损坏定位和传感器布局优化用于使用深度学习和声波发射的现役钢筋混凝土柱
Tao Liu1,2, Aiping Yu1,2, Zhengkang Li1,2
1School of Civil Engineering, Guilin University of Technology, Guilin 541004, China.
Materials (Basel, Switzerland)
|September 27, 2025
概括
这项研究通过使用深度学习和声辐射 (AE) 技术来加强钢筋混凝土 (RC) 柱状况监测. 背向传播 (BP) 模型在定位AE源方面表现出色,优化的传感器布局提高了损坏检测的准确性.
科学领域:
- 结构工程 结构工程
- 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:
- 钢筋混凝土 (RC) 柱是结构中的关键承载元件.
- 定期健康评估对于延长结构的使用寿命和性能至关重要.
- 在使用中的RC柱健康监测带来了重大挑战.
研究的目的:
- 在正在使用的RC柱中检测和定位声辐射 (AE) 源.
- 确定最佳的传感器布局,以有效监测健康状况.
- 评估不同的深度学习模型在AE源本地化方面的性能.
主要方法:
- 实施深度学习算法与声辐射 (AE) 技术相结合.
- 使用k-means集群和投票选择进行数据清理,以提高数据质量.
- 对AE源定位的反向传播 (BP),辐射基函数 (RBF) 和支向量回归 (SVR) 模型进行比较.
- 评估不同的传感器布局方案 (线性,混合线性-体积).
主要成果:
- 使用k-means集群和投票选择数据清理方法显著提高了数据质量.
- 与RBF和SVR模型相比,BP模型展示了优越的AE源定位性能,显示了降低的平均绝对误差 (MAE) 和根平均平方误差 (RMSE),并增加了R平方 (R2).
- 线性传感器安排对浅层混凝土矩阵损伤有效,而混合线性体积计安排对深层矩阵损伤更有效.
结论:
- 逆向传播 (BP) 深度学习模型对于在现有 RC 列中的 AE 源本地化非常有效.
- 优化的传感器布局策略,特别是混合线性体积计方案,提高了在浅层和深层混凝土矩阵中损坏检测的准确性.
- 这种综合方法为正在使用的RC柱的结构健康监测提供了显著的应用价值.
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