基于车辆 - 轨道振动数据,智能识别无压轨道次级结算
1Department of Transportation, Inner Mongolia University, Hohhot, 010020, China.
Scientific reports
|July 2, 2025
概括
一个新的深度学习模型使用车辆-铁路动态准确地检测无压轨道中不均的下层沉积. 这种先进的方法 (CNN-LSTM) 达到99.26%的准确性,改善了基础设施监控.
科学领域:
- 土木工程 土木工程是指土木工程.
- 地质技术工程 地质技术工程
- 人工智能的人工智能
背景情况:
- 监测在无压轨道上的下层沉积,在目前的方法中是困难的.
- 现有的技术缺乏准确性和连续性.
- 不均的结算对轨道稳定性和安全性构成风险.
研究的目的:
- 开发一种新的深度学习方法,用于准确地检测次级结算.
- 分析车辆-轨道动态响应以识别结算.
- 增强无压轨道系统的智能监控.
主要方法:
- 开发了一个卷积神经网络长期短期记忆 (CNN-LSTM) 模型.
- 一个车辆-轨道-底层合模型模拟了振动响应.
- 车身垂直加速,点头角速度和轨道位移被确定为关键输入.
主要成果:
- 该CNN-LSTM模型实现了99.26%的检测准确度.
- 混合深度学习方法的表现优于基准模型 (BP,RBF,CNN,LSTM).
- 组合的车辆-轨道反应显著改善了轨道-底层变形不匹配的识别.
结论:
- 拟议的CNN-LSTM模型提供了一个强大的和有效的解决方案,用于监测次级结算.
- 这项研究为智能轨道监控提供了理论见解和实践指南.
- 这些发现有助于加强无压轨道的安全和维护策略.
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