期刊
JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING
卷 12, 期 4, 页码 797-815出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s13349-021-00526-4
关键词
Real-time frequency estimation; Time-varying cable tension; Cable-stayed bridge; Wireless sensors; Packet loss; Compressive sensing; Data reconstruction; Sparse component analysis; Blind source separation
资金
- Science and Engineering Research Board of India (SERB)
- Rice University
- SERB-India
Stay-cables in cable-stayed bridges are vital components that require continuous real-time performance monitoring. Wireless sensors provide accurate measurement without wiring cost, but data loss during transmission may interrupt real-time monitoring, which can be addressed through data processing. Monitoring cable health using multiple sensors reduces estimation errors and a proposed framework utilizes compressive sensing algorithm and Blind Source Separation Technique to estimate real-time tension effectively.
Stay-cables in the cable-stayed bridge are the most vital components as they carry the bridge deck's load and transmit the force to the bridge pylons. However, dynamics loads due to vortex-induced vibration, ambient wind excitation, and even vehicular vibration cause fatigue in the stay-cable. Hence continuous real-time performance monitoring of such cables is necessary for maintenance to avoid any kind of damage to the cable. Wireless sensors are contact-based sensor that provide accurate measurement, and it does not involve any wiring cost like conventional wired sensors. Monitoring cable health using such wireless sensors is a good choice provided packet loss (which occurs while transmitting the measured data to the base station) that invariably occurs is addressed by data processing. Such discontinuity in data (due to packet loss) may interrupt the real-time/online cable health monitoring process - depending on the window length of the data loss. In general, online health monitoring using multiple sensors reduces the estimation errors. In this paper, we propose a framework that takes the wireless sensor data as the input, then reconstructs the packet lost samples (if any), and finally, provides a real-time tension estimation as an output. The novel framework, first adopts compressive sensing algorithm to reconstruct the data due to packet loss. Subsequently, we synthesize the reconstructed responses from multiple sensors to estimate the real-time frequency variation using Blind Source Separation (BSS) Technique. As the cable response due to ambient vibration contains a large number of modes, the dominant modal response or the corresponding dominant frequency is estimated from very few measurements using a variant of the BSS technique named Sparse Component Analysis (SCA). Finally, real-time cable tension is estimated from the frequency variation using the taut-string theory. The proposed technique is applied to a real full-scale cable-stayed bridge. The mean tension obtained from the framework is comparable with the cable's actual design tension. The accurate estimation of real-time stay-cable tension by the proposed algorithm shows great potential in the field of structural health monitoring.
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