Modeling results.

This research examines the seismic hazard impact on railway infrastructure along the U.S. West Coast (Washington, Oregon and California), using machine learning to explore how measures of seismic hazard such as fault density, earthquake frequency, and ground shaking relate to railway infrastructure accidents. By comparing linear and non-linear models, it finds non-linear approaches superior, particularly noting that higher fault densities and stronger peak ground shaking correlate with increased infrastructure accident rates. Shallow earthquakes with magnitudes of 3.5 or greater and hypocentral depths <20 km also exhibit a pronounced correlation with the incidence of railway infrastructure accidents The study extends to financial impact analysis through Net Present Value and Monte Carlo Simulation, and evaluates damage costs from 2000–2023 to guide financial planning and risk management strategies. It highlights the crucial role of advanced financial tools in optimizing maintenance and long-term planning that could result in better preparedness in high seismic hazard regions and emphasizes the need for robust risk management strategies in enhancing railway operational safety that considers the local and regional tectonic and seismic activity and local ground shaking intensity.

本研究以美国西海岸（华盛顿州、俄勒冈州与加利福尼亚州）沿线铁路基础设施为研究对象，探讨地震灾害对其造成的影响；借助机器学习（Machine Learning）方法，分析断层密度、地震频次、地面震动等地震灾害表征指标与铁路基础设施事故间的关联关系。通过对比线性与非线性模型，本研究发现非线性模型表现更优，尤其指出更高的断层密度与更强的峰值地面震动，与铁路基础设施事故率上升呈显著正相关。震级不低于3.5级、震源深度小于20公里的浅源地震，同样与铁路基础设施事故发生率存在显著关联。本研究进一步通过净现值（Net Present Value）与蒙特卡洛模拟（Monte Carlo Simulation）开展财务影响分析，对2000至2023年的灾害损失成本进行评估，以此为铁路财务规划与风险管理策略提供指导。本研究强调了先进财务工具在优化运维与长期规划中的关键作用，可助力高地震灾害风险区域提升灾害应对准备水平；同时指出，为提升铁路运营安全性，需构建完善的风险管理策略，充分考量局地与区域的构造活动、地震活动特征以及局地地面震动强度。