基于多传感器系统的越南桥梁结构健康监测中随机振动信号的PSD特性

IF 1.9 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS International Journal of Distributed Sensor Networks Pub Date : 2022-09-01 DOI:10.1177/15501329221125110
Thanh Q. Nguyen, Tuan-Anh Nguyen, Thuy T. Nguyen
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引用次数: 4

摘要

本研究提出了两个参数,包括谐波出现频率(AFH)和功率谱密度(PSD)的形状变化,这两个参数被用来评估桥梁跨距刚度的下降。psd是基于加速度计多传感器随机交通荷载模型的真实振动信号,反映了结构的力学行为随时间的变化。此外,AFHs还评估了结构的可加工性。考虑到这些参数,研究了实际梁结构的实际振动,目的是利用结构健康监测来评估西贡大桥跨径的承载能力降低。结果表明,在一定时间内,afh和高频区域与桥梁的刚度下降有关。在未来,该研究可用于监测各种类型的结构材料和许多不同的桥梁跨度的结构健康。
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PSD characteristics for the random vibration signals used in bridge structural health monitoring in Vietnam based on a multi-sensor system
This study proposes two parameters, including the appearance frequency of harmonics (AFH) and the change in shape of the power spectral density (PSD), which are examined to assess the decline in stiffness of a bridge span. PSDs are obtained from the real vibration signals of the randomized traffic load model based on accelerometer multi-sensors that indicate the change in mechanical behavior of the structure over time. In addition, AFHs evaluate the workability of the structure. With these parameters in mind, actual vibrations in real beam structures are studied with the aim of using structural health monitoring to assess the bearing capacity reduction on Saigon Bridge’s spans. The results show that AFHs and the high-frequency regions relate to the decreased stiffness of the bridge’s spans over a given period of time. In the future, this research can be used to monitor structural health for various types of structure materials and many different bridge spans.
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来源期刊
CiteScore
6.50
自引率
4.30%
发文量
94
审稿时长
3.6 months
期刊介绍: International Journal of Distributed Sensor Networks (IJDSN) is a JCR ranked, peer-reviewed, open access journal that focuses on applied research and applications of sensor networks. The goal of this journal is to provide a forum for the publication of important research contributions in developing high performance computing solutions to problems arising from the complexities of these sensor network systems. Articles highlight advances in uses of sensor network systems for solving computational tasks in manufacturing, engineering and environmental systems.
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