Solid mechanics and AI hybrid approach to mitigation of climate change driven railway track buckling

铁路网络的一个重要问题是防止在炎热天气下轨道屈曲，特别是在气候变化导致极端和多变条件的情况下。屈曲风险意味着列车运行速度缓慢，降低了网络容量，并带来了糟糕的客户体验。穿越弯曲轨道可能导致脱轨，造成严重的安全后果。数据显示，在特定的赛道条件下，赛车更普遍。个别不重要的因素组合在一起会导致弯曲，而没有明显的原因。影响因素可能具有随机性，包括组件、安装、使用年限、装载历史或其他因素的变化。铁路网络上的大规模数据收集为人工智能（AI）方法提供了可能性，以配合传统力学来理解铁路基础设施。谢菲尔德提出了一种考虑轨道系统为约束梯形结构的钢轨屈曲分析力学模型。在这项研究中，预计这将扩展与有限元模型，以更全面地捕捉现实（和随机）的行为。除此之外，还将利用铁路网络数据开发屈曲的AI模型。一个特定的模糊集为基础的方法已被证明提供快速可靠的预测（在替代情况下95-97%的准确度）估计因素影响结果显着。学术主管和工业界对研究的共同支持将提供一个难得的机会，利用最新的想法解决研究问题，并有可能带来改善铁路系统性能的真实的好处。

A significant problem for rail networks is prevention of track buckling in hot weather, particularly with climate change leading to extreme and variable conditions. Buckling risk means trains run at slow speeds, reducing network capacity and giving poor customer experience. Traversing buckled track can lead to derailment with severe safety consequences. Data shows buckles are more prevalent for specific track conditions. Individually insignificant factors occur in combination leading to a buckle without an obvious cause. Contributory factors may have stochastic nature through variability in components, installation, their age, loading history or other factors.Large-scale data collection on the railway network is opening the possibility of Artificial Intelligence (AI) approaches to accompany conventional mechanics in understanding rail infrastructure. An analytical mechanics model of rail buckling considering the track system as a restrained ladder structure is available in Sheffield. In this research it is anticipated this will be extended with a finite element model to more fully capture realistic (and stochastic) behaviour. Alongside this an AI model of buckling will be developed with rail network data. A particular fuzzy-set based methodology has proven to offer fast reliable predictions (in alternative cases 95-97% accuracy) estimating factors influencing results significantly. The joint support of the research by academic supervisors and industry will provide an exceptional opportunity to address the research problem using the latest ideas with potential to deliver real benefits of improved rail system performance.