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    Design and Simulation of a Green Bi-Variable Mono-Parametric SHM Node and Early Seismic Warning Algorithm for Wave Identification and Scattering

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    Date
    2018
    Author
    Touati F.
    Tariq H.
    Crescini D.
    Manouer A.B.
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    Abstract
    Early seismic warning systems are key for safe future scalable infrastructures. In this work, a dual variable i.e. vibration and line of sight (LOS) based structure health monitoring (SHM) node is designed to sense tilt angle for early seismic warning and wave scattering detection. The SHM node, consisting of high-precision five bi-axis tiltmeters and five Blue-Violet laser diodes transmitter/receiver/reflector(LDTRR) assembly, has been designed and simulated in Proteus 7ISIS, MATLAB 7 and drafted in AutoCAD. In AutoCAD, a four LDTRR assembly is oriented at the bottom of building and its four co-planer reflectors have been orthogonally placed at effective radii with respect to the characteristic wavelengths of P, S, and Rayleigh whilst Love seismic waves, and one reflector is placed at the bottom of building. PV umbrella with a Li-ion battery has been used for green ergonomic shape. The time plots from real tiltmeter sensor nodes and data acquired from the proposed SHM node show similar behavior and results. The derived parameters of wavelength S, i.e. seismic parameter F,varied linearly from safe to hazardous seismic conditions. The variation from safe seismic to hazardous seismic transition of randomly simulated environment, also varied network traffic in GPS module as per defined threshold of sensor variables in Proteus ISIS Electronics Design Automation (EDA) engine. As per early warning evaluation functions (EWEF), the proposed design for early seismic warning algorithm (ESWA) can be a cost-effective analytics resource for any scalable SHM solution for observation range within 5km+ radius at low cost and 20km at moderate/high cost.
    DOI/handle
    http://dx.doi.org/10.1109/IWCMC.2018.8450277
    http://hdl.handle.net/10576/13230
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    • Electrical Engineering [‎2840‎ items ]

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