使用改进的盲源分离 (BSS) 技术对温度诱导的分离结构应变的研究.
Hao'an Gu1, Xin Zhang2, Dragoslav Sumarac3,4
1Department of Engineering Mechanics, Hohai University, Nanjing 210098, China.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
这项研究引入了一种新方法,可以将温度影响与桥梁应变数据分开,从而改善结构健康监测 (SHM). 温度分离二级盲识别 (TS-SOBI) 方法提高了评估桥梁状况的准确性.
科学领域:
- 结构工程 结构工程
- 信号处理 信号处理
- 民用基础设施 民用基础设施
背景情况:
- 桥梁的结构健康监测 (SHM) 对安全至关重要.
- 温度和车辆负载污染了菌株数据,使健康评估复杂化.
- 现有的分离温度效应的方法缺乏准确性和稳定性.
研究的目的:
- 开发一种新且准确的方法,从桥梁监测数据中分离温度诱导的应变.
- 为了解决当前统计和信号处理技术的局限性.
- 提高桥梁结构健康评估的可靠性.
主要方法:
- 提出了温度分离二次盲识别 (TS-SOBI) 方法,这是盲源分离 (BSS) 的一个进步.
- 在温度和车辆负载的组合下使用有限元 (FE) 桥梁模型验证了TS-SOBI方法.
- 将TS-SOBI应用于来自长跨桥的SHM系统的真实世界菌株数据.
主要成果:
- 在数值模拟中,TS-SOBI准确地分离了温度诱导的应变组件.
- 该方法在使用真实桥梁数据的实际工程场景中表现出有效性.
- 对剩余应变元件的分离后分析为桥梁负载条件提供了更清晰的见解.
结论:
- TS-SOBI提供了一种强大而准确的解决方案,用于减轻桥梁SHM的温度影响.
- 开发的技术提高了大跨度桥梁结构健康评估的精度.
- 这项研究为提高桥梁监控系统的可靠性提供了新的视角.
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