根据多物理现场信息指导,研究大跨线缆桥梁的性能检查
Li Jiaqing1, Xu Jintao2, He Hongmou3
1Guangdong Shengxiang Traffic Engineering Testing Co., Ltd., Guangzhou, 511400, China.
Scientific reports
|December 20, 2025
概括
这项研究引入了一个新的深度学习模型,IMPFE-HGNN,以使用多传感器数据准确更新桥梁有限元模型. 该模型通过捕捉复杂的相互作用来提高大跨度桥梁的安全评估和寿命预测.
科学领域:
- 结构工程 结构工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 桥梁机械性能的时间退化需要精确的有限元 (FE) 模型更新,以确保安全.
- 目前的深度学习方法由于单个输入和同质数据假设,与多物理交互作斗争.
研究的目的:
- 开发一种新的深度学习架构,即集成多物理场编码异质图形神经网络 (IMPFE-HGNN).
- 通过整合异质的多物理传感器数据,使大跨度桥梁能够精确更新FE模型参数.
主要方法:
- 拟议的IMPFE-HGNN架构明确地使用元路径子图和关系意识编码模拟异质传感器拓.
- 该模型从应变,偏移,温度,电缆力和加速传感器数据中提取高阶的多物理语义.
- 在长跨式缆索桥的案例研究中得到验证.
主要成果:
- 实现了显著参数的识别:Poisson比率得到了43.33%的修正,弹性模量得到了10.20%.
- 提高FE模型预测准确度:中位应变预测误差减少了61.4%,中位偏移预测准确度提高了72.8%.
- 废弃和灵敏度分析证实了拟议机制的有效性,并确定了最佳超参数.
结论:
- IMPFE-HGNN提供了一个可物理解释的GNN范式,用于结构性健康监测中的多源数据融合.
- 该框架提供了一种强大而精确的方法,用于对大跨度吊桥的结构性能的评估.
关键词:
电缆桥架桥梁 电缆桥架桥梁 电缆桥梁在FEM纠正FEM纠正IMPFE-HGNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN结构性健康监测 结构性健康监测相关概念视频
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