一种使用概率深度学习和测量数据进行桥梁偏移预测的新方法
Xinhui Xiao1, Zepeng Wang1, Haiping Zhang1
1School of Civil Engineering, Hunan University of Technology, Zhuzhou 412007, China.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
这项研究引入了CNN-LSTM-GD模型,用于预测悬架桥梁梁梁在交通和温度负载下的偏移,提高了准确性,并使异常偏移的早期预警系统成为可能.
科学领域:
- 结构工程 结构工程
- 在土木工程中的人工智能.
- 桥梁健康监测 桥梁健康监测
背景情况:
- 吊桥是灵活的结构,需要精确的曲折控制,以确保运行安全.
- 由于随机的交通负载和环境温度变化,预测垂直梁的偏移是复杂的.
研究的目的:
- 开发一种综合方法,用于预测悬架桥梁梁的垂直偏斜间隔.
- 提高偏移预测的准确性,并建立识别异常偏移和警告值的方法.
主要方法:
- 使用卷积神经网络 (CNN) 和长短期记忆 (LSTM) 网络进行时间序列数据分析.
- 集成了一个概率密度估计层与高斯分布 (GD) 间隔预测.
- 使用桥梁健康监测数据训练模型,包括环境温度,车辆负载和偏移.
主要成果:
- 与LSTM和CNN-LSTM模型相比,CNN-LSTM-GD模型在短时间和长时间尺度上显著改善了根平均平方误差 (RMSE) 和确定系数 (R2).
- 与基线模型相比,实现了高达54.40%的RMSE改善和12.37%的R2增加.
- 在识别异常偏移和设置警告值方面已证明有效.
结论:
- 拟议的CNN-LSTM-GD模型为悬架桥曲折预测提供了强大而准确的方法.
- 该方法对于开发有效的桥梁偏移预警系统至关重要.
- 准确的曲折预测和异常曲折的识别可以提高桥梁的运行安全和维护.
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