Department of Civil Engineering, University of Birmingham, Birmingham, B15 2TT, UK.
Sci Rep. 2023 Feb 6;13(1):2106. doi: 10.1038/s41598-023-29292-7.
The increase in demand for railway transportation results in a significant need for higher train axle load and faster speed. Weak and sensitive trackforms such as railway switches and crossings (or called 'turnout') can suffer from such an increase in either axle loads or speeds. Moreover, railway turnout supports can deteriorate from other incidences due to extreme weather such as floods which undermine cohesion between ballast leading to ballast washaway or loss of support under turnout structures. In this study, new intelligent automation based on machine learning pattern recognition has been built to detect and predict the deterioration of railway turnouts exposed to flooding conditions which is the scope of this study. Since the turnout system is very complex by nature, different features and smart filtering are explored to find the potential features for deep learning. Nonlinear finite element models validated by actual field measurements are used to mimic the dynamic behaviors of turnout supports under flooding conditions. The study exhibits that the novel recognition model can achieve more than 98% accuracy, yielding the potential capability to recognize and classify turnout support deteriorations facing extreme weather conditions which will be beneficial for responsible parties to schedule and plan maintenance activities.
铁路运输需求的增加导致对更高的列车轴重和更快的速度的需求显著增加。诸如铁路道岔和交叉（或称为“转辙器”）等薄弱和敏感的轨道结构可能会因轴重或速度的增加而受到影响。此外，由于洪水等极端天气，铁路转辙器的支撑物会因其他事件而恶化，导致道床之间的粘结力下降，导致道床冲刷或转辙器结构下的支撑物丧失。在这项研究中，基于机器学习模式识别的新智能自动化系统已经建立，用于检测和预测暴露于洪水条件下的铁路道岔的恶化情况，这是本研究的范围。由于道岔系统本质上非常复杂，因此探索了不同的特征和智能过滤方法，以找到适用于深度学习的潜在特征。经过实际现场测量验证的非线性有限元模型被用于模拟洪水条件下转辙器支撑的动态行为。研究表明，新型识别模型的准确率超过 98%，具有识别和分类极端天气条件下转辙器支撑物劣化的潜力，这将有助于责任方安排和规划维护活动。
