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Spectrum Analysis of Seismic Responses of a Building during an Earthquake
KALOOPMOSBEHRASHEDMOSBEH,최석준,허종완 한국복합신소재구조학회 2015 복합신소재구조학회논문집 Vol.6 No.1
This study presents the design and implementation of a structural health monitoring system based on acceleration measurements which used to observe and investigate the structural performance of the administration building in Seoul National University of Education during an earthquake event. The frequency and spectrum are analyzed to assess the building performance during an earthquake shaking which took place on March 31st, 2014. The results indicate that : the vibration of the roof is more clear and dominant during the shaking, and the response of building during earthquake is so small and safe.
허종완,KALOOPMOSBEHRASHEDMOSBEH 대한기계학회 2015 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.29 No.7
Model identification of a system can be used for a variety of purposes, including model updates, damage assessment, active controland original design evaluation. We used single input-single output (SISO) nonlinear regression with least square solution and nonlinearAutoRegresive with eXogenous inputs (NLARX) with wavelet neural networks models to identify the thermal response of a bridge underhard environmental effects and estimated the nonlinearity model parameters. Fu-Sui Bridge strain structural health monitoring measurementswere used as a case study. Nonlinear regression analysis showed that the thermal response is a nonlinear effect with temperaturechanges; and the NLARX with wavelet network solution is capable of accurately predicting thermal response and can help with interpretingmeasurements from continuous bridge health monitoring systems.
H. T. El Shambaky,KALOOPMOSBEHRASHEDMOSBEH 대한토목학회 2017 KSCE JOURNAL OF CIVIL ENGINEERING Vol.21 No.1
Recently Kalman filters have been widely used in vehicle navigation systems and various Global Navigation Satellite System (GNSS) receivers. A conventional Kalman (CK) filter is found as an inferior solution to precisely determine the turning and monitoring points in comparison with the Adaptive Kalman filter, previously. Therefore, this study aims at investigating a novel solution to improve the CK. algorithm based on the effect of the variance ratio on the algorithm accuracy. The simulation process is used to determine and update the characteristic equation of the Kalman algorithm system. In addition, the comparison between the real observations for proposed updated CK (UCK) and Adaptive algorithms are applied and discussed in this study. The results, herein, demonstrate that the variance factor ratio (variance ratio) can aid a CK algorithm to be more economical, precise and steady. Moreover, the real observations prove that the CK filter with variance ratio performs more efficient than the adaptive one.