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使用基于机器学习的替代模型对桥梁梁的应变数据进行肌应力估计
Sadia Umer Khayam1, Ammar Ajmal2, Junyoung Park1
1Department of Civil and Environmental Engineering, Urban Design and Studies, Chung-Ang University, Seoul 06974, Republic of Korea.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究引入了一种机器学习方法,以估计使用应变数据在梁中预应力肌应力. 这种方法可以实时监测和调整张力,从而提高结构完整性.
科学领域:
- 土木工程 土木工程是指土木工程.
- 结构工程 结构工程
- 材料科学 材料科学 材料科学
背景情况:
- 预应力混凝土梁可实现长跨度和减少裂纹,但需要精确的紧张力控制.
- 精确的设计和肌力量的监测对于防止过度爬行和确保结构完整性至关重要.
- 估计预应力肌应力是具有挑战性的,因为有限的可访问性.
研究的目的:
- 开发和验证一种基于应变的机器学习方法,用于实时估计预应力梁中应用的肌应力.
- 评估机器学习模型在预测肌力方面的准确性和可行性.
主要方法:
- 使用有限元法 (FEM) 分析在45米高的梁上生成了一个数据集,肌应力有所变化.
- 机器学习网络模型被训练并使用应变数据进行测试,以预测肌应力.
- 对于应力预测,选择了具有最低根平均平方误差 (RMSE) 的模型.
主要成果:
- 开发的机器学习模型在肌应力方面实现了不到10%的预测误差.
- 选择的模型准确地估计了实时的肌应力,使拉伸力调整成为可能.
- 该研究确定了最佳的梁位置和应变传感器配置.
结论:
- 机器学习,利用应变数据,是一种可行的方法,用于在预应力梁中即时估计肌力.
- 这种方法提供了一个实用的解决方案,用于实时监测和控制预应力.
- 这些发现有助于改善预压混凝土结构的设计和维护策略.
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