预测铁路运营基础设施条件恶化的情况
Juan de Dios Sanz Bobi1, Pablo Garrido Martínez-Llop2, Pablo Rubio Marcos3
1Department of Mechanical Engineering, Universidad Politécnica de Madrid-UPM, 28006 Madrid, Spain.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
这项研究提出了一种基于人工智能的铁路桥梁预测性维护方法,使用机器学习来检测气候变化影响. 它优化了传感器的位置,以实现经济高效的监控和交通安全决策.
科学领域:
- 土木工程 土木工程是指土木工程.
- 人工智能的人工智能
- 环境科学 环境科学
背景情况:
- 气候变化通过极端天气事件对铁路基础设施构成重大风险.
- 维护铁路基础设施对于安全可靠的客运至关重要.
- 目前用于大型基础设施的监控方法往往成本过高.
研究的目的:
- 提出基于人工智能的预测性维护方法,用于早期检测极端气候条件引起的桥梁退化.
- 通过优化传感器数量来开发一个具有成本效益的铁路基础设施监控策略.
- 建立基于基础设施状况的交通安全决策门.
主要方法:
- 实现人工智能用于预测性维护.
- 持续监控,并设置了降低风险的警报值.
- 机器学习 (随机森林) 和长短期记忆 (LSTM) 循环神经网络的应用.
主要成果:
- 开发了一种经济可行的传感器放置方法.
- 确定了交通安全决策的门.
- 该研究整合了机器学习和人工认知系统,用于基础设施监控.
结论:
- 人工智能驱动的预测性维护为管理气候变化对铁路桥梁的影响提供了可行的解决方案.
- 拟议的方法平衡了基础设施监测成本与交通安全要求.
- 优化的传感器网络和智能算法提高了铁路基础设施的弹性.
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