@article{ref1,
title="Stochastic characterization of wind field characteristics of an arch bridge instrumented with structural health monitoring system",
journal="Structural safety",
year="2018",
author="Ye, X. W. and Xi, P. S. and Su, Y. H. and Chen, B. and Han, J. P.",
volume="71",
number="",
pages="47-56",
abstract="This paper aims to conduct a stochastic characterization of wind field characteristics nearby an arch bridge based on long-term monitoring data from an instrumented structural health monitoring (SHM) system. The fluctuating wind characteristics are first presented by analyzing the real-time wind monitoring data. A genetic algorithm (GA)-based finite mixture modeling approach is proposed to formulate the joint distribution of the wind speed and direction. For the probability density function (PDF) of the wind speed, a two-parameter Weibull distribution is applied, and a von Mises distribution is selected to present the PDF of the wind direction. The parameters of finite mixture models are estimated by the GA-based parameter estimation method. The effectiveness of the proposed direct probabilistic modeling approach is validated by use of one-year of wind monitoring data, and compared with the traditional angular-linear (AL) distribution-based indirect modeling approach in terms of the Akaike's information criterion (AIC), Bayesian information criterion (BIC) and R2 value. <br><br>RESULTS indicate that the proposed GA-based finite mixture modeling approach fits the measured data better than the AL distribution-based indirect modeling approach. In addition, the joint distribution of the wind speed and direction will facilitate the wind-resistant design and wind-induced fatigue assessment of long-span bridges.<p /> <p>Language: en</p>",
language="en",
issn="0167-4730",
doi="10.1016/j.strusafe.2017.11.003",
url="http://dx.doi.org/10.1016/j.strusafe.2017.11.003"
}