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    Preliminary evaluation of precise inclination sensor and GPS for monitoring full-scale dynamic response of a tall reinforced concrete building
    (2010) Yigit C.O.; Yigit C.O.; Li X.; Ge L.; Inal C.; Yetkin M.
    It is necessary to use different sensors in an integrated manner-GPS, accelerometer, inclination sensor and so on-in order to monitor and identify static, quasi-static and resonant response of tall buildings subjected to wind loading. There are some differences among these sensors with respect to data sampling rate, data quality, and their measurement accuracy. Therefore, using different sensors together for a monitoring project is important because of the complementary nature of each sensor. In this study, the behaviour of a tall reinforced concrete building (30 stories high) under wind load has been monitored using GPS and inclination sensors. This paper assesses the dynamic measurement quality and reliability of inclinometers for building monitoring applications, and discusses the strengths and weaknesses of GPS vis-a-vis the use of inclination sensors for monitoring the dynamic response of tall buildings under wind load. Data collected by these sensors have been analysed in the time and frequency domains. It was found that GPS observations were distorted by multipath caused by a reflecting surface on top of the building. From the analyses in the frequency domain, the 1st mode natural frequencies of the building determined from both sensors agree very well with each other. The discrepancy of this measured 1st mode natural frequency compared to that derived from FEM (Finite Element Model) prediction is 7%. © 2010, de Gruyter. All rights reserved.
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    Preliminary results of the sign-constrained robust least squares method in a leveling network
    (2012) Yetkin M.; Berber M.
    The method of least squares yields the most likely solution for a set of redundant observation data provided that both functional and stochastic model are correct and only random errors affect the observations. However, the method of least squares is very sensitive to model errors and gross errors. Therefore, spatial data analysis must be performed using rigorous robust statistical procedures to reduce bad effects of outlying observations on parameter estimation. A newly introduced robust estimation method, sign constrained robust least squares, may be applied to geodetic networks. Nevertheless, the implementation of the method may require a good computational technique. In this study, we propose the use of the shuffled frog leaping algorithm which is an evolutionary optimization algorithm to solve sign-constrained robust least squares estimation problem in a geodetic network. The constraints in the optimization problem can be dealt with penalty function approach. The practical results are given in a leveling network.