基于动态响应的安全监测和通过PSO-LSTM识别混凝土的损害识别
Jianchun Qiu1, Wenqin He1, Changlin Long2
1College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
一个新的PSO-LSTM模型增强了混凝土门大安全监测. 该方法使用动态响应准确检测结构损坏,提高整体水安全性和可靠性.
科学领域:
- 工程 工程师 工程师 工程师
- 结构健康监测 结构健康监测
- 人工智能的人工智能
背景情况:
- 在地震刺激下混凝土梁大的动态反应包含重要的结构健康信息.
- 有效的安全监测对于早期检测损坏和确保大完整性至关重要.
研究的目的:
- 提出一种新的PSO-LSTM模型,用于加强形水结构的安全监测和损坏识别.
- 提高监测非线性和非静止动态响应的准确性.
主要方法:
- 使用长期短期记忆 (LSTM) 神经网络.
- 采用粒子群集优化 (PSO) 来适应调整LSTM超参数.
- 使用结构损坏识别的3σ标准进行了残留分析.
主要成果:
- 与独立的LSTM和SSA-LSTM模型相比,PSO-LSTM模型在监测梁动态响应方面表现出卓越的准确性.
- 在识别结构损坏方面实现了高精度.
- 通过数值模拟和摇摆表测试进行验证.
结论:
- 拟议的PSO-LSTM方法对门大的实时安全监测有效.
- 这种方法显著提高了损害识别的准确性,确保了大的安全性.
- 这种人工智能驱动的方法为结构健康评估提供了可靠的解决方案.
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