Faculty of Engineering, Rajamangala University of Technology Thanyaburi, Pathum Thani, Thailand.
Department of Industrial Engineering, Rajamangala University of Technology Thanyaburi, Pathum Thani, Thailand.
PLoS One. 2024 Aug 12;19(8):e0308255. doi: 10.1371/journal.pone.0308255. eCollection 2024.
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.
本研究考察了美国西海岸（华盛顿州、俄勒冈州和加利福尼亚州）铁路基础设施的地震灾害影响，利用机器学习方法探讨了地震灾害指标（如断层密度、地震频率和地面震动）与铁路基础设施事故之间的关系。通过比较线性和非线性模型，发现非线性方法更优，特别是注意到较高的断层密度和较强的峰值地面震动与基础设施事故率的增加相关。震级为 3.5 或更大且震源深度<20 公里的浅层地震也与铁路基础设施事故的发生有明显的相关性。研究通过净现值和蒙特卡罗模拟扩展到财务影响分析，并评估了 2000 年至 2023 年的损害成本，以指导财务规划和风险管理策略。它强调了先进的财务工具在优化维护和长期规划方面的关键作用，这可能会在高地震灾害地区提高准备水平，并强调需要制定稳健的风险管理策略，以增强铁路运营安全，考虑到当地和区域构造和地震活动以及当地地面震动强度。
