在增量启动施工过程中,基于LSTM的钢结构结构形状错误的预测方法
Zhe Hu1, Hao Chen1, Chunguang Dong2
1School of Civil Engineering and Transportation, South China University of Technology, Guangzhou City, Guangdong Province, China.
PloS one
|July 10, 2025
概括
本研究介绍了一种长短期记忆 (LSTM) 模型,用于实时预测钢结构结构增量启动中的形状错误. 该方法准确地预测了几何变化,提高了桥梁工程精度和效率.
科学领域:
- 桥梁工程 桥梁工程
- 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:
- 由于结构移动期间的动态几何变化,钢结构结构的增量启动中精确的形状控制具有挑战性.
- 传统的测量方法在这些动态过程的实时错误确定和控制标准方面扎.
研究的目的:
- 开发一种基于长短期记忆 (LSTM) 的新方法,用于实时预测和适应性控制钢结构结构逐步启动期间的形状错误.
- 通过解决传统方法的局限性,提高桥梁工程中形状控制的准确性和效率.
主要方法:
- 使用实际推进测量和虚拟组装技术建立了一个错误矩阵,以生成错误线条.
- 采用滑窗方法进行递归预测和更新,将数据输入训练用于形状错误预测的LSTM模型.
- 在试验组中,LSTM模型实现了0.03的根平均平方误差 (RMSE).
主要成果:
- 实地实验证实LSTM预测与测量值非常相匹配,保持了在3mm以内的短期形状误差预测准确度.
- 预测准确度在较长的时间步骤下降,但随着使用新测量数据的模型更新,预测准确度迅速提高.
- 拟议的方法在类似的桥梁工程项目中显示出高精度和多功能性.
结论:
- 基于LSTM的方法提供了一个高度准确和多功能解决方案,用于预测和控制钢结构结构增量启动中的形状错误.
- 这种方法显著减少了手动错误处理时间,并最大限度地减少了桥梁施工中的计算不准确性.
- 该研究强调了机器学习在复杂的结构工程应用中实现实时自适应控制的潜力.
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