以物理为导向的GNN变压器模型用于在高速铁路中预测混凝土轨道板的多尺度疲劳寿命
Xing Su1,2, Pin Lou3, Zihan Zha2
1School of Civil Engineering, Central South University, Changsha, 410075, China.
Scientific reports
|January 30, 2026
概括
这项研究引入了一种新的机器学习模型,用于预测高速铁路中的混凝土疲劳寿命. 先进的GNN-变压器方法提高了基础设施维护的准确性和可解释性.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 高速铁路 (HSR) 传统的混凝土疲劳寿命预测方法缺乏准确性和可解释性.
- 现有的模型往往忽略了关键的微观结构特征和负载随机性,限制了它们的有效性.
- 需要先进的预测模型来支持HSR基础设施的智能健康管理.
研究的目的:
- 开发一种新的机器学习范式,整合物理机制,预测HSR轨道板的混凝土疲劳寿命.
- 通过结合微结构拓和负载随机性来克服传统方法的局限性.
- 为HSR基础设施创建一个高度准确,高效和可解释的预测模型.
主要方法:
- 开发了一个多任务学习框架,结合了图形神经网络 (GNN) 和变压器架构.
- GNN将混凝土微结构抽象为拓图,以捕捉空间损伤传播.
- 变压器分析随机负载光谱,以确定影响疲劳损伤的关键时间模式.
主要成果:
- 拟议的GNN-变压器模型表现出色的性能,达到0.939的确定系数.
- 该模型在150个训练时代后实现了较低的预测误差 (MSE:0.224,RMSE:0.332).
- 该模型表现出高效的预测延迟 (0.86s) 和适度的GPU利用率 (48.3%),表明在线部署的巨大潜力.
结论:
- 集成的GNN-变压器框架成功地预测了高精度和物理可解释性的混凝土疲劳寿命.
- 该模型有效地将微观结构拓与宏观负载序列特征相结合,用于可靠的预测.
- 这项研究为高速铁路基础设施的预测性维护和智能健康管理提供了重要的算法支持.
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