钢筋混凝土结构的安全评估使用多源融合不确定性云推断和实验研究
Zhao Liu1, Huiyong Guo1, Bo Zhang1
1School of Civil Engineering, Chongqing University, Chongqing 400045, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
本研究引入了一种新的结构健康监测 (SHM) 方法,通过分析多来源数据和考虑不确定性来量化结构安全. 该方法有效评估组件和结构的健康状况,优于现有的模型.
科学领域:
- 土木工程 土木工程是指土木工程.
- 结构健康监测 (SHM) 是一种结构健康监测.
- 在工程领域的人工智能.
背景情况:
- 结构损坏检测和安全评估在结构健康监测 (SHM) 中至关重要.
- 现有的方法面临着多源监测数据的挑战,以及来自缺陷和错误的不确定性.
研究的目的:
- 通过量化结构健康状况来开发评估结构安全的综合方法.
- 使用多源数据来描述条件索引和结构性表现之间的关系.
主要方法:
- 开发了一种多源融合不确定性云推理 (MFUCI) 方法.
- 利用云理论从条件指数云滴中建立定性规则基础.
- 采用多源融合发电机来获得共同的确定度和定量评估.
主要成果:
- MFUCI有效量化结构部件 (RC T-beam) 和结构 (RC框架) 的健康状况,基于刚性降解.
- 该方法通过噪声免疫和跨领域推断证明了可靠性和稳定性.
- 在不确定性估计方面,MFUCI的表现优于贝叶斯神经网络 (BNN),在点估计方面优于长短期记忆 (LSTM).
结论:
- 拟议的MFUCI方法为定量结构健康评估提供了一种可靠和可靠的方法.
- 纳入不确定性信息可以提高结构安全评估的准确性和性能.
- 对于SHM应用,MFUCI比现有模型提供了显著的进步.
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