卡斯卡迪亚俯冲带地震中液化引发的路网级道路中断
Network-Scale Road Disruption from Liquefaction in Cascadia Subduction Zone Earthquakes
AI总结：
该研究构建力学数据驱动框架，模拟卡斯卡迪亚9级地震液化引发的路网中断，经空间蒙特卡洛模拟评估影响，为当地交通韧性规划提供可靠工具。
AI中文摘要：
本文提出了一种力学驱动、数据驱动的框架，用于模拟卡斯卡迪亚俯冲带（CSZ）发生9级地震后液化引发的路网中断。液化灾害采用基于超过37000次静力触探试验（CPT）训练的地理空间液化模型进行预测，该模型以描述地貌、水文、气候和表层地质的空间数据为条件。地面运动来自基于物理的CSZ破裂场景集合。利用经验推导的脆弱性关系估计路段封闭和服务降级的概率，并通过空间相关的蒙特卡洛模拟在国家公路网中传播。结果显示，影响高度集中在沿海低地、河口和河谷，美国101号公路沿线暴露显著。对华盛顿州太平洋县和格雷港县的重点分析显示，医疗隔离概率升高。与之前基于地质筛选的全州评估相比，仅因液化导致的预测桥梁封闭数量低一个数量级，这反映了对地下条件和破坏机制的更准确表征。尽管存在不确定性，该框架为卡斯卡迪亚的交通韧性规划和资产优先级排序提供了可靠工具。
英文摘要：
This paper presents a mechanics-informed, data-driven framework for modeling liquefaction-induced disruption of roadway networks following a magnitude-9 earthquake on the Cascadia Subduction Zone (CSZ). Liquefaction hazard is predicted using a geospatial liquefaction model trained on more than 37,000 cone penetration tests (CPTs) and conditioned on spatial data describing geomorphology, hydrology, climate, and surficial geology. Ground motions are derived from physics-based ensembles of CSZ rupture scenarios. Segment-level probabilities of closure and service degradation are estimated using empirically derived fragility relationships and propagated through the National Highway System using spatially correlated Monte Carlo simulation. Results indicate strong concentration of impacts in coastal lowlands, estuaries, and river valleys, with pronounced exposure along U.S. Route 101. Focused analysis in Pacific and Grays Harbor Counties, Washington, shows elevated probability of healthcare isolation. Compared with prior statewide assessments based on geologic screening, predicted bridge closures due solely to liquefaction are an order of magnitude lower, reflecting improved representation of subsurface conditions and damage mechanisms. Despite uncertainties, the framework provides a defensible tool for transportation resilience planning and asset prioritization in Cascadia.
