基于深度学习的混凝土桥塔裂的长期温度变化特征研究
Zhaoxu Lv1, Youliang Ding2,3, Yan Zhang2,3
1Jiangsu Xiandai Road & Bridge Co., Ltd., Nanjing 210018, China.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
这项研究使用长短期记忆 (LSTM) 网络从温度数据中预测桥梁裂深度. 该模型准确预测裂宽度的变化,有助于结构健康监测.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 温度波动对桥梁中的混凝土裂产生重大影响.
- 由于热梯度,在中央塔附近观察到花安桥上的混凝土裂.
- 现有的结构健康监测方法需要加强裂分析.
研究的目的:
- 开发一个深度学习模型来预测桥梁裂深度.
- 为了利用长短期记忆 (LSTM) 神经网络进行热变量分析.
- 为了提高桥梁结构中裂宽度预测的准确性.
主要方法:
- 开发了一个基于LSTM神经网络的深度学习回归模型.
- 用多个温度输入数据集来捕捉空间变化.
- 该模型经过训练,可以根据热数据预测裂深度.
主要成果:
- 该LSTM网络在预测裂深度方面表现出有效性.
- 通过考虑空间温度变化,模型在预测裂纹宽度方面的准确性得到了提高.
- 确定了裂形成的精确温度值.
结论:
- 开发的深度学习模型提供了一种有效的方法来监测桥梁裂状态.
- 这种方法有助于早期检测裂宽度的异常.
- 该研究提供了用于桥梁结构健康监测的技术解决方案.
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