• Title/Summary/Keyword: multimetric data fusion

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Quasi real-time and continuous non-stationary strain estimation in bottom-fixed offshore structures by multimetric data fusion

  • Palanisamy, Rajendra P.;Jung, Byung-Jin;Sim, Sung-Han;Yi, Jin-Hak
    • Smart Structures and Systems
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    • v.23 no.1
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    • pp.61-69
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    • 2019
  • Offshore structures are generally exposed to harsh environments such as strong tidal currents and wind loadings. Monitoring the structural soundness and integrity of offshore structures is crucial to prevent catastrophic collapses and to prolong their lifetime; however, it is intrinsically challenging because of the difficulties in accessing the critical structural members that are located under water for installing and repairing sensors and data acquisition systems. Virtual sensing technologies have the potential to alleviate such difficulties by estimating the unmeasured structural responses at the desired locations using other measured responses. Despite the usefulness of virtual sensing, its performance and applicability to the structural health monitoring of offshore structures have not been fully studied to date. This study investigates the use of virtual sensing of offshore structures. A Kalman filter based virtual sensing algorithm is developed to estimate responses at the location of interest. Further, this algorithm performs a multi-sensor data fusion to improve the estimation accuracy under non-stationary tidal loading. Numerical analysis and laboratory experiments are conducted to verify the performance of the virtual sensing strategy using a bottom-fixed offshore structural model. Numerical and experimental results show that the unmeasured responses can be reasonably recovered from the measured responses.

Development of Low-Power IoT Sensor and Cloud-Based Data Fusion Displacement Estimation Method for Ambient Bridge Monitoring (상시 교량 모니터링을 위한 저전력 IoT 센서 및 클라우드 기반 데이터 융합 변위 측정 기법 개발)

  • Park, Jun-Young;Shin, Jun-Sik;Won, Jong-Bin;Park, Jong-Woong;Park, Min-Yong
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.34 no.5
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    • pp.301-308
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    • 2021
  • It is important to develop a digital SOC (Social Overhead Capital) maintenance system for preemptive maintenance in response to the rapid aging of social infrastructures. Abnormal signals induced from structures can be detected quickly and optimal decisions can be made promptly using IoT sensors deployed on the structures. In this study, a digital SOC monitoring system incorporating a multimetric IoT sensor was developed for long-term monitoring, for use in cloud-computing server for automated and powerful data analysis, and for establishing databases to perform : (1) multimetric sensing, (2) long-term operation, and (3) LTE-based direct communication. The developed sensor had three axes of acceleration, and five axes of strain sensing channels for multimetric sensing, and had an event-driven power management system that activated the sensors only when vibration exceeded a predetermined limit, or the timer was triggered. The power management system could reduce power consumption, and an additional solar panel charging could enable long-term operation. Data from the sensors were transmitted to the server in real-time via low-power LTE-CAT M1 communication, which does not require an additional gateway device. Furthermore, the cloud server was developed to receive multi-variable data from the sensor, and perform a displacement fusion algorithm to obtain reference-free structural displacement for ambient structural assessment. The proposed digital SOC system was experimentally validated on a steel railroad and concrete girder bridge.

Issues in structural health monitoring for fixed-type offshore structures under harsh tidal environments

  • Jung, Byung-Jin;Park, Jong-Woong;Sim, Sung-Han;Yi, Jin-Hak
    • Smart Structures and Systems
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    • v.15 no.2
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    • pp.335-353
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    • 2015
  • Previous long-term measurements of the Uldolmok tidal current power plant showed that the structure's natural frequencies fluctuate with a constant cycle-i.e., twice a day with changes in tidal height and tidal current velocity. This study aims to improve structural health monitoring (SHM) techniques for offshore structures under a harsh tidal environment like the Uldolmok Strait. In this study, lab-scale experiments on a simplified offshore structure as a lab-scale test structure were conducted in a circulating water channel to thoroughly investigate the causes of fluctuation of the natural frequencies and to validate the displacement estimation method using multimetric data fusion. To this end, the numerical study was additionally carried out on the simplified offshore structure with damage scenarios, and the corresponding change in the natural frequency was analyzed to support the experimental results. In conclusion, (1) the damage that occurred at the foundation resulted in a more significant change in natural frequencies compared with the effect of added mass; moreover, the structural system became nonlinear when the damage was severe; (2) the proposed damage index was able to indicate an approximate level of damage and the nonlinearity of the lab-scale test structure; (3) displacement estimation using data fusion was valid compared with the reference displacement using the vision-based method.