depth and arrival time). The second approach is a 3D-geometry-resolved CFD inundation model that simulates spatially distributed depths/flows under prescribed inflow and drainage/outlet conditions, providing high-fidelity validation and hazard maps. A GeoSLAM handheld 3D Laser Scanning system was used to reconstruct as-built, modeling-ready 3D geometry of the underground space, addressing the common limitation of idealized layouts in prior evacuation-time assessments and enabling geometry-specific inundation and warning-threshold predictions. Using an underground parking garage in Tongzhou District, Beijing as a case study, we evaluated flood dynamics and risks under rainfall scenarios with annual exceedance probabilities of 1%, 2%, and 5%. Results show that stronger rainfall significantly advances critical water-depth thresholds and compresses evacuation windows; for example, under P = 1%, the 0.2 m alert occurs 1.5 h earlier than under P = 2% and 5.3 h earlier than under P = 5%. The two methods exhibit strong consistency in threshold timing (typically within 0–1 h), while CFD resolves spatial heterogeneity and identifies medium-to-high risk zones earlier in the intrusion stage. This integrated framework supports rapid early warning, evacuation-window assessment, entrance protection, and drainage-capacity design. Novelty lies in (i) integrating handheld 3D Laser Scanning with a “curve-first, CFD-refine” dual-model workflow; (ii) cross-validating fast C–D–D-based warning thresholds against geometry-resolved CFD dynamics; and (iii) delivering actionable time-to-threshold warnings and spatial risk maps for emergency planning.