Wireless Sensor Network-Based Structural Health Monitoring of Bridges Using Advanced Signal Processing Techniques

被引:3
|
作者
Ali, Syed Humair [1 ]
Khan, Tariq Mairaj Rasool [1 ]
Abdullah, Murad [1 ]
Zaid, Muhammad [1 ]
机构
[1] Natl Univ Sci & Technol NUST, Dept Elect & Power Engn, PNEC Karachi Campus,H-12, Islamabad, Pakistan
关键词
structural health monitoring; wireless sensor network; fast Fourier transform; Hilbert Huang transform; empirical mode decomposition; intrinsic mode function; EMPIRICAL MODE DECOMPOSITION; TECHNOLOGY; PREDICTION; SYSTEMS;
D O I
10.1520/JTE20180849
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
Continuous growth in the networks of roads and bridges pose a continuously rising challenge to maintenance agencies for ensuring accident-free operations of transportation network. This challenge exacerbates in developing economies. Specifically, in port/harbor areas, the challenge increases many folds because of the corrosive environment. Assessment of aging civil infrastructure integrity is imperative to guarantee safe operations. Wireless sensor networks (WSNs) offer reliable and cost-effective automated applications for structural health monitoring (SHM) of civil infrastructure. This article presents a vibration analysis-based method for SHM of civil infrastructures using WSNs. In the proposed research work, advanced signal processing techniques are applied on actual vibration data acquired using low-power WSNs to classify healthy and degraded civil structures. The proposed scheme has successfully been implemented on a 20-year-old concrete bridge situated in the harbor area of Karachi, Pakistan. Micro electro mechanical system-based accelerometers connected to low-power-consuming wireless nodes are installed on the bridge under study. The acquired vibration data are uploaded on the cloud. Subsequent application of time-frequency energy analysis of the recorded vibration data enabled the discrimination between healthy and degraded structures. The proposed scheme can be used for civil infrastructure monitoring at remote sites using low-power WSNs with the least intervention.
引用
收藏
页码:1266 / 1283
页数:18
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