针对极端降雨条件下水网密集城市地下交通基础设施面临的内涝威胁，为评估城市隧道在洪涝灾害下的易损性和在灾害冲击下的失效机制，以苏州市城市隧道网络为例，基于复杂网络理论与地理信息系统技术，构建融合空间地理属性与交通流量特征的双权重道路隧道网络模型；通过建立临界淹没判据与节点失效仿真方法，量化分析极端降雨条件下城市隧道网络运行效率的动态衰减规律。结果表明： 1）苏州市道路隧道网络呈现小世界特性。2）在极端降雨初期，网络运行效率在洪水位上升至3 m前即显著下降20%以上； 地处低洼的关键隧道节点如城北快速路西隧道、星港街隧道的同步失效是道路网络衰减的主要原因； 随着洪水位的进一步升高，因隧道出入口高程存在差异，网络性能衰减趋于平缓。为有效切断失效链条，基于网络节点拓扑脆弱性与空间异质性分析结果，提出针对性的路网韧性提升策略，具体包括实施抗涝防御设施的差异化分级部署、推进地下排水系统的多维度优化，以及构建一体化智能水位监测与早期预警平台。

Underground transportation infrastructure in cities with dense river networks is at risk of waterlogging during extreme rainfall. To evaluate the flood vulnerability and failure mechanisms of urban tunnels in disaster scenarios, a case study is conducted on the tunnel network in Suzhou, China, and a dual-weighted model of the road tunnel network, incorporating spatial geographical attributes and traffic flow characteristics, is constructed using complex network theory and geographic information system technology. Critical inundation criteria and a node failure simulation method are established to quantitatively analyze the dynamic attenuation law of the operational efficiency of urban tunnel networks under extreme rainfall conditions. The results show that: (1) the road tunnel network in Suzhou displays small-world characteristics; (2) during the initial phase of extreme rainfall, operational efficiency drops by more than 20% before the water level reaches 3 m. The simultaneous failure of key low-lying tunnel nodes, such as the Chengbei Expressway West Tunnel and Xinggang Street Tunnel, primarily contributes to this decline. As the water level rises further, the degradation of network performance stabilizes due to elevation differences at tunnel entrances and exits. To effectively interrupt the failure chain, targeted road network resilience enhancement strategies are proposed based on an analysis of topological vulnerability and spatial heterogeneity of network nodes. These strategies include the differentiated and tiered deployment of flood control facilities, multidimensional optimization of underground drainage systems, and the construction of an integrated intelligent water level monitoring and early warning platform.