在网络桥结构特异性背景下基于ML的移动权衡系统的性能
Dawid Piotrowski1, Marcin Jasiński1, Artur Nowoświat1
1Faculty of Civil Engineering, Silesian University of Technology, ul. Akademicka 5, 44-100 Gliwice, Poland.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
机器学习使用应变数据准确地估计了车辆的重量和在桥上的位置. 这种方法利用更新的有限元模型和数据增强来进行有效的结构健康监测.
科学领域:
- 土木工程 土木工程是指土木工程.
- 结构健康监测 结构健康监测
- 机器学习应用 机器学习应用
背景情况:
- 机器学习 (ML) 在工业和科学中越来越多地用于模式识别.
- 桥上的结构健康监测 (SHM) 系统生成用于分析的数据.
- 估计桥上车辆负载对于安全和维护至关重要.
研究的目的:
- 通过应变传感评估ML算法来估计车辆的重量和桥上的位置.
- 开发和测试一个结合有限元模型 (FE) 和数据驱动方法的新框架.
- 评估监督回归模型在桥梁运动称量 (BWIM) 系统中的性能.
主要方法:
- 建立并更新基于负载测试的有限元 (FE) 模型.
- 使用数据增强来增强ML模型的训练数据集.
- 使用纤维支格 (FBG) 传感器进行应变测量.
- 应用监督回归模型,包括基于物理学的方法.
- 利用网络桥的结构特异性来优化功能选择.
主要成果:
- 证明了使用应变测量建立桥梁运动称重 (BWIM) 系统的可行性.
- 在减少传感器数量的情况下,在估计车辆负载时获得了令人满意的准确性.
- 验证了更新的FE模型在生成培训数据方面的有效性.
- 展示了各种ML回归模型在负载估计中的性能.
结论:
- 来自SHM系统的应变测量可以有效地支持BWIM系统.
- 拟议的综合模型和数据驱动框架增强了桥梁监控的ML预测能力.
- 利用桥梁的结构特异性,可以更高效,更准确地估计负载.
- ML为推进桥梁工程和结构健康评估提供了一个强大的工具.
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